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Science
Vo
l.
41
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3
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Ma
r
ch
20
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6
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I
SS
N:
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cs
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41
.
i
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.
pp
966
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966
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:
h
ttp
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//ij
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cs
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co
m
Enha
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pre
dict
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kid
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disea
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nset
throug
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ma
chine learning
techniqu
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Sa
m
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o
hn
P
a
rr
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M
a
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Cristine
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ims
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o
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k
-
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a
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ig
h
b
o
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NN)
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a
n
d
a
sta
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k
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g
m
o
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l.
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e
d
a
tas
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t,
s
o
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rc
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d
fro
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th
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a
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sit
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ifi
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ica
ti
n
g
th
e
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e
ffe
c
ti
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g
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h
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m
n
o
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-
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c
a
s
e
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e
m
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siz
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l
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f
m
a
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lea
rn
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n
g
i
n
CKD
d
iag
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o
sis.
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d
e
tec
ti
o
n
c
a
n
lea
d
t
o
imp
ro
v
e
d
c
li
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tco
m
e
s
b
y
e
n
a
b
li
n
g
ti
m
e
ly
in
terv
e
n
t
io
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s
a
n
d
p
e
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li
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trea
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n
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stra
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s.
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u
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larg
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m
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to
imp
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v
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p
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e
a
c
c
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r
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z
a
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c
o
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p
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g
m
a
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rn
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m
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e
tec
ti
o
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a
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m
a
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m
e
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t.
K
ey
w
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s
:
C
h
r
o
n
ic
k
id
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d
is
ea
s
e
k
-
n
ea
r
est n
eig
h
b
o
r
s
Ma
ch
in
e
lear
n
in
g
Stack
in
g
m
o
d
el
Su
p
p
o
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t
v
ec
to
r
m
ac
h
in
es
XGBo
o
s
t
T
h
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s
a
n
o
p
e
n
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c
c
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ss
a
rticle
u
n
d
e
r th
e
CC B
Y
-
SA
li
c
e
n
se
.
C
o
r
r
e
s
p
o
nd
ing
A
uth
o
r
:
Sam
u
el
J
o
h
n
Par
r
eñ
o
Ma
th
em
atics Div
is
io
n
,
Un
iv
er
s
ity
o
f
Min
d
an
a
o
,
Dig
o
s
C
o
lleg
e
Dig
o
s
C
ity
,
Ph
ilip
p
in
es
E
m
ail:
s
am
u
eljo
h
n
_
p
a
r
r
en
o
@
u
m
in
d
an
a
o
.
ed
u
.
p
h
1.
I
NT
RO
D
UCT
I
O
N
C
h
r
o
n
ic
k
id
n
ey
d
is
ea
s
e
(
C
K
D)
is
in
cr
ea
s
in
g
ly
r
ec
o
g
n
ize
d
as
a
g
lo
b
al
p
u
b
lic
h
ea
lth
co
n
ce
r
n
,
af
f
ec
tin
g
m
illi
o
n
s
o
f
p
eo
p
le
wo
r
ld
wid
e.
I
t
s
er
v
es
as
a
cr
itica
l
d
eter
m
in
an
t
o
f
p
o
o
r
h
ea
lth
o
u
tco
m
es,
in
clu
d
in
g
p
r
em
atu
r
e
m
o
r
tality
.
C
KD
o
f
t
en
p
r
o
g
r
ess
es
s
ilen
tly
an
d
in
s
i
d
io
u
s
ly
,
m
ak
in
g
th
e
id
e
n
tific
atio
n
an
d
tr
ea
tm
en
t
o
f
its
ea
r
ly
s
tag
es
cr
u
cial
f
o
r
im
p
r
o
v
in
g
p
atien
t
o
u
tco
m
es.
I
ts
co
m
p
licatio
n
s
ca
n
b
e
s
ev
er
e,
in
cl
u
d
in
g
h
y
p
er
ten
s
io
n
,
an
em
ia,
b
o
n
e
d
is
ea
s
e,
an
d
a
s
ig
n
if
ican
tly
in
c
r
ea
s
ed
r
is
k
o
f
ca
r
d
io
v
ascu
lar
d
is
ea
s
es
an
d
en
d
-
s
tag
e
r
en
al
d
is
e
ase,
n
ec
ess
ita
tin
g
d
ialy
s
is
o
r
k
id
n
ey
tr
a
n
s
p
lan
tatio
n
[
1]
−
[
3]
.
T
h
e
p
r
ev
alen
ce
o
f
C
KD
is
n
o
tab
ly
h
ig
h
er
in
r
eg
io
n
s
with
lim
ited
ac
ce
s
s
to
p
r
im
ar
y
h
ea
lth
ca
r
e
an
d
wh
er
e
ed
u
ca
ti
o
n
al
an
d
ec
o
n
o
m
ic
d
is
ad
v
an
tag
es
p
r
e
v
ail.
T
h
is
i
s
p
ar
ticu
lar
ly
ev
i
d
en
t
in
r
u
r
a
l
ar
e
as
o
f
d
e
v
elo
p
in
g
co
u
n
tr
ies
wh
er
e
m
ed
ical
r
eso
u
r
ce
s
ar
e
s
ca
r
ce
,
an
d
awa
r
en
ess
ab
o
u
t
th
e
d
is
ea
s
e
i
s
lac
k
in
g
[
4]
−
[
6]
.
T
h
e
co
m
p
lex
ity
o
f
C
KD,
co
m
b
in
ed
with
its
asy
m
p
to
m
atic
n
atu
r
e
in
ea
r
ly
s
tag
es,
p
o
s
es
s
ig
n
if
ican
t
ch
allen
g
es
f
o
r
h
ea
lth
c
ar
e
s
y
s
tem
s
,
o
f
ten
r
esu
ltin
g
in
d
elay
e
d
d
iag
n
o
s
es a
n
d
s
u
b
o
p
tim
al
m
an
a
g
em
en
t.
Evaluation Warning : The document was created with Spire.PDF for Python.
I
n
d
o
n
esian
J
E
lec
E
n
g
&
C
o
m
p
Sci
I
SS
N:
2
5
0
2
-
4
7
52
E
n
h
a
n
ce
d
p
r
ed
ictio
n
o
f c
h
r
o
n
i
c
kid
n
ey
d
is
ea
s
e
o
n
s
et
th
r
o
u
g
h
ma
ch
in
e
lea
r
n
in
g
…
(
S
a
mu
el
Jo
h
n
P
a
r
r
eñ
o
)
967
Mo
d
er
n
h
ea
lth
ca
r
e
h
as
b
eg
u
n
to
le
v
er
ag
e
ad
v
an
ce
m
en
ts
in
d
ata
an
aly
tics
an
d
m
ac
h
in
e
lear
n
in
g
to
ad
d
r
ess
th
e
ch
allen
g
es
ass
o
ci
ated
with
ea
r
ly
d
etec
tio
n
an
d
o
n
g
o
in
g
m
an
ag
em
e
n
t
o
f
ch
r
o
n
ic
d
is
ea
s
es
l
ik
e
C
KD.
Ma
ch
in
e
lear
n
in
g
o
f
f
er
s
p
o
wer
f
u
l
to
o
ls
f
o
r
s
if
tin
g
th
r
o
u
g
h
lar
g
e
v
o
lu
m
es
o
f
d
ata
t
o
d
etec
t
p
atter
n
s
th
at
m
ay
in
d
icate
ea
r
l
y
s
tag
es
o
f
k
id
n
ey
d
is
ea
s
e
[
7]
,
[
8]
.
T
h
ese
p
atter
n
s
,
o
f
ten
im
p
er
ce
p
tib
le
to
h
u
m
a
n
an
aly
s
ts
,
ca
n
in
clu
d
e
s
u
b
tle
c
h
an
g
es
in
k
id
n
ey
f
u
n
c
tio
n
o
v
er
tim
e
o
r
co
r
r
elatio
n
s
b
etwe
en
v
a
r
io
u
s
r
is
k
f
ac
to
r
s
an
d
th
e
p
r
o
g
r
ess
io
n
o
f
C
KD
[
9]
,
[
10]
.
Desp
ite
s
ig
n
if
ican
t
ad
v
an
ce
m
en
ts
in
m
ac
h
in
e
lear
n
in
g
a
p
p
licatio
n
s
f
o
r
C
KD
p
r
ed
ictio
n
,
t
h
er
e
r
e
m
ain
s
a
g
ap
in
th
e
im
p
lem
en
tatio
n
o
f
t
h
ese
tech
n
o
lo
g
ies
in
r
ea
l
-
wo
r
ld
clin
ical
s
ettin
g
s
,
p
ar
ticu
lar
ly
in
u
n
d
er
-
r
eso
u
r
ce
d
ar
ea
s
.
Ad
d
itio
n
ally
,
p
r
ev
io
u
s
s
tu
d
ies
h
av
e
o
f
ten
f
o
cu
s
ed
o
n
s
in
g
le
m
o
d
els,
wh
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ea
s
o
u
r
ap
p
r
o
ac
h
in
te
g
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at
es
m
u
ltip
le
m
o
d
els
an
d
a
s
tack
in
g
tec
h
n
iq
u
e
to
p
o
te
n
tially
en
h
an
ce
p
r
e
d
ictiv
e
ac
cu
r
ac
y
.
