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ise
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m
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K
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w
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s
:
Ar
tific
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tellig
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Au
to
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C
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CC B
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C
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Dep
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E
n
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Vel
T
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R
an
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ar
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Dr
.
Sag
u
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th
ala
R
&
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I
n
s
titu
te
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Scien
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an
d
T
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o
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C
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en
n
ai,
I
n
d
ia
E
m
ail: je
n
cy
r
u
b
ia@
g
m
ail.
co
m
1.
I
NT
RO
D
UCT
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Patien
ts
wh
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av
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r
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A
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tr
ea
tm
en
ts
lik
e
h
em
o
d
ialy
s
is
,
p
er
ito
n
ea
l
d
ialy
s
is
,
o
r
k
id
n
ey
tr
an
s
p
lan
tatio
n
,
ea
r
ly
d
etec
tio
n
o
f
C
KD
is
ess
en
tia
l
f
o
r
m
in
im
izin
g
th
e
b
u
r
d
en
o
n
m
ed
ical
r
eso
u
r
ce
s
.
B
lo
o
d
test
i
n
g
,
wh
ich
in
v
o
l
v
es
d
eter
m
in
i
n
g
th
e
b
lo
o
d
u
r
ea
n
itro
g
en
(
B
UN)
in
d
e
x
a
n
d
c
r
e
atin
in
e
lev
els,
is
f
r
e
q
u
en
tly
u
s
ed
to
m
ak
e
an
ea
r
ly
d
iag
n
o
s
is
o
f
C
KD.
Ar
tific
ial
in
tellig
en
ce
(
AI
)
s
y
s
tem
s
h
av
e
r
ec
en
tly
b
ec
o
m
e
ef
f
ec
tiv
e
t
o
o
ls
f
o
r
aid
in
g
in
th
e
d
iag
n
o
s
is
o
f
d
is
ea
s
e
s
[
4
]
.
Par
ticu
lar
ly
with
m
ac
h
in
e
lea
r
n
i
n
g
,
ex
is
tin
g
d
ata
ca
n
b
e
in
t
ellig
en
tly
in
ter
p
r
eted
,
cr
ea
tin
g
u
s
ef
u
l
k
n
o
wled
g
e
an
d
en
h
an
ci
n
g
th
e
ef
f
ec
tiv
e
n
e
s
s
o
f
th
e
d
iag
n
o
s
tic
p
r
o
ce
d
u
r
e
.
T
h
is
m
eth
o
d
h
as
alr
ea
d
y
d
e
m
o
n
s
tr
ated
s
u
cc
ess
in
ev
alu
atin
g
th
e
h
ea
lth
o
f
th
e
h
u
m
a
n
b
o
d
y
,
l
o
o
k
in
g
at
r
is
k
f
ac
to
r
s
f
o
r
d
is
ea
s
es,
an
d
d
i
ag
n
o
s
in
g
n
u
m
er
o
u
s
ailm
en
ts
.
R
esear
ch
er
s
h
av
e
lo
o
k
ed
i
n
to
u
s
in
g
s
u
p
er
v
is
ed
alg
o
r
ith
m
s
to
id
e
n
tify
ch
r
o
n
ic
k
id
n
e
y
illn
ess
,
in
clu
d
in
g
r
an
d
o
m
f
o
r
est
,
f
u
zz
y
C
-
m
ea
n
s
(
FC
M)
,
Naiv
e
B
ay
es
(
NB
)
,
s
u
p
p
o
r
t
v
e
cto
r
m
ac
h
i
n
e
(
SVM)
,
g
r
a
d
ien
t
b
o
o
s
tin
g
(
GB
)
,
a
nd
r
eg
r
ess
io
n
b
ased
class
if
ier
s
.
Ma
ch
in
e
le
ar
n
in
g
is
u
s
ed
to
im
p
r
o
v
e
th
e
q
u
ality
o
f
m
ed
ical
d
ata,
d
ec
r
ea
s
e
h
o
s
p
ital a
d
m
is
s
io
n
s
,
an
d
e
v
en
tu
ally
lo
we
r
m
e
d
ical
co
s
ts
[
5
]
.
As
a
r
esu
lt,
co
m
p
ar
ed
to
m
o
r
e
tr
ad
itio
n
al
m
eth
o
d
s
,
th
ese
m
o
d
els
ar
e
ap
p
lied
m
o
r
e
f
r
e
q
u
en
tly
in
d
iag
n
o
s
tic
an
aly
tical
r
esear
ch
.