T
h
is
g
a
p
in
tec
h
n
o
lo
g
y
ap
p
licatio
n
h
ig
h
lig
h
ts
an
u
r
g
en
t
n
ee
d
f
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r
m
o
r
e
tar
g
eted
r
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ch
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ev
elo
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m
e
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r
ts
th
at
ca
n
b
r
in
g
th
e
b
en
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f
its
o
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ac
h
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th
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d
tr
ea
tm
en
t.
T
o
th
is
en
d
,
o
u
r
r
esear
ch
f
o
cu
s
es
o
n
th
e
a
p
p
licatio
n
o
f
s
o
p
h
is
ticated
m
ac
h
in
e
lear
n
in
g
alg
o
r
ith
m
s
to
an
al
y
z
e
th
e
r
is
k
f
ac
to
r
s
ass
o
ciate
d
with
C
KD.
B
y
in
teg
r
atin
g
th
ese
co
m
p
u
tatio
n
al
tech
n
iq
u
es,
we
aim
to
en
h
a
n
c
e
th
e
p
r
ed
ictiv
e
ac
cu
r
ac
y
o
f
C
KD
o
n
s
et
an
d
p
r
o
g
r
ess
io
n
,
th
er
eb
y
f
ac
ilit
atin
g
ea
r
lier
in
t
er
v
en
tio
n
an
d
b
etter
clin
ical
o
u
tco
m
es.
T
h
e
m
eth
o
d
o
lo
g
y
o
f
o
u
r
r
esea
r
ch
in
v
o
lv
es
th
e
ap
p
licatio
n
o
f
f
o
u
r
m
ac
h
in
e
lear
n
in
g
m
o
d
els:
s
u
p
p
o
r
t
v
ec
to
r
m
ac
h
in
es
(
SVM)
,
ex
tr
em
e
g
r
ad
ien
t
b
o
o
s
tin
g
(
XGBo
o
s
t)
,
k
-
n
ea
r
est
n
eig
h
b
o
r
s
(
k
-
NN)
,
an
d
a
s
tack
in
g
m
o
d
el
.
E
ac
h
o
f
th
ese
m
o
d
els
b
r
in
g
s
a
u
n
iq
u
e
s
tr
en
g
t
h
in
h
an
d
lin
g
co
m
p
lex
,
n
o
n
lin
ea
r
r
elatio
n
s
h
ip
s
with
in
lar
g
e
d
atasets
,
wh
ich
is
cr
itical
in
ac
cu
r
ately
m
o
d
elin
g
th
e
m
u
ltifa
ce
ted
n
atu
r
e
o
f
C
KD
r
is
k
f
ac
to
r
s
.
Ou
r
m
eth
o
d
o
l
o
g
y
in
clu
d
es
a
r
ig
o
r
o
u
s
p
r
e
p
r
o
ce
s
s
in
g
p
h
ase
in
v
o
lv
in
g
im
p
u
tatio
n
o
f
m
is
s
in
g
v
al
u
es,
o
n
e
-
h
o
t
en
co
d
in
g
o
f
ca
teg
o
r
ical
f
ea
tu
r
es,
an
d
r
em
o
v
al
o
f
ze
r
o
-
v
ar
ia
n
ce
f
ea
tu
r
es.
Fo
llo
win
g
th
is
,
we
im
p
lem
en
ted
an
d
o
p
tim
ized
t
h
r
ee
b
ase
lear
n
e
r
s
:
SVN,
XGBo
o
s
t,
an
d
k
-
NN.
F
u
r
th
er
,
we
im
p
lem
en
ted
GL
M
Net
,
r
an
d
o
m
f
o
r
est
(
R
F)
,
a
nd
XGBo
o
s
t
,
an
d
co
m
b
in
ed
th
eir
o
u
tp
u
ts
u
s
in
g
a
lo
g
is
tic
r
eg
r
ess
io
n
-
b
ased
s
tack
i
n
g
m
o
d
el.
SVM
h
as
b
ee
n
ex
ten
s
iv
ely
ap
p
lied
in
m
ed
ical
d
iag
n
o
s
tics
d
u
e
to
its
r
o
b
u
s
tn
ess
in
d
ea
lin
g
with
h
ig
h
-
d
im
en
s
io
n
al
d
ata
an
d
its
ef
f
ec
tiv
en
ess
in
b
in
ar
y
class
if
icatio
n
p
r
o
b
lem
s
[
1
1
]
,
[
12]
.
Fo
r
in
s
tan
ce
,
Po
lat
et
a
l.
[
1
3
]
d
em
o
n
s
tr
ated
th
e
u
s
e
o
f
SVM
to
ac
cu
r
ate
ly
class
if
y
s
tag
es
o
f
k
id
n
ey
d
is
ea
s
e
u
s
in
g
clin
ical
d
ataset
s
,
h
ig
h
lig
h
tin
g
its
s
u
p
er
io
r
p
er
f
o
r
m
a
n
ce
o
v
er
o
th
er
m
ac
h
in
e
lear
n
in
g
m
o
d
els
in
ter
m
s
o
f
p
r
ec
is
io
n
an
d
r
ec
all.
An
o
th
er
s
tu
d
y
b
y
Xiao
et
a
l.
[
14]
ap
p
lied
SVM
to
d
if
f
e
r
en
tiate
b
etwe
en
C
KD
p
atien
ts
an
d
h
ea
lth
y
co
n
tr
o
ls
with
a
h
i
g
h
d
eg
r
ee
o
f
ac
cu
r
ac
y
,
u
s
in
g
g
e
n
etic
an
d
lab
o
r
ato
r
y
d
ata.
Similar
ly
,
Sin
g
h
et
a
l.
[
1
5
]
em
p
l
o
y
ed
S
VM
to
ex
p
lo
r
e
th
e
r
elatio
n
s
h
ip
b
etwe
en
C
KD
a
n
d
v
ar
io
u
s
d
em
o
g
r
a
p
h
ic
an
d
b
io
ch
em
ical
m
ar
k
er
s
,
r
esu
lti
n
g
in
a
m
o
d
el
th
at
co
u
ld
p
o
ten
tially
g
u
i
d
e
ea
r
ly
s
cr
ee
n
in
g
e
f
f
o
r
ts
in
clin
ical
s
ettin
g
s
.
Ad
d
itio
n
ally
,
C
h
en
et
a
l.
[
16]
u
tili
ze
d
SVM
to
an
aly
ze
u
l
tr
aso
u
n
d
im
a
g
es
o
f
th
e
k
id
n
ey
,
s
u
cc
ess
f
u
lly
id
en
tify
in
g
m
o
r
p
h
o
l
o
g
ical
ch
a
n
g
es
ass
o
ciate
d
with
ea
r
ly
s
tag
es
o
f
C
KD.
XGBo
o
s
t
h
as
g
ain
ed
p
o
p
u
lar
ity
f
o
r
it
s
ab
ilit
y
to
h
an
d
le
v
ar
i
o
u
s
ty
p
es
o
f
d
ata
a
n
d
its
ef
f
icien
cy
in
p
e
r
f
o
r
m
an
ce
o
n
lar
g
e
d
atasets
[
17]
.
A
s
tu
d
y
b
y
R
aih
an
et
a
l.
[
18]
em
p
lo
y
e
d
XGBo
o
s
t to
id
en
tify
C
KD
r
is
k
f
ac
to
r
s
f
r
o
m
elec
tr
o
n
ic
h
ea
lth
r
ec
o
r
d
s
,
ac
h
ie
v
in
g
s
ig
n
if
ican
t
im
p
r
o
v
em
en
ts
in
p
r
ed
ictio
n
ac
c
u
r
ac
y
co
m
p
ar
ed
t
o
tr
ad
itio
n
al
m
o
d
el
s
.
C
h
u
ah
et
a
l.
[
19]
ap
p
lied
X
GB
o
o
s
t
in
a
m
u
lti
-
ce
n
ter
s
tu
d
y
to
f
o
r
ec
ast
r
en
al
f
u
n
ctio
n
d
ec
lin
e
in
C
KD
p
atien
ts
,
wh
er
e
th
e
m
o
d
el
o
u
tp
er
f
o
r
m
ed
co
n
v
en
tio
n
al
r
is
k
s
co
r
in
g
s
y
s
tem
s
.
Mo
r
eo
v
er
,
Min
ato
et
a
l.
[
2
0
]
d
em
o
n
s
tr
ated
th
e
ef
f
ec
tiv
en
es
s
o
f
XGBo
o
s
t
in
p
r
ed
ictin
g
th
e
o
u
tco
m
es
o
f
r
en
al
tr
an
s
p
lan
tatio
n
,
a
n
ess
en
tial
asp
ec
t
o
f
m
an
a
g
in
g
ad
v
a
n
ce
d
C
KD.
Me
an
wh
ile,
th
e
k
-
NN
alg
o
r
ith
m
is
v
alu
e
d
f
o
r
its
s
im
p
licity
an
d
ef
f
ec
tiv
e
n
ess
,
p
ar
ticu
lar
ly
in
s
ce
n
ar
io
s
wh
er
e
th
e
r
elatio
n
s
h
ip
b
etwe
e
n
v
ar
iab
les
is
n
o
n
-
lin
ea
r
[
21]
.
Dev
ik
a
et
a
l.
[
22]
em
p
lo
y
ed
k
-
NN
to
class
if
y
C
KD
s
ev
er
ity
b
ased
o
n
b
io
ch
em
ical
an
d
p
h
y
s
io
lo
g
ical
p
ar
a
m
eter
s
wit
h
h
ig
h
ac
cu
r
ac
y
,
d
em
o
n
s
tr
atin
g
th
e
ef
f
ec
tiv
en
ess
o
f
th
is
m
ac
h
in
e
lear
n
in
g
tech
n
iq
u
e
i
n
id
en
tif
y
in
g
t
h
e
c
o
n
d
itio
n
’
s
s
tag
es.
Fu
r
t
h
er
m
o
r
e,
Ma
h
b
o
o
b
et
a
l.
[
23]
p
r
esen
ted
a
f
r
a
m
ewo
r
k
f
o
r
im
p
u
tin
g
m
is
s
in
g
v
alu
es
in
la
r
g
e
C
KD
d
atasets
u
s
in
g
-
Nea
r
est
Neig
h
b
o
r
s
(
k
-
NN)
,
-
Me
an
s
,
an
d
-
Me
d
o
id
s
clu
s
ter
in
g
alg
o
r
it
h
m
s
,
d
em
o
n
s
tr
atin
g
th
at
k
-
NN
p
r
o
v
id
e
d
th
e
m
o
s
t
ac
cu
r
ate
r
esu
lts
,
ac
h
ie
v
in
g
a
n
ac
cu
r
ac
y
o
f
8
6
.
6
7
%
with
Dec
is
io
n
T
r
ee
an
d
7
5
.
2
5
%
with
RF
,
co
m
p
a
r
ed
to
th
e
o
th
er
al
g
o
r
ith
m
s
.
I
n
ad
d
itio
n
to
th
ese
m
o
d
els,
we
ap
p
lied
a
s
tack
in
g
m
o
d
el,
wh
ich
co
m
b
in
es
th
e
p
r
ed
ictio
n
s
o
f
SVM,
XGBo
o
s
t,
an
d
k
-
NN
u
s
in
g
a
lo
g
is
tic
r
eg
r
ess
io
n
m
eta
-
lear
n
er
to
en
h
an
ce
p
r
ed
ictiv
e
p
er
f
o
r
m
an
ce
.
A
r
ec
en
t
s
tu
d
y
by
Ma
h
ajan
et
a
l.
[
24]
h
av
e
h
ig
h
lig
h
ted
th
e
p
o
ten
tial o
f
en
s
em
b
le
lea
r
n
in
g
tech
n
iq
u
es
in
m
ed
ical
d
iag
n
o
s
tics
,
y
et
th
eir
ap
p
licatio
n
to
C
KD
p
r
ed
ictio
n
r
em
ain
s
u
n
d
er
ex
p
lo
r
e
d
.
Fu
r
th
er
m
o
r
e,
co
n
f
lictin
g
f
in
d
in
g
s
i
n
th
e
liter
a
tu
r
e
r
e
g
ar
d
in
g
t
h
e
ef
f
icac
y
o
f
d
if
f
er
en
t
m
ac
h
in
e
lear
n
in
g
m
o
d
els
u
n
d
er
s
co
r
e
t
h
e
n
ee
d
f
o
r
co
m
p
r
eh
en
s
iv
e
c
o
m
p
ar
ativ
e
an
al
y
s
es
lik
e
o
u
r
s
.
Ou
r
s
tu
d
y
n
o
t
o
n
ly
ap
p
lies
th
e
s
e
m
o
d
els
b
u
t
also
cr
itically
a
s
s
es
s
es
th
eir
p
er
f
o
r
m
an
ce
i
n
t
h
e
co
n
te
x
t
o
f
C
KD
p
r
e
d
ictio
n
.
W
e
u
tili
ze
a
c
o
m
p
r
e
h
en
s
iv
e
d
ataset
co
m
p
r
is
in
g
clin
ical
an
d
d
em
o
g
r
ap
h
ic
v
a
r
iab
les
co
llected
f
r
o
m
p
atie
n
ts
s
u
s
p
ec
ted
o
f
h
av
i
n
g
C
KD.
T
h
ese
i
n
clu
d
e,
b
u
t
a
r
e
n
o
t
lim
ited
t
o
,
m
ar
k
er
s
o
f
k
id
n
e
y
f
u
n
ctio
n
s
u
c
h
as
s
er
u
m
cr
ea
ti
n
in
e
lev
els,
u
r
ea
,
an
d
h
em
o
g
lo
b
in
lev
els
,
as
well
as
d
em
o
g
r
ap
h
ic
f
ac
to
r
s
lik
e
ag
e
an
d
ap
p
etite
.
Ou
r
an
aly
s
is
aim
s
to
d
elin
ea
te
th
e
s
p
ec
if
ic
co
n
tr
ib
u
tio
n
s
o
f
ea
ch
f
ac
t
o
r
t
o
th
e
r
is
k
o
f
C
KD,
o
f
f
er
in
g
in
s
ig
h
ts
in
to
b
o
th
t
h
e
b
io
lo
g
ical
a
n
d
s
o
cial
d
ete
r
m
i
n
an
ts
o
f
k
id
n
ey
h
ea
lth
.
Fu
r
th
er
m
o
r
e,
we
aim
to
b
r
id
g
e
t
h
e
tech
n
o
lo
g
ical
g
ap
in
p
u
b
lic
h
ea
lth
a
p
p
licatio
n
s
b
y
d
e
m
o
n
s
tr
atin
g
th
e
ef
f
ec
tiv
en
ess
o
f
m
ac
h
i
n
e
lear
n
in
g
i
n
im
p
r
o
v
i
n
g
C
KD
d
iag
n
o
s
is
an
d
m
an
ag
em
e
n
t,
p
ar
ticu
lar
ly
in
r
eso
u
r
ce
-
lim
ited
s
ettin
g
s
.
T
h
is
Evaluation Warning : The document was created with Spire.PDF for Python.
I
SS
N
:
2
5
0
2
-
4
7
52
In
d
o
n
esian
J
E
lec
E
n
g
&
C
o
m
p
Sci
,
Vo
l.
41
,
No
.
3
,
Ma
r
ch
20
2
6
:
9
6
6
-
976
968
r
esear
ch
h
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2
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ma
chine
SVM
is
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s
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p
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ac
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ea
r
m
ap
p
in
g
to
a
h
ig
h
e
r
-
d
i
m
en
s
io
n
al
s
p
ac
e,
is
th
e
r
eg
u
lar
izatio
n
p
a
r
am
eter
,
an
d
ar
e
s
lack
v
ar
iab
l
es.
T
o
f
u
r
t
h
er
v
ali
d
ate
th
e
m
o
d
e
l,
we
co
m
p
u
te
d
p
er
f
o
r
m
a
n
ce
m
etr
ics
s
u
ch
as
ac
cu
r
ac
y
,
s
en
s
itiv
ity
,
s
p
ec
if
icity
,
p
r
ec
is
io
n
,
r
ec
all,
F1
-
Sco
r
e,
an
d
R
OC
AU
C
.
Mo
r
eo
v
er
,
we
em
p
lo
y
ed
th
e
p
er
m
u
tatio
n
f
ea
tu
r
e
im
p
o
r
tan
ce
tec
h
n
iq
u
e
to
id
e
n
tify
th
e
m
o
s
t
in
f
lu
en
tial
f
ea
tu
r
es
co
n
tr
ib
u
tin
g
to
th
e
m
o
d
el’
s
p
r
ed
ictio
n
s
.
T
h
is
ap
p
r
o
ac
h
p
r
o
v
i
d
ed
i
n
s
ig
h
ts
in
to
th
e
u
n
d
er
ly
in
g
d
ata
p
atte
r
n
s
an
d
v
alid
ated
th
e
im
p
o
r
ta
n
ce
o
f
k
e
y
clin
ical
v
ar
iab
les in
p
r
e
d
ictin
g
C
KD.
2
.
3
.
Ex
t
re
m
e
g
r
a
dient
bo
o
s
t
ing
XGBo
o
s
t
is
a
s
ca
lab
le
m
ac
h
in
e
lear
n
in
g
s
y
s
tem
f
o
r
tr
ee
b
o
o
s
tin
g
.
I
t
im
p
lem
e
n
ts
a
g
r
ad
ien
t
-
b
o
o
s
ted
d
ec
is
io
n
tr
ee
alg
o
r
ith
m
with
s
u
p
er
io
r
s
p
ee
d
a
n
d
p
er
f
o
r
m
an
ce
.
XGBo
o
s
t
s
eq
u
en
tially
b
u
ild
s
a
s
er
ies
o
f
d
ec
is
io
n
tr
ee
s
wh
er
e
ea
ch
n
ew
tr
ee
attem
p
ts
to
co
r
r
ec
t
er
r
o
r
s
m
ad
e
b
y
th
e
p
r
ev
io
u
s
tr
ee
s
,
r
esu
ltin
g
in
a
r
o
b
u
s
t
an
d
ac
cu
r
ate
p
r
ed
ictiv
e
m
o
d
el
[
26]
.