On
ly
p
r
o
p
e
r
tr
ea
tm
en
t
an
d
ea
r
ly
d
etec
tio
n
o
f
c
h
r
o
n
ic
d
is
ea
s
es
(
C
D)
ca
n
lo
wer
th
eir
m
o
r
tality
r
ates.
T
h
e
f
ea
t
u
r
e
ab
s
tr
ac
tio
n
an
d
ca
teco
r
iza
tio
n
s
tep
s
in
co
n
v
en
tio
n
al
m
a
ch
in
e
lear
n
in
g
r
ely
o
n
two
d
if
f
er
en
t
ap
p
r
o
ac
h
es,
w
h
ich
r
esu
lts
in
lab
o
r
io
u
s
ca
lcu
latio
n
s
.
T
h
e
co
n
v
en
tio
n
al
m
e
th
o
d
is
th
er
ef
o
r
e
n
o
lo
n
g
er
a
p
p
r
o
p
r
iate
f
o
r
r
ea
l
-
tim
e
d
iag
n
o
s
tic
ap
p
licatio
n
s
.
C
o
n
tr
ar
ily
,
n
e
u
r
al
n
etwo
r
k
s
(
NN)
f
r
eq
u
e
n
tly
o
u
tp
er
f
o
r
m
c
o
n
v
en
tio
n
al
m
ac
h
i
n
e
lear
n
i
n
g
m
eth
o
d
s
,
an
d
th
e
p
e
r
f
o
r
m
an
ce
o
f
t
h
e
r
e
s
o
u
r
ce
lear
n
in
g
ar
ch
itectu
r
e
c
an
b
e
f
u
r
th
er
im
p
r
o
v
e
d
.
NN
m
o
d
els
h
av
e
alr
ea
d
y
b
ee
n
u
s
ed
to
i
d
en
tify
k
id
n
e
y
d
is
ea
s
es,
alth
o
u
g
h
th
e
b
u
lk
o
f
C
KD
m
o
d
els
cu
r
r
e
n
tly
o
n
th
e
m
ar
k
et
h
av
e
p
o
o
r
class
if
icatio
n
ac
cu
r
aten
ess
.
As
a
o
u
tco
m
e,
th
is
wo
r
k
p
r
esen
ts
a
n
o
v
el
C
KD
m
o
d
el
t
h
at
u
s
es
d
ee
p
lear
n
in
g
to
m
ak
e
p
r
e
d
ictio
n
s
.
T
h
e
F
ig
u
r
e
1
s
h
o
ws th
e
d
if
f
e
r
en
t stag
es o
f
C
KD.
T
h
is
p
ap
er
’
s
p
r
i
m
ar
y
c
o
n
tr
ib
u
tio
n
s
ar
e:
−
T
h
e
d
etec
tio
n
a
n
d
d
ia
g
n
o
s
is
o
f
C
KD
h
av
e
b
ee
n
p
r
o
p
o
s
ed
u
s
in
g
d
ee
p
n
eu
r
al
n
etwo
r
k
s
(
DNN)
.
−
T
h
e
s
u
g
g
ested
m
o
d
el
’
s
co
m
p
u
tatio
n
al
p
r
ec
is
io
n
is
co
n
tr
ast
ed
with
th
at
o
f
o
th
e
r
p
u
b
lis
h
ed
class
if
icatio
n
tech
n
iq
u
es.
−
Ad
d
itio
n
ally
,
th
e
p
er
f
o
r
m
an
ce
is
ass
e
s
s
ed
u
s
in
g
a
v
ar
iety
o
f
p
er
f
o
r
m
an
ce
m
etr
ics.
T
h
e
b
r
ea
k
d
o
wn
o
f
th
e
p
ap
er
’
s
s
tr
u
ctu
r
e
is
as
f
o
llo
ws:
s
ec
tio
n
1
co
n
tai
n
s
an
in
tr
o
d
u
ctio
n
to
th
e
C
KD.
I
n
s
ec
tio
n
2
,
th
e
r
elate
d
wo
r
k
s
o
n
m
ac
h
in
e
lear
n
in
g
m
eth
o
d
s
in
th
e
C
KD
f
ield
a
r
e
p
r
esen
te
d
.
T
h
e
d
ee
p
n
eu
r
a
l
m
o
d
el
f
o
r
C
KD
ea
r
ly
d
etec
tio
n
is
p
r
esen
te
d
in
s
ec
tio
n
3
.
I
n
s
ec
tio
n
4
,
th
e
f
i
n
d
in
g
s
ar
e
c
o
v
er
ed
in
d
etail
alo
n
g
with
an
ex
p
lan
atio
n
.
Sectio
n
5
s
u
m
m
ar
izes th
e
r
esu
lts
an
d
ca
s
ts
an
ey
e
to
war
d
s
th
e
f
u
t
u
r
e.
Fig
u
r
e
1
.
Dif
f
e
r
en
t stag
es o
f
C
KD
2.
L
I
T
RA
T
UR
E
RE
V
I
E
W
E
f
f
ec
tiv
e
m
a
n
ag
em
en
t
an
d
tr
e
atm
en
t
o
f
C
NR
ar
e
th
o
u
g
h
t
t
o
d
ep
e
n
d
g
r
ea
tly
o
n
ea
r
ly
d
et
ec
tio
n
an
d
ch
ar
ac
ter
izatio
n
o
f
th
e
co
n
d
itio
n
.
T
h
is
is
d
o
n
e
b
y
u
s
in
g
e
f
f
ec
tiv
e
d
ata
m
in
in
g
tech
n
iq
u
es
to
u
n
ea
r
th
an
d
ex
tr
ac
t
h
id
d
en
in
f
o
r
m
atio
n
f
r
o
m
clin
ical
a
n
d
lab
o
r
at
o
r
y
p
a
tien
t
r
ec
o
r
d
s
.
T
h
e
in
f
o
r
m
atio
n
th
at
e
m
er
g
es
m
ay
h
elp
p
h
y
s
ician
s
co
r
r
ec
tly
class
if
y
th
e
s
tag
es
o
f
illn
ess
s
ev
er
ity
.
E
lk
h
o
ly
et
a
l
.
[
6
]
s
u
g
g
ested
th
e
m
o
d
if
ied
d
ee
p
b
elief
n
etwo
r
k
ar
c
h
itectu
r
e
f
o
r
C
KD
p
r
ed
ictio
n
,
s
in
ce
to
im
p
r
o
v
e
clin
ical
an
aly
s
is
o
f
th
e
C
KD
p
atien
t
’
s
r
ep
o
r
t.
T
h
e
p
lan
n
e
d
is
o
n
e
o
f
th
e
in
tellig
en
t
an
d
ad
v
an
ce
d
m
o
d
els,
wh
ich
u
s
e
th
e
So
f
tM
ax
as
th
e
ac
tiv
atio
n
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.
3
6
,
No
.
3
,
Dec
em
b
er
20
24
:
1
7
98
-
1
8
0
6
1800
f
u
n
ctio
n
at
class
if
icatio
n
lay
er
an
d
ca
teg
o
r
ical
cr
o
s
s
-
en
tr
o
p
y
as
er
r
o
r
f
u
n
ctio
n
.
T
h
e
au
th
o
r
p
r
o
d
u
ce
d
9
8
.
5
%
ac
cu
r
ac
y
.
T
h
is
m
o
d
el
g
a
v
e
b
e
s
t
ac
cu
r
ac
y
r
a
te
th
an
t
h
e
p
r
e
v
io
u
s
m
o
d
els.
J
o
h
ar
i
et
a
l
.
[
7
]
in
tr
o
d
u
ce
d
a
n
o
v
el
p
r
ed
ictio
n
al
g
o
r
ith
m
s
two
-
class
d
ec
is
io
n
f
o
r
est
a
n
d
NN
to
g
u
ess
th
e
p
o
s
s
ib
ilit
ies
o
f
af
f
ec
te
d
p
er
s
o
n
wh
o
h
u
r
ts
f
r
o
m
ch
r
o
n
ic
ca
s
e
o
f
k
id
n
e
y
d
is
ea
s
es.
Am
o
n
g
th
ese
two
m
o
d
els,
Dec
is
io
n
Fo
r
est
g
av
e
th
e
b
est
p
er
f
o
r
m
a
n
ce
th
an
th
e
two
-
f
o
l
d
NN.
T
h
is
p
ap
er
also
co
m
p
a
r
ed
t
h
e
p
r
o
p
o
s
ed
wo
r
k
with
s
o
m
e
o
t
h
er
m
ac
h
in
e
lear
n
in
g
alg
o
r
ith
m
s
s
u
ch
as
k
-
n
ea
r
est n
eig
h
b
o
r
(
KNN
)
an
d
SVM.