An
XGBo
o
s
t
m
o
d
el
was
d
ev
elo
p
ed
u
s
in
g
th
e
XGBo
o
s
t
p
ac
k
ag
e.
T
o
en
s
u
r
e
th
e
m
o
d
el
’
s
r
o
b
u
s
tn
ess
an
d
r
ep
r
o
d
u
ci
b
ilit
y
,
we
p
er
f
o
r
m
ed
m
eticu
lo
u
s
p
r
e
p
r
o
ce
s
s
in
g
s
tep
s
,
in
clu
d
in
g
im
p
u
tin
g
m
is
s
in
g
v
alu
es,
o
n
e
-
h
o
t
en
co
d
in
g
ca
teg
o
r
ical
v
ar
iab
les,
an
d
r
em
o
v
in
g
ze
r
o
-
v
ar
ian
ce
f
ea
tu
r
es.
W
e
th
en
s
p
lit
th
e
d
ataset
in
to
tr
ain
in
g
an
d
test
in
g
s
ets,
m
ain
tain
in
g
co
n
s
is
ten
t c
lass
p
r
o
p
o
r
t
io
n
s
th
r
o
u
g
h
s
tr
atif
ied
s
am
p
lin
g
.
T
h
e
tr
ain
in
g
s
et
co
m
p
r
is
ed
8
0
%
o
f
th
e
d
ata,
w
h
ile
th
e
r
em
ain
in
g
2
0
%
f
o
r
m
e
d
th
e
test
s
et.
Nu
m
er
ical
f
ea
tu
r
es
wer
e
s
ca
led
to
en
h
an
ce
th
e
m
o
d
el
’
s
p
er
f
o
r
m
an
ce
.
A
g
r
id
s
ea
r
ch
was
co
n
d
u
cted
o
v
er
v
a
r
io
u
s
co
m
b
i
n
atio
n
s
o
f
p
ar
am
eter
s
s
u
ch
as
n
r
o
u
n
d
s
,
eta,
m
ax
_
d
ep
th
,
g
am
m
a,
co
ls
am
p
le_
b
y
tr
ee
,
m
in
_
ch
ild
_
weig
h
t,
an
d
s
u
b
s
am
p
le
.
T
h
e
tr
ain
C
o
n
tr
o
l
f
u
n
ctio
n
was u
s
ed
f
o
r
1
0
-
f
o
ld
cr
o
s
s
-
v
alid
atio
n
,
an
d
th
e
tr
ain
f
u
n
ctio
n
f
r
o
m
th
e
ca
r
et
p
ac
k
ag
e
was
u
s
ed
to
b
u
ild
th
e
m
o
d
el.
T
h
e
o
b
jectiv
e
f
u
n
ctio
n
o
f
XGBo
o
s
t c
an
b
e
d
e
f
in
ed
as
(
)
=
∑
(
̂
,
)
+
∑
Ω
(
)
=
1
=
1
(
2
)
wh
er
e
is
th
e
lo
s
s
f
u
n
ctio
n
,
̂
is
th
e
p
r
ed
icted
v
alu
e,
is
th
e
tr
u
e
v
alu
e,
Ω
is
th
e
r
eg
u
lar
izatio
n
t
er
m
,
an
d
is
th
e
in
d
iv
id
u
al
tr
ee
m
o
d
e
l.
T
h
e
m
o
d
el
’
s
p
er
f
o
r
m
an
ce
was
th
en
ev
alu
ated
o
n
th
e
test
d
ataset
an
d
p
er
f
o
r
m
an
ce
m
etr
ics
wer
e
co
m
p
u
ted
.
Ad
d
itio
n
ally
,
we
c
o
n
d
u
cted
a
f
ea
tu
r
e
im
p
o
r
tan
ce
an
aly
s
is
to
id
en
tify
th
e
m
o
s
t
in
f
l
u
en
tial
f
ea
tu
r
es
co
n
tr
ib
u
tin
g
to
th
e
m
o
d
el
’
s
p
r
ed
ictio
n
s
.
T
h
is
was
ac
h
ie
v
ed
b
y
p
lo
ttin
g
t
h
e
f
ea
tu
r
e
im
p
o
r
tan
ce
,
wh
ic
h
h
ig
h
lig
h
ted
th
e
k
ey
v
ar
iab
les d
r
iv
in
g
th
e
m
o
d
el
’
s
ac
cu
r
ac
y
.
2
.
4
.
K
-
n
ea
re
s
t
neig
hb
o
rs
k
-
Nea
r
est
Neig
h
b
o
r
s
is
a
n
o
n
-
p
ar
am
etr
ic,
in
s
tan
ce
-
b
ased
lea
r
n
in
g
m
eth
o
d
u
s
ed
f
o
r
class
if
icatio
n
an
d
r
eg
r
ess
io
n
.
T
h
e
al
g
o
r
ith
m
cla
s
s
if
ies
a
d
ata
p
o
in
t
b
ased
o
n
t
h
e
m
ajo
r
ity
v
o
te
o
f
th
e
n
ea
r
e
s
t
n
eig
h
b
o
r
s
.
T
h
e
ch
o
ice
o
f
s
ig
n
if
ican
tly
af
f
ec
ts
th
e
m
o
d
el
’
s
p
er
f
o
r
m
an
ce
,
with
a
lo
wer
v
alu
e
lead
in
g
to
m
o
r
e
n
o
is
e
s
en
s
itiv
ity
an
d
a
h
ig
h
e
r
v
alu
e
p
o
ten
tially
d
ilu
tin
g
th
e
d
ec
is
io
n
b
o
u
n
d
ar
ies
[
27]
.
T
h
e
k
-
NN
m
o
d
el
was
d
ev
elo
p
ed
u
s
in
g
t
h
e
ca
r
et
p
ac
k
ag
e.
T
o
e
n
s
u
r
e
th
e
r
o
b
u
s
tn
ess
an
d
r
ep
r
o
d
u
cib
ilit
y
o
f
o
u
r
m
o
d
el,
we
u
n
d
er
to
o
k
co
m
p
r
e
h
en
s
iv
e
p
r
ep
r
o
ce
s
s
in
g
s
tep
s
.
T
h
is
in
clu
d
ed
im
p
u
tin
g
m
is
s
in
g
v
alu
es,
o
n
e
-
h
o
t e
n
c
o
d
in
g
ca
teg
o
r
ical
v
ar
ia
b
les,
an
d
r
em
o
v
in
g
ze
r
o
-
v
ar
ian
ce
f
ea
tu
r
es.
T
h
e
d
ataset
wa
s
s
p
lit
in
to
tr
ain
in
g
an
d
test
in
g
s
ets,
m
ain
tain
in
g
co
n
s
is
ten
t
cl
ass
p
r
o
p
o
r
tio
n
s
th
r
o
u
g
h
s
tr
atif
ied
s
am
p
lin
g
.
T
h
e
tr
ain
in
g
s
et
co
m
p
r
is
ed
8
0
%
o
f
th
e
d
ata,
wh
ile
th
e
r
em
ain
in
g
2
0
%
f
o
r
m
ed
th
e
test
s
et.
Nu
m
er
ical
f
ea
tu
r
es
wer
e
s
ca
led
to
im
p
r
o
v
e
m
o
d
el
p
er
f
o
r
m
an
ce
.
A
g
r
id
s
ea
r
ch
was
co
n
d
u
cte
d
o
v
er
t
h
e
p
ar
am
eter
with
o
d
d
v
alu
es
r
a
n
g
in
g
f
r
o
m
1
to
3
0
.
T
h
e
tr
ain
C
o
n
tr
o
l
f
u
n
ctio
n
was u
s
ed
f
o
r
1
0
-
f
o
ld
cr
o
s
s
-
v
alid
atio
n
,
an
d
th
e
tr
ain
f
u
n
ctio
n
f
r
o
m
th
e
ca
r
et
p
ac
k
ag
e
was
u
s
ed
to
b
u
ild
th
e
m
o
d
el.
k
-
NN
class
if
ies
a
n
ew
d
ata
p
o
in
t
b
ased
o
n
th
e
m
ajo
r
ity
v
o
te
o
f
th
e
n
ea
r
est
n
eig
h
b
o
r
s
:
̂
=
a
r
g
ma
x
∑
1
(
=
)
∈
(
3
)
wh
er
e
is
th
e
s
et
o
f
n
ea
r
est
n
eig
h
b
o
r
s
an
d
1
is
th
e
i
n
d
icato
r
f
u
n
ctio
n
.
T
o
v
alid
ate
t
h
e
m
o
d
el,
we
co
m
p
u
ted
t
h
e
p
er
f
o
r
m
an
ce
m
etr
ics.
Ad
d
itio
n
ally
,
we
co
n
d
u
cted
a
f
ea
tu
r
e
im
p
o
r
tan
ce
an
al
y
s
is
to
id
en
tify
th
e
m
o
s
t in
f
lu
en
tial f
ea
tu
r
es c
o
n
tr
ib
u
tin
g
to
t
h
e
m
o
d
el
’
s
p
r
ed
icti
o
n
s
.
2
.
5
.
St
a
ck
ing
m
o
del
Stack
in
g
is
an
en
s
em
b
le
lea
r
n
in
g
tech
n
iq
u
e
th
at
co
m
b
i
n
es
m
u
ltip
le
class
if
icatio
n
m
o
d
els
(
b
ase
lear
n
er
s
)
to
im
p
r
o
v
e
p
r
e
d
ictiv
e
p
er
f
o
r
m
an
ce
b
y
lev
er
ag
i
n
g
th
e
s
tr
en
g
th
s
o
f
ea
ch
m
o
d
e
l
[
28
]
,
[
29]
.
I
n
th
is
s
tu
d
y
,
we
im
p
lem
en
ted
a
s
tac
k
in
g
m
o
d
el
u
s
in
g
th
r
ee
b
ase
lear
n
er
s
:
g
en
er
alize
d
lin
ea
r
m
o
d
el
(
GL
MN
et)
,
R
F,
Evaluation Warning : The document was created with Spire.PDF for Python.
I
SS
N
:
2
5
0
2
-
4
7
52
In
d
o
n
esian
J
E
lec
E
n
g
&
C
o
m
p
Sci
,
Vo
l.
41
,
No
.
3
,
Ma
r
ch
20
2
6
:
9
6
6
-
976
970
an
d
XGBo
o
s
t.
T
h
ese
b
ase
lea
r
n
er
s
wer
e
ch
o
s
en
f
o
r
th
eir
d
i
v
er
s
e
s
tr
en
g
th
s
in
h
an
d
lin
g
d
if
f
er
en
t
d
ata
p
atter
n
s
an
d
co
m
p
lex
ities
.
T
o
en
s
u
r
e
th
e
r
o
b
u
s
tn
ess
an
d
r
e
p
r
o
d
u
cib
ilit
y
o
f
o
u
r
m
o
d
el,
we
u
n
d
er
to
o
k
c
o
m
p
r
eh
e
n
s
iv
e
p
r
ep
r
o
ce
s
s
in
g
s
tep
s
.
T
h
is
in
c
lu
d
ed
im
p
u
tin
g
m
is
s
in
g
v
alu
es,
o
n
e
-
h
o
t
en
co
d
in
g
ca
teg
o
r
ical
v
ar
iab
les,
an
d
r
em
o
v
in
g
ze
r
o
-
v
a
r
ian
ce
f
ea
tu
r
es.
T
h
e
d
ataset
was
th
e
n
s
p
lit
in
to
tr
ain
in
g
a
n
d
test
in
g
s
ets,
m
ain
tain
in
g
co
n
s
is
ten
t
class
p
r
o
p
o
r
tio
n
s
t
h
r
o
u
g
h
s
tr
at
if
ied
s
am
p
lin
g
.
T
h
e
tr
ain
in
g
s
et
co
m
p
r
is
ed
8
0
%
o
f
th
e
d
ata,
wh
ile
th
e
r
em
ain
in
g
2
0
%
f
o
r
m
ed
th
e
test
s
et.
Nu
m
er
ical
f
ea
tu
r
es
wer
e
s
ca
led
to
im
p
r
o
v
e
m
o
d
e
l
p
er
f
o
r
m
a
n
ce
.
T
h
e
b
ase
lear
n
er
s
wer
e
tr
ain
ed
u
s
in
g
th
e
tr
ain
f
u
n
ctio
n
f
r
o
m
th
e
ca
r
et
p
ac
k
ag
e
with
1
0
-
f
o
l
d
cr
o
s
s
-
v
alid
atio
n
to
en
s
u
r
e
g
en
e
r
aliza
b
ilit
y
.
T
h
e
h
y
p
er
p
a
r
am
eter
s
f
o
r
ea
ch
b
ase
l
ea
r
n
er
wer
e
tu
n
ed
u
s
in
g
g
r
i
d
s
ea
r
ch
:
−
GL
MN
et:
R
eg
u
lar
ized
lo
g
is
tic
r
eg
r
ess
io
n
m
o
d
el
tu
n
e
d
f
o
r
o
p
tim
al
lam
b
d
a
an
d
alp
h
a
v
alu
es.
−
RF
: A
n
en
s
em
b
le
o
f
d
ec
is
io
n
t
r
ee
s
tu
n
ed
f
o
r
th
e
n
u
m
b
er
o
f
tr
ee
s
an
d
m
ax
im
u
m
f
ea
tu
r
es.
−
XGBo
o
s
t:
Gr
ad
ien
t
b
o
o
s
tin
g
alg
o
r
ith
m
tu
n
ed
f
o
r
n
r
o
u
n
d
s
,
eta,
m
ax
_
d
ep
th
,
g
am
m
a
,
co
l
s
am
p
le_
b
y
tr
ee
,
m
in
_
ch
ild
_
weig
h
t,
an
d
s
u
b
s
a
m
p
le.
Af
ter
tr
ain
in
g
th
e
b
ase
lear
n
e
r
s
,
p
r
ed
ictio
n
s
wer
e
m
ad
e
o
n
th
e
test
d
ataset.
T
h
ese
p
r
ed
i
ctio
n
s
wer
e
th
en
co
m
b
in
e
d
in
to
a
n
ew
d
at
aset,
wh
ich
s
er
v
ed
as
in
p
u
t
f
o
r
th
e
m
eta
-
lear
n
er
.
T
h
e
m
eta
-
l
ea
r
n
er
was
tr
ain
ed
u
s
in
g
lo
g
is
tic
r
eg
r
ess
io
n
with
1
0
-
f
o
l
d
cr
o
s
s
-
v
alid
atio
n
,
u
s
in
g
th
e
co
m
b
in
ed
p
r
ed
ictio
n
s
f
r
o
m
th
e
b
ase
lear
n
er
s
to
m
ak
e
th
e
f
in
al
class
if
i
ca
t
i
o
n
.
T
h
is
ap
p
r
o
ac
h
allo
wed
th
e
m
eta
-
lear
n
er
to
lear
n
f
r
o
m
th
e
s
tr
en
g
th
s
an
d
wea
k
n
ess
es
o
f
ea
ch
b
ase
lear
n
er
,
r
esu
ltin
g
in
a
m
o
r
e
r
o
b
u
s
t
an
d
ac
cu
r
ate
p
r
ed
ictiv
e
m
o
d
el.
Fig
u
r
e
1
p
r
esen
ts
th
e
d
iag
r
am
o
f
th
e
p
r
o
p
o
s
ed
s
t
ac
k
in
g
m
o
d
el.
Fig
u
r
e
1
.
Pro
p
o
s
ed
s
tack
in
g
m
o
d
el
f
lo
wch
a
r
t
2
.
6
.
M
o
del
perf
o
rma
nce
m
e
t
rics
T
h
e
p
e
r
f
o
r
m
an
ce
o
f
ea
ch
m
o
d
el
was
ass
ess
ed
u
s
in
g
s
e
v
er
al
ev
al
u
atio
n
m
etr
ics
to
p
r
o
v
id
e
a
co
m
p
r
eh
e
n
s
iv
e
an
aly
s
is
o
f
th
e
ir
p
r
ed
ictiv
e
ca
p
a
b
ilit
ies.
T
h
e
f
o
llo
win
g
m
etr
ics
wer
e
em
p
lo
y
ed
to
ev
alu
ate
th
e
ef
f
ec
tiv
en
ess
an
d
r
eliab
ilit
y
o
f
th
e
m
o
d
els:
Acc
u
r
ac
y
m
ea
s
u
r
es
th
e
o
v
er
al
l c
o
r
r
ec
tn
ess
o
f
th
e
m
o
d
el
a
n
d
is
ca
lcu
lated
as:
A
c
c
ura
c
y
:
+
+
+
+
(
4
)
Pre
cisi
o
n
(
p
o
s
itiv
e
p
r
ed
ictiv
e
v
alu
e
)
in
d
icate
s
th
e
p
r
o
p
o
r
tio
n
o
f
p
o
s
itiv
e
p
r
ed
ictio
n
s
th
at
a
r
e
ac
tu
ally
co
r
r
ec
t:
Pr
e
c
ision
(
pos
pr
e
d
va
l
ue
)
:
+
(
5
)
R
ec
all
(
s
en
s
itiv
ity
)
r
ef
lects th
e
p
r
o
p
o
r
tio
n
o
f
ac
tu
al
p
o
s
itiv
es th
at
ar
e
co
r
r
ec
tl
y
id
en
tifie
d
b
y
th
e
m
o
d
el:
R
e
c
a
l
l
(
s
e
n
s
it
ivity
)
:
+
(
6
)
Evaluation Warning : The document was created with Spire.PDF for Python.