Gh
af
ar
et
a
l.
[
8
]
p
la
n
n
ed
t
o
u
s
e
SVM
alg
o
r
ith
m
f
o
r
C
KD
p
r
ed
ictio
n
.
T
h
e
an
al
y
s
is
o
f
th
e
p
lan
n
e
d
m
o
d
el
is
d
o
n
e
o
n
th
e
I
B
M
SP
SS
s
tati
s
tic.
I
n
th
is
r
e
s
ea
r
ch
,
th
e
ad
v
an
ce
d
f
ea
tu
r
e
s
elec
tio
n
im
p
r
o
v
e
d
th
e
ac
cu
r
ac
y
r
ate
to
9
3
.
5
%,
wh
ich
m
o
d
el
h
o
ld
th
e
5
0
%
v
alid
at
io
n
.
T
h
is
m
o
d
el
p
r
ed
icts
th
e
s
tag
es
o
f
th
e
R
u
b
ia
an
d
L
in
c
y
[
9
]
u
s
ed
two
v
a
r
io
u
s
s
h
ap
e
NN,
wh
ich
is
c
o
m
b
i
n
ed
with
PC
A
to
d
etec
t
th
e
C
KD.
Gen
er
ally
ea
r
ly
d
iag
n
o
s
is
o
f
C
KD
i
s
d
if
f
icu
lt.
So
th
e
au
th
o
r
s
u
g
g
ested
th
is
au
to
m
ati
c
d
etec
tio
n
m
eth
o
d
f
o
r
ea
r
ly
d
iag
n
o
s
is
o
f
C
KD.
Als
o
,
th
is
p
ap
er
u
s
ed
s
o
m
e
o
th
er
m
ac
h
in
e
lear
n
in
g
alg
o
r
ith
m
s
s
u
ch
as
clu
s
ter
in
g
alg
o
r
ith
m
,
Naïv
e
B
ay
es
an
d
d
ec
is
io
n
tr
ee
alg
o
r
ith
m
s
.
Yash
f
i
et
a
l
.
[
1
0
]
s
u
g
g
ested
r
an
d
o
m
f
o
r
est
an
d
ar
tifi
cial
n
eu
r
al
n
etwo
r
k
(
ANN
)
tech
n
iq
u
e
f
o
r
C
KD
d
etec
ti
o
n
o
n
th
e
UC
I
Ma
ch
i
n
e
lear
n
in
g
R
ep
o
s
ito
r
y
.
T
h
e
r
esear
ch
er
c
o
n
s
id
er
ed
4
5
5
p
atien
t
’
s
d
etails
f
o
r
th
e
tr
ain
in
g
p
u
r
p
o
s
e.
T
h
e
p
lan
n
ed
m
o
d
el
g
en
er
ate
d
9
7
.
1
2
%
an
d
9
4
.
5
%
ac
cu
r
ac
y
f
o
r
r
an
d
o
m
f
o
r
est
an
d
ANN
tech
n
iq
u
e
r
esp
ec
tiv
ely
.
B
h
o
wm
ick
an
d
Xa
v
ier
[
1
1
]
r
ec
o
m
m
en
d
ed
KNN
b
ased
al
g
o
r
ith
m
f
o
r
C
KD
p
r
e
d
ictio
n
with
UC
I
d
ataset.
T
h
is
m
o
d
el
is
c
o
m
p
ar
ed
with
d
ec
is
io
n
t
r
ee
a
n
d
ad
b
o
o
s
t
alg
o
r
it
h
m
s
.
T
h
e
p
lan
n
ed
KNN
b
ased
m
o
d
el
p
r
o
d
u
ce
th
e
9
8
.
2
%
ac
cu
r
ac
y
r
ate.
R
ajesh
war
i
an
d
Yo
g
is
h
[
1
2
]
a
d
v
is
ed
a
n
o
v
el
NB
alg
o
r
ith
m
f
o
r
C
KD
d
ete
ctio
n
.
T
h
e
s
y
s
tem
co
m
p
ar
ed
with
o
th
er
tec
h
n
o
l
o
g
ies
lik
e
SVM,
r
an
d
o
m
f
o
r
est,
an
d
d
ec
is
io
n
tr
ee
.