I
n
d
o
n
esian
J
E
lec
E
n
g
&
C
o
m
p
Sci
I
SS
N:
2
5
0
2
-
4
7
52
E
n
h
a
n
ce
d
p
r
ed
ictio
n
o
f c
h
r
o
n
i
c
kid
n
ey
d
is
ea
s
e
o
n
s
et
th
r
o
u
g
h
ma
ch
in
e
lea
r
n
in
g
…
(
S
a
mu
el
Jo
h
n
P
a
r
r
eñ
o
)
971
F
1
Sco
r
e
p
r
o
v
id
es a
h
ar
m
o
n
ic
m
ea
n
o
f
p
r
ec
is
io
n
an
d
r
ec
all,
o
f
f
er
in
g
a
b
alan
ce
b
etwe
en
th
e
two
m
etr
ics:
F1
Score
:
2
⋅
Pr
ecis
i
o
n
⋅
Recal
l
Pr
ecis
i
o
n
+
Recal
l
(
7
)
wh
er
e
ar
e
tr
u
e
p
o
s
itiv
es,
ar
e
tr
u
e
n
eg
ativ
es,
ar
e
f
alse
p
o
s
itiv
es,
an
d
ar
e
f
alse
n
eg
ativ
es.
T
h
ese
m
etr
ics
co
llectiv
ely
o
f
f
er
a
d
etailed
v
iew
o
f
th
e
m
o
d
el
’
s
p
er
f
o
r
m
a
n
ce
,
ad
d
r
ess
in
g
v
ar
io
u
s
asp
ec
ts
o
f
p
r
ed
ictio
n
ac
cu
r
ac
y
an
d
er
r
o
r
r
ates.
E
ac
h
m
etr
ic
p
r
o
v
id
es
u
n
iq
u
e
in
s
ig
h
ts
in
to
d
if
f
er
en
t
p
er
f
o
r
m
a
n
ce
asp
ec
ts
,
th
er
eb
y
e
n
s
u
r
in
g
a
co
m
p
r
eh
en
s
iv
e
ev
alu
atio
n
o
f
th
e
m
o
d
els.
3.
RE
SU
L
T
S AN
D
D
I
SCU
SS
I
O
N
T
h
e
in
itial
d
ataset
co
n
tain
ed
2
0
0
s
am
p
les
an
d
2
8
f
ea
tu
r
es
r
ep
r
esen
tin
g
clin
ical
an
d
d
e
m
o
g
r
ap
h
ic
attr
ib
u
tes
r
elev
an
t
to
ch
r
o
n
ic
k
id
n
ey
d
is
ea
s
e.
Pre
p
r
o
ce
s
s
in
g
was
d
o
n
e
to
th
e
d
ata
b
ef
o
r
e
im
p
lem
en
tin
g
th
e
m
o
d
els.
Af
ter
r
ep
lacin
g
p
lac
eh
o
ld
er
s
tr
in
g
s
with
NA
v
al
u
es
an
d
im
p
u
tin
g
m
is
s
in
g
n
u
m
er
ical
d
ata
u
s
in
g
m
ed
ian
im
p
u
tatio
n
,
t
h
e
d
atas
et
was
o
n
e
-
h
o
t
en
co
d
ed
to
tr
a
n
s
f
o
r
m
ca
teg
o
r
ical
f
ea
t
u
r
es
in
to
n
u
m
e
r
ical
o
n
es.
Featu
r
es
with
ze
r
o
v
ar
ian
ce
wer
e
r
em
o
v
ed
,
as
th
ey
d
o
n
o
t
p
r
o
v
id
e
an
y
u
s
ef
u
l
in
f
o
r
m
atio
n
f
o
r
th
e
m
o
d
el
an
d
ca
n
lead
to
o
v
er
f
itti
n
g
.
T
h
is
r
esu
lted
in
a
f
in
al
d
ataset
o
f
1
6
1
s
am
p
les
an
d
1
4
3
f
ea
tu
r
es,
w
h
ich
in
clu
d
ed
o
n
e
-
h
o
t
en
co
d
ed
a
n
d
n
u
m
er
ical
at
tr
ib
u
tes.
Af
ter
p
r
e
p
r
o
ce
s
s
in
g
,
th
e
d
ataset
was
d
iv
id
ed
in
to
t
r
ain
in
g
an
d
test
in
g
s
ets
u
s
in
g
s
tr
atif
ied
s
am
p
lin
g
to
en
s
u
r
e
co
n
s
is
ten
t
class
p
r
o
p
o
r
tio
n
s
.
T
h
e
tr
ain
i
n
g
s
et
co
n
t
ain
ed
1
2
9
s
am
p
les,
wh
ile
th
e
test
in
g
s
et
h
a
d
3
2
s
a
m
p
les.
Nu
m
er
ical
f
ea
tu
r
es
in
th
e
tr
ain
in
g
an
d
test
in
g
s
ets
we
r
e
s
ca
led
to
en
s
u
r
e
co
n
s
is
ten
t p
er
f
o
r
m
a
n
ce
ac
r
o
s
s
d
if
f
er
en
t
m
ac
h
in
e
lear
n
in
g
m
o
d
els.
T
h
e
SVM
m
o
d
el
was
im
p
lem
en
ted
u
s
in
g
th
e
s
v
m
R
ad
ial
m
eth
o
d
in
th
e
ca
r
et
p
ac
k
a
g
e.
G
r
id
s
ea
r
ch
was
u
s
ed
to
tu
n
e
th
e
h
y
p
er
p
a
r
am
eter
s
an
d
.
T
h
e
b
est
co
m
b
in
atio
n
o
f
p
ar
am
eter
s
was
f
o
u
n
d
to
b
e
=
0
.
01
an
d
=
4
.
T
h
e
SVM
m
o
d
el
ac
h
iev
ed
an
ac
c
u
r
ac
y
o
f
1
0
0
%,
in
d
ic
atin
g
th
at
it
co
r
r
ec
tly
class
if
ied
all
test
s
am
p
les.
T
h
e
Kap
p
a
s
tati
s
tic
o
f
1
s
ig
n
if
ies
p
er
f
ec
t
ag
r
ee
m
e
n
t
b
etwe
en
p
r
ed
icted
an
d
ac
tu
al
class
e
s
.
T
h
e
h
ig
h
s
en
s
itiv
ity
(
1
.
0
0
0
)
an
d
s
p
ec
if
icity
(
1
.
0
0
0
)
d
em
o
n
s
tr
ate
th
at
th
e
m
o
d
el
ca
n
ac
cu
r
ately
d
is
tin
g
u
is
h
b
etwe
en
C
KD
an
d
n
o
n
-
C
KD
s
am
p
les
.
Ad
d
itio
n
ally
,
th
e
p
o
s
itiv
e
p
r
ed
ictiv
e
v
a
lu
e
(
1
.
0
0
0
)
a
n
d
n
eg
ativ
e
p
r
ed
ictiv
e
v
alu
e
(
1
.
0
0
0
)
co
n
f
ir
m
th
e
m
o
d
el
’
s
r
eliab
ilit
y
in
id
en
tif
y
in
g
b
o
t
h
C
KD
-
p
o
s
itiv
e
an
d
C
KD
-
n
eg
ativ
e
ca
s
es.
T
ab
le
2
p
r
esen
ts
th
e
ev
alu
atio
n
m
etr
ics o
f
th
e
SVM
m
o
d
el.
T
ab
le
2
.
E
v
alu
atio
n
m
et
r
ics o
f
th
e
SVM
m
o
d
el
M
e
t
r
i
c
V
a
l
u
e
A
c
c
u
r
a
c
y
1
.
0
0
0
0
9
5
%
c
o
n
f
i
d
e
n
c
e
i
n
t
e
r
v
a
l
(
0
.
9
0
9
7
,
1
)
N
o
i
n
f
o
r
m
a
t
i
o
n
r
a
t
e
0
.
6
4
1
0
K
a
p
p
a
1
.
0
0
0
0
S
e
n
s
i
t
i
v
i
t
y
1
.
0
0
0
0
S
p
e
c
i
f
i
c
i
t
y
1
.
0
0
0
0
P
o
si
t
i
v
e
p
r
e
d
i
c
t
i
v
e
v
a
l
u
e
1
.
0
0
0
0
N
e
g
a
t
i
v
e
p
r
e
d
i
c
t
i
v
e
v
a
l
u
e
1
.
0
0
0
0
P
r
e
v
a
l
e
n
c
e
0
.
6
4
1
0
D
e
t
e
c
t
i
o
n
r
a
t
e
0
.
6
4
1
0
D
e
t
e
c
t
i
o
n
p
r
e
v
a
l
e
n
c
e
0
.
6
4
1
0
B
a
l
a
n
c
e
d
a
c
c
u
r
a
c
y
1
.
0
0
0
0
T
h
e
XGBo
o
s
t
m
o
d
el
was
im
p
lem
en
ted
u
s
in
g
t
h
e
x
g
b
T
r
ee
m
eth
o
d
in
th
e
ca
r
et
p
ac
k
a
g
e.
Gr
id
s
ea
r
ch
an
d
cr
o
s
s
-
v
alid
atio
n
wer
e
u
s
ed
to
tu
n
e
th
e
f
o
llo
win
g
h
y
p
er
p
ar
am
eter
s
:
n
r
o
u
n
d
s
,
m
ax
_
d
ep
th
,
eta,
g
am
m
a,
co
ls
am
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ee
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m
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_
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ild
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h
e
b
est p
ar
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m
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i
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h
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T
a
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ab
le
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XGBo
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t b
est p
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t
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m
p
l
e
0
.
5
Evaluation Warning : The document was created with Spire.PDF for Python.
I
SS
N
:
2
5
0
2
-
4
7
52
In
d
o
n
esian
J
E
lec
E
n
g
&
C
o
m
p
Sci
,
Vo
l.
41
,
No
.
3
,
Ma
r
ch
20
2
6
:
9
6
6
-
976
972
As
p
r
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ted
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a
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le
4
,
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e
XGBo
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t
m
o
d
el
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h
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ited
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tr
o
n
g
class
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icatio
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e
r
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o
r
m
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e,
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h
iev
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g
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ac
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r
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tr
ate
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ilit
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is
tin
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is
h
b
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C
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n
o
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KD
s
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les
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tify
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KD
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a
n
d
C
KD
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n
eg
ativ
e
ca
s
es.
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ab
le
4
.
E
v
alu
atio
n
m
et
r
ics o
f
th
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XGBo
o
s
t
m
o
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el
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e
t
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c
V
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l
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l
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T
h
e
k
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o
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el
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im
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le
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ted
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s
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et
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e.
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r
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h
e
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est
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ar
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eter
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o
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n
d
is
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NN
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el
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tr
ain
ed
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e
o
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tim
al
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e,
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d
p
r
ed
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n
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e
m
ad
e
o
n
t
h
e
test
d
ata.
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ab
le
5
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r
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ce
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ics.
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h
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k
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f
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4
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d
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s
u
b
s
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tial
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en
t
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etwe
en
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ed
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s
an
d
ac
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al
lab
els.
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h
e
m
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d
el
’
s
s
en
s
itiv
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0
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8
0
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d
s
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if
icity
(
1
.
0
0
0
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)
r
ef
lect
its
ef
f
ec
t
iv
en
es
s
in
id
e
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tify
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g
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KD
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n
d
n
o
n
-
C
KD
ca
s
es.
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h
e
p
o
s
itiv
e
p
r
e
d
ictiv
e
v
alu
e
(
1
.
0
0
0
0
)
a
n
d
n
eg
ativ
e
p
r
ed
icti
v
e
v
alu
e
(
0
.
8
2
3
5
)
h
ig
h
lig
h
t
th
e
m
o
d
el
’
s
ab
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to
p
r
ed
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C
KD
ac
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r
ately
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m
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im
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alse
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eg
ativ
es.
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h
e
b
alan
ce
d
ac
cu
r
ac
y
o
f
0
.
9
4
0
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f
u
r
th
er
d
em
o
n
s
tr
ates th
e
m
o
d
el
’
s
r
o
b
u
s
t c
lass
if
icatio
n
ab
ilit
y
.
T
ab
le
5
.
E
v
alu
atio
n
m
et
r
ics o
f
th
e
k
-
NN
m
o
d
el
M
e
t
r
i
c
V
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l
u
e
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c
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r
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y
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9
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4
4
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5
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o
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f
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t
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l
(
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.
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9
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g
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t
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l
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c
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c
y
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8
0
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T
h
e
s
tack
in
g
m
o
d
el
was
im
p
lem
en
ted
to
lev
e
r
ag
e
th
e
s
tr
en
g
th
s
o
f
m
u
ltip
le
b
ase
lear
n
er
s
:
GL
MN
et,
R
F,
an
d
XGBo
o
s
t.
T
h
e
b
ase
l
ea
r
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er
s
wer
e
tr
ain
ed
u
s
in
g
th
e
tr
ain
f
u
n
ctio
n
f
r
o
m
th
e
ca
r
e
t
p
ac
k
ag
e
with
1
0
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f
o
ld
cr
o
s
s
-
v
alid
atio
n
.
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ter
tr
ain
in
g
,
p
r
ed
ictio
n
s
f
r
o
m
th
e
b
ase
lear
n
er
s
wer
e
co
m
b
in
ed
in
to
a
n
ew
d
ataset,
wh
ich
s
er
v
ed
as in
p
u
t f
o
r
th
e
m
eta
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er
,
a
l
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is
tic
r
eg
r
ess
io
n
m
o
d
el
tr
ain
ed
with
1
0
-
f
o
ld
cr
o
s
s
-
v
alid
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n
.
T
h
e
s
tack
in
g
m
o
d
el
ac
h
iev
ed
an
im
p
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p
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f
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r
m
an
ce
o
n
th
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ataset,
with
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a
cc
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r
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f
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0
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%.
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h
e
c
o
n
f
u
s
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n
m
atr
ix
s
h
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wed
p
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f
ec
t
a
g
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m
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t
b
et
wee
n
p
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icted
a
n
d
ac
tu
al
cla
s
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es,
with
a
Kap
p
a
s
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o
f
1
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in
d
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d
if
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en
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m
en
t.
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e
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ig
h
s
en
s
itiv
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city
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tr
ate
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el
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r
r
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ctly
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s
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ess
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d
r
eliab
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lity
.
T
ab
le
6
p
r
esen
ts
th
e
o
t
h
er
ev
alu
atio
n
m
etr
ics
o
f
th
e
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tack
in
g
m
o
d
el.
Evaluation Warning : The document was created with Spire.PDF for Python.
I
n
d
o
n
esian
J
E
lec
E
n
g
&
C
o
m
p
Sci
I
SS
N:
2
5
0
2
-
4
7
52
E
n
h
a
n
ce
d
p
r
ed
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o
f c
h
r
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kid
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et
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ch
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(
S
a
mu
el
Jo
h
n
P
a
r
r
eñ
o
)
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ab
le
6
.
E
v
alu
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n
m
et
r
ics o
f
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e
s
tack
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g
m
o
d
el
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e
t
r
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c
V
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l
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e
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r
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y
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An
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ile
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ated
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ited
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52
In
d
o
n
esian
J
E
lec
E
n
g
&
C
o
m
p
Sci
,
Vo
l.
41
,
No
.
3
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Ma
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20
2
6
:
9
6
6
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976
974
T
h
e
r
esu
lts
d
em
o
n
s
tr
ate
th
e
e
f
f
icac
y
o
f
v
a
r
io
u
s
m
ac
h
in
e
le
ar
n
in
g
m
o
d
els
in
p
r
e
d
ictin
g
C
KD.
T
h
e
SVM
m
o
d
el
ac
h
iev
ed
p
er
f
e
ct
class
if
icatio
n
p
er
f
o
r
m
an
c
e,
r
ef
lectin
g
its
r
o
b
u
s
tn
ess
in
h
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d
lin
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h
ig
h
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d
im
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ata
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h
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t
m
o
d
el
also
p
er
f
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r
m
ed
ex
c
ellen
tly
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s
h
o
wca
s
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its
ab
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ter
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.
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o
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g
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th
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k
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el
h
a
d
s
lig
h
tly
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p
er
f
o
r
m
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ce
co
m
p
ar
ed
to
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d
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o
s
t,
it st
ill s
h
o
wed
s
tr
o
n
g
p
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ed
ictiv
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ca
p
ab
ilit
ies,
esp
ec
ially
in
id
en
tify
in
g
C
KD
-
p
o
s
itiv
e
ca
s
es.
T
h
e
s
tack
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g
m
o
d
el,
wh
ich
c
o
m
b
in
ed
th
e
p
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ed
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s
f
r
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m
GL
MN
et,
R
F,
an
d
XG
B
o
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t
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s
in
g
a
lo
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is
tic
r
eg
r
ess
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n
m
eta
-
lear
n
er
,
also
h
as
a
p
er
f
ec
t
p
er
f
o
r
m
an
ce
.
T
h
is
h
ig
h
lig
h
ts
th
e
s
tr
en
g
th
o
f
en
s
em
b
le
lear
n
in
g
tech
n
iq
u
es in
lev
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ag
in
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th
e
s
tr
en
g
th
s
o
f
m
u
ltip
le
m
o
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s
to
im
p
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v
e
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v
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all
p
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d
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p
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f
o
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m
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ce
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h
e
p
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f
ec
t
ac
cu
r
ac
y
,
s
en
s
itiv
ity
,
an
d
s
p
ec
if
icity
ac
h
iev
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b
y
th
e
s
tack
in
g
m
o
d
el
d
em
o
n
s
tr
ate
its
p
o
ten
tial
as
a
r
eliab
le
to
o
l f
o
r
C
KD
p
r
ed
ic
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.
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h
e
h
ig
h
ac
c
u
r
ac
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n
d
r
elia
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o
f
th
e
m
ac
h
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in
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m
o
d
els
,
p
ar
ticu
lar
ly
t
h
e
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tack
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m
o
d
el,
s
u
g
g
est
th
at
th
ese
tech
n
iq
u
es
ca
n
b
e
ef
f
ec
tiv
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y
u
s
ed
in
c
lin
ical
s
ettin
g
s
to
p
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ed
ict
C
K
D
o
n
s
et.
E
ar
ly
an
d
ac
cu
r
ate
p
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ed
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n
o
f
C
KD
ca
n
f
ac
ilit
ate
tim
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ter
v
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tio
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s
,
p
e
r
s
o
n
alize
d
tr
ea
tm
en
t
s
tr
ateg
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an
d
u
ltima
tely
im
p
r
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p
atien
t
o
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es.