Fro
m
th
e
ex
p
er
im
en
tal
r
esu
lt,
th
e
p
r
o
p
o
s
ed
m
o
d
el
ac
h
iev
ed
b
est
ac
cu
r
ac
y
r
ate
with
9
8
.
7
5
%.
I
t
h
elp
s
th
e
d
o
cto
r
f
o
r
au
to
m
atic
k
id
n
ey
d
is
ea
s
e
d
et
ec
tio
n
.
I
s
lam
an
d
R
ip
o
n
[
1
3
]
u
s
ed
A
d
aBo
o
s
t
an
d
L
o
g
it
B
o
o
s
t
f
o
r
C
KD.
T
h
e
m
ain
o
b
jectiv
es
o
f
t
h
is
r
esear
ch
is
v
er
if
y
in
g
th
e
o
u
tco
m
e
o
f
th
e
b
o
o
s
tin
g
alg
o
r
ith
m
a
n
d
f
in
d
in
g
th
e
r
elatio
n
b
etwe
en
attr
ib
u
tes
o
f
th
e
C
KD
d
etec
tio
n
.
T
h
e
au
t
h
o
r
c
o
m
b
in
ed
th
e
d
ata
m
in
in
g
tec
h
n
i
q
u
es
with
b
o
o
s
tin
g
alg
o
r
ith
m
f
o
r
b
est
r
esu
lt.
Vis
h
wan
ath
a
et
a
l
.
[
1
4
]
d
is
cu
s
s
ed
th
e
p
r
o
b
lem
s
r
elate
d
to
C
KD.
Dam
o
d
ar
a
an
d
T
h
ak
u
r
[
1
5
]
co
n
s
id
er
e
d
ad
a
p
tiv
e
f
u
zz
y
lo
g
ic
f
o
r
C
KD.
T
h
is
an
aly
s
is
th
e
s
tag
es
o
f
th
e
C
KD.
T
ab
le
1
m
en
tio
n
ed
t
h
e
v
ar
io
u
s
wo
r
k
s
r
elate
d
to
C
KD
p
r
ed
ictio
n
.
T
ab
le
1
.
L
iter
atu
r
e
an
aly
s
is
o
f
th
e
d
iag
n
o
s
is
o
f
ch
r
o
n
ic
d
is
ea
s
es
R
e
f
e
r
e
n
c
e
M
o
d
e
l
A
c
c
u
r
a
c
y
[
1
6
]
N
e
u
r
a
l
n
e
t
w
o
r
k
c
l
a
ss
i
f
i
e
r
9
5
%
[
1
7
]
Lo
g
i
s
t
i
c
r
e
g
r
e
ssi
o
n
,
d
e
c
i
s
i
o
n
t
r
e
e
9
3
%
[
1
8
]
X
G
B
o
o
st
9
5
.
8
%
[
1
9
]
S
V
M
9
5
.
8
%
[
2
0
]
S
o
f
t
M
a
x
r
e
g
r
e
ss
i
o
n
9
6
%
[
2
1
]
K
N
N
,
S
V
M
9
3
%
[
2
2
]
S
V
M
9
6
.
1
%
Vijay
alak
s
h
m
i
an
d
Su
m
alath
a
[
2
3
]
r
ec
o
m
m
e
n
d
ed
m
ac
h
in
e
alg
o
r
ith
m
s
f
o
r
C
KD
p
r
ed
ictio
n
.
T
h
e
p
ap
er
a
n
aly
ze
d
v
ar
io
u
s
r
esear
ch
p
ap
er
r
elate
d
to
th
e
C
KD.
C
R
D,
also
k
n
o
wn
as
C
KD,
is
a
d
is
o
r
d
er
in
wh
ich
th
e
k
id
n
ey
s
ar
e
u
n
ab
l
e
to
p
er
f
o
r
m
as
th
ey
s
h
o
u
ld
,
im
p
air
in
g
th
ei
r
ca
p
ac
ity
to
f
ilter
th
e
b
lo
o
d
in
a
m
an
n
er
s
im
ilar
to
t
h
at
o
f
h
ea
lth
y
k
id
n
ey
s
.
U
n
f
o
r
t
u
n
ately
,
wh
en
th
is
c
o
m
m
o
n
d
is
ea
s
e
r
ea
ch
es
its
ter
m
in
al
s
tag
es,
th
er
e
is
n
o
k
n
o
wn
cu
r
e,
leav
in
g
p
atien
ts
with
litt
le
ch
o
ice
b
u
t
to
r
ely
o
n
d
ialy
s
is
o
r
k
id
n
ey
tr
an
s
p
lan
tat
io
n
,
b
o
th
o
f
wh
i
ch
m
ay
b
e
tim
e
-
co
n
s
u
m
i
n
g
an
d
ex
p
e
n
s
iv
e
p
r
o
ce
d
u
r
es.
Ma
ch
in
e
lear
n
in
g
ap
p
r
o
ac
h
es
ar
e
b
ei
n
g
u
s
ed
t
o
d
iag
n
o
s
e
th
e
co
n
d
itio
n
ea
r
ly
o
n
t
o
o
v
er
co
m
e
th
ese
is
s
u
es.
Fo
u
r
m
ac
h
in
e
lear
n
in
g
al
g
o
r
ith
m
s
-
SVM,
K
NN,
r
an
d
o
m
f
o
r
est
alg
o
r
ith
m
,
an
d
d
ec
is
io
n
tr
ee
a
lg
o
r
ith
m
-
a
r
e
u
s
ed
i
n
th
e
p
ap
e
r
[
2
4
]
cited
a
b
o
v
e.
T
h
e
UC
I
r
ep
o
s
ito
r
y
is
wh
er
e
th
e
d
atas
et
f
o
r
th
ese
alg
o
r
ith
m
s
’
tr
ain
in
g
is
tak
en
f
r
o
m
.
Ma
u
r
y
a
et
a
l
.
[
2
5
]
also
d
is
cu
s
s
ed
ab
o
u
t th
e
C
KD
p
r
ed
ictio
n
.
3.
P
RO
P
O
SE
D
M
O
D
E
L
I
t
is
cr
itical
to
g
et
a
k
id
n
e
y
d
is
ea
s
e
d
iag
n
o
s
is
ea
r
ly
o
n
s
o
th
at
p
atien
ts
ca
n
av
o
id
s
ig
n
if
ican
t
p
r
o
b
lem
s
.
I
t
tak
es
ca
r
ef
u
l
r
es
ea
r
ch
to
u
n
d
er
s
tan
d
th
e
elem
en
ts
th
at
co
n
tr
ib
u
te
to
r
en
al
d
is
o
r
d
er
s
.
T
o
d
a
y
’
s
h
ea
lth
ca
r
e
in
d
u
s
tr
y
r
elies
h
ea
v
ily
o
n
AI
to
e
x
tr
ac
t
in
s
ig
h
tf
u
l
in
f
o
r
m
atio
n
f
r
o
m
h
ea
lth
d
ata
wh
ile
m
ain
tain
in
g
p
r
iv
ac
y
.
I
n
h
ea
lth
ca
r
e
o
r
g
an
i
s
atio
n
s
,
d
ee
p
lear
n
in
g
an
d
n
o
tab
ly
class
if
icatio
n
p
lay
s
a
cr
u
cial
r
o
le.
E
ac
h
d
ataset
’
s
tar
g
et
ca
teg
o
r
y
is
d
e
ter
m
in
ed
th
r
o
u
g
h
th
e
class
if
icatio
n
p
r
o
ce
s
s
,
wh
ich
also
p
r
o
v
id
es
a
u
s
ef
u
l
to
o
l
f
o
r
m
ak
in
g
class
if
ier
s
u
g
g
esti
o
n
s
th
at
m
ay
s
u
cc
ess
f
u
lly
h
an
d
le
n
o
v
el
ca
s
es.
T
h
e
u
s
e
o
f
d
ee
p
lear
n
in
g
tech
n
iq
u
es
to
au
to
m
ate
th
e
ex
t
r
ac
tio
n
a
n
d
i
n
ter
p
r
etatio
n
o
f
f
u
n
ctio
n
s
,
p
r
o
d
u
cin
g
h
ig
h
-
p
e
r
f
o
r
m
in
g
m
o
d
els,
h
as
Evaluation Warning : The document was created with Spire.PDF for Python.
I
n
d
o
n
esian
J
E
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E
n
g
&
C
o
m
p
Sci
I
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N:
2
5
0
2
-
4
7
52
A
u
to
ma
tic
kid
n
ey
d
is
ea
s
e
p
r
ed
ictio
n
u
s
in
g
d
ee
p
lea
r
n
in
g
tech
n
iq
u
es
(
Je
n
cy
R
u
b
i
a
)
1801
r
ec
en
tly
in
cr
ea
s
ed
in
th
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f
ield
o
f
k
id
n
ey
d
is
ea
s
e.