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h
e
in
teg
r
atio
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o
f
m
a
ch
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lear
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in
g
m
o
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els
in
h
ea
lt
h
ca
r
e
ca
n
also
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elp
in
r
eso
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lim
ited
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s
b
y
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v
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is
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s
u
p
p
o
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t to
h
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r
e
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f
ess
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als.
Ou
r
f
in
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s
ar
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is
ten
t
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p
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s
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d
ies
th
at
h
av
e
d
em
o
n
s
tr
ate
d
th
e
p
o
ten
tial
o
f
m
ac
h
in
e
lear
n
in
g
i
n
C
KD
p
r
e
d
ictio
n
.
Fo
r
in
s
tan
ce
,
Po
lat
et
a
l.
[
1
3
]
an
d
Sin
g
h
et
a
l.
[
1
5
]
h
ig
h
li
g
h
ted
t
h
e
s
u
p
er
i
o
r
p
er
f
o
r
m
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ce
o
f
SVM
in
C
KD
class
if
icatio
n
.
Ho
wev
er
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r
SVM
m
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er
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r
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r
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m
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wh
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r
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r
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ly
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R
aih
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et
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l.
[
1
8
]
a
n
d
C
h
u
ah
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l.
[
1
9
]
d
em
o
n
s
tr
ated
th
e
e
f
f
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o
f
XGBo
o
s
t
in
p
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d
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r
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ely
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XGBo
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t
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R
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l.
[
1
8
]
p
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f
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r
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f
9
9
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r
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4
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Ho
wev
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r
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e
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m
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etter
th
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t
h
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h
u
ah
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l.
[
1
9
]
,
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s
e
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t
m
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el
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d
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%.
Ou
r
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y
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th
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f
in
d
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s
b
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c
o
r
p
o
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atin
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s
tack
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th
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co
m
b
i
n
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m
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ltip
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ase
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to
ac
h
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ig
h
er
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a
cc
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r
ac
y
.
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h
e
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f
icac
y
o
f
o
u
r
s
tack
in
g
m
o
d
el
is
s
im
ilar
to
th
e
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tu
d
y
b
y
B
h
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y
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m
i
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d
Dwa
r
ak
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h
[
3
0
]
,
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s
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o
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co
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r
o
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f
o
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m
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ce
.
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n
th
e
s
tu
d
y
,
a
h
y
b
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id
C
NN
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L
R
m
o
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tp
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m
o
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,
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ig
h
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ith
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o
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p
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if
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n
t
m
ac
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in
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tech
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iq
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p
ab
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T
h
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s
u
p
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m
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im
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ies
o
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Gu
p
ta
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Sh
a
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[
3
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wh
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elo
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a
h
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id
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NN
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STM
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h
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a
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e
h
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f
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f
th
e
m
o
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im
p
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o
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as
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o
m
e
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e
d
at
aset
u
s
ed
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s
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s
m
all
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d
m
ay
n
o
t
f
u
lly
ca
p
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r
e
th
e
d
iv
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s
ity
o
f
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p
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ts
.
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d
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ally
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e
m
o
d
els
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e
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ated
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n
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s
in
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d
th
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to
o
th
e
r
p
o
p
u
latio
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s
n
ee
d
s
to
b
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test
ed
.
Fu
tu
r
e
r
esear
ch
s
h
o
u
ld
i
n
co
r
p
o
r
ate
lar
g
er
,
m
o
r
e
d
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r
s
e
d
ata
s
ets
an
d
ex
p
lo
r
e
r
ea
l
-
wo
r
ld
clin
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d
ata
to
v
alid
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th
e
m
o
d
els
f
u
r
th
er
.
Ad
d
itio
n
a
lly
,
in
v
esti
g
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o
t
h
er
ad
v
a
n
ce
d
m
ac
h
in
e
lear
n
in
g
tech
n
iq
u
es
co
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ld
p
r
o
v
id
e
f
u
r
th
er
in
s
ig
h
ts
.
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n
te
g
r
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g
t
h
ese
m
o
d
els
in
to
cli
n
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p
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f
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s
u
p
p
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r
t
s
y
s
tem
s
f
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lth
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p
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p
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d
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clin
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s
to
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s
th
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im
p
ac
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n
C
KD
d
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n
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s
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d
m
an
ag
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n
t,
en
s
u
r
in
g
p
r
ac
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ap
p
licab
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an
d
ef
f
ec
tiv
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in
r
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-
wo
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ld
s
ce
n
ar
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s
.
4.
CO
NCLU
SI
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Gr
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th
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o
n
s
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h
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ac
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p
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class
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ely
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ase
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u
tp
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f
o
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m
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all
in
d
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id
u
al
m
o
d
els,
ac
h
iev
in
g
p
er
f
ec
t
ac
cu
r
ac
y
an
d
d
e
m
o
n
s
tr
atin
g
its
p
o
t
en
tial
as
a
r
eliab
le
to
o
l
f
o
r
C
KD
p
r
ed
ictio
n
.
T
h
e
s
e
r
esu
lts
em
p
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th
e
s
ig
n
if
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t
p
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tial
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f
m
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n
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m
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h
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p
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ly
d
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d
d
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o
s
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f
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Acc
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an
d
tim
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p
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lead
to
im
p
r
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s
an
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p
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s
tr
ateg
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T
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in
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o
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i
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e
lear
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in
g
m
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in
h
ea
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ca
n
also
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eso
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r
ce
-
lim
ited
s
e
ttin
g
s
b
y
p
r
o
v
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Evaluation Warning : The document was created with Spire.PDF for Python.
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atasets
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ch
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elativ
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iv
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ity
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wh
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ay
af
f
ec
t
th
e
g
en
er
aliza
b
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o
f
t
h
e
f
in
d
in
g
s
.
Fu
tu
r
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s
h
o
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th
e
in
clu
s
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o
f
m
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p
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s
iv
e
clin
ical
f
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ata
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ir
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es f
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r
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p
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g
lo
b
ally
.
RE
F
E
R
E
NC
E
S
[
1]
A
.
S
a
n
y
a
o
l
u
e
t
a
l
.
,
“
E
p
i
d
e
m
i
o
l
o
g
y
a
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d
m
a
n
a
g
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t
o
f
c
h
r
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c
r
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n
a
l
f
a
i
l
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e
:
a
g
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o
b
a
l
p
u
b
l
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c
h
e
a
l
t
h
p
r
o
b
l
e
m,
”
Bi
o
s
t
a
t
i
st
i
c
s
a
n
d
Ep
i
d
e
m
i
o
l
o
g
y
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n
t
e
r
n
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t
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o
n
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l
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o
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l
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o
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p
.
1
1
–
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6
,
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0
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8
.
[
2]
J.
C
.
L
v
a
n
d
L.
X
.
Z
h
a
n
g
,
“
P
r
e
v
a
l
e
n
c
e
a
n
d
d
i
se
a
se
b
u
r
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e
n
o
f
c
h
r
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n
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c
k
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d
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e
y
d
i
se
a
se
,”
Ad
v
a
n
c
e
s
i
n
Ex
p
e
r
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m
e
n
t
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l
M
e
d
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c
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n
e
a
n
d
Bi
o
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o
g
y
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v
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l
.
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p
p
.
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–
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5
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0
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,
d
o
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-
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[
3]
A
.
F
r
a
n
c
i
s
e
t
a
l
.
,
“
C
h
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o
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c
k
i
d
n
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y
d
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sea
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n
d
t
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:
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se
n
s
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s,
”
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a
t
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r
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R
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v
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e
w
s
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p
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[
4]
D
.
R
u
c
k
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r
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t
a
l
.
,
“
Q
u
a
l
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t
y
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f
c
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r
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m
o
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t
a
l
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t
y
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r
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w
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n
c
h
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i
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k
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s
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se
p
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t
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n
t
s
l
i
v
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n
g
i
n
r
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m
o
t
e
a
r
e
a
s
,
”
K
i
d
n
e
y
I
n
t
e
r
n
a
t
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o
n
a
l
,
v
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l
.
7
9
,
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o
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,
p
p
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1
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.
2
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3
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.
[
5]
F
.
F
.
K
a
z
e
,
D
.
T
.
M
e
t
o
,
M
.
P
.
H
a
l
l
e
,
J
.
N
g
o
g
a
n
g
,
a
n
d
A
.
P
.
K
e
n
g
n
e
,
“
P
r
e
v
a
l
e
n
c
e
a
n
d
d
e
t
e
r
m
i
n
a
n
t
s
o
f
c
h
r
o
n
i
c
k
i
d
n
e
y
d
i
s
e
a
s
e
i
n
r
u
r
a
l
a
n
d
u
r
b
a
n
C
a
m
e
r
o
o
n
i
a
n
s
:
A
c
r
o
ss
-
s
e
c
t
i
o
n
a
l
s
t
u
d
y
,
”
B
M
C
N
e
p
h
r
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l
o
g
y
,
v
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.
1
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[
6]
E.
S
.
W
i
j
e
w
i
c
k
r
a
m
a
e
t
a
l
.
,
“
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r
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v
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l
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e
o
f
C
K
D
o
f
u
n
k
n
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w
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t
i
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s
k
f
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c
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p
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La
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k
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Re
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[
7]
S
.
C
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a
,
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T
h
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s
,”
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n
t
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r
n
a
t
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a
l
J
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rn
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l
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M
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