A
s
u
b
ty
p
e
o
f
m
ac
h
i
n
e
lear
n
i
n
g
ca
lled
“
d
ee
p
lear
n
in
g
”
p
lace
s
a
s
tr
o
n
g
em
p
h
asis
o
n
lear
n
in
g
an
d
b
u
il
d
in
g
s
ev
er
al
h
ier
ar
ch
i
ca
l la
y
er
s
.
A
DNN
is
a
k
in
d
o
f
n
eu
r
al
n
e
two
r
k
th
at
h
as
n
u
m
e
r
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u
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lay
e
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s
o
f
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o
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es,
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n
ab
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it
to
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is
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p
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s
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ata
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v
a
r
io
u
s
s
tag
es
b
etwe
en
in
p
u
t
a
n
d
o
u
tp
u
t.
I
n
th
e
ar
ea
o
f
k
id
n
ey
d
is
ea
s
e
d
iag
n
o
s
is
an
d
an
aly
s
is
,
d
ee
p
lear
n
i
n
g
m
o
d
e
ls
ca
n
p
r
o
d
u
ce
o
u
ts
tan
d
in
g
o
u
tco
m
es
b
ec
au
s
e
to
th
is
h
ier
ar
ch
ical
ap
p
r
o
ac
h
.
Dee
p
lear
n
in
g
d
o
es,
in
f
ac
t,
r
e
ly
o
n
c
o
m
p
u
tatio
n
al
m
o
d
els
w
ith
n
u
m
er
o
u
s
lay
er
s
th
a
t
ar
e
ca
p
ab
le
o
f
ca
p
tu
r
in
g
d
ata
s
tr
u
ctu
r
es
at
d
if
f
er
en
t
d
e
g
r
ee
s
o
f
ab
s
tr
ac
tio
n
.
L
ea
r
n
in
g
f
ea
tu
r
es
i
n
h
ier
ar
ch
ical
s
tr
u
ctu
r
es,
wh
er
e
h
ig
h
er
-
lev
el
ch
ar
ac
ter
is
tics
ar
e
p
r
o
d
u
ce
d
b
y
m
ix
in
g
lo
wer
-
lev
el
f
ea
tu
r
es,
is
th
e
m
ain
g
o
al
o
f
d
ee
p
lear
n
in
g
s
y
s
tem
s
.
C
o
n
v
o
lu
tio
n
al
n
eu
r
al
n
etwo
r
k
s
(
C
NN)
will
b
e
u
s
ed
in
th
e
co
n
tex
t
o
f
th
e
s
u
g
g
ested
m
o
d
el
f
o
r
p
r
ed
ictin
g
C
KD
.
C
NNs
h
av
e
d
em
o
n
s
tr
ated
p
r
o
m
is
in
g
o
u
tco
m
es
in
m
ed
ical
d
iag
n
o
s
tics
,
in
clu
d
in
g
th
e
p
r
ed
ictio
n
o
f
C
KD,
an
d
ar
e
p
ar
ticu
lar
ly
well
s
u
ited
f
o
r
p
ictu
r
e
an
d
p
atter
n
r
ec
o
g
n
itio
n
task
s
.
T
h
e
wo
r
k
f
lo
w
o
f
th
e
p
r
o
p
o
s
ed
m
o
d
el
s
h
o
wn
in
Fig
u
r
e
2
.
Data
g
ath
er
in
g
is
th
e
f
ir
s
t
p
h
ase
in
o
u
r
r
esear
ch
tech
n
iq
u
e,
wh
ich
also
in
clu
d
es
p
r
e
-
p
r
o
ce
s
s
in
g
,
f
ea
tu
r
e
en
g
i
n
ee
r
in
g
b
ased
o
n
d
ee
p
lear
n
in
g
,
class
if
icatio
n
,
an
d
ev
alu
atio
n
.
O
u
r
p
r
o
p
o
s
ed
m
o
d
el
is
b
ased
o
n
th
e
UC
I
Data
s
et,
wh
ich
co
n
s
i
s
ts
o
f
2
5
attr
ib
u
tes,
in
clu
d
in
g
1
4
n
o
m
in
al,
1
1
n
u
m
er
ical,
a
n
d
1
class
attr
ib
u
te.
T
h
ese
attr
ib
u
tes
co
llectiv
ely
r
ep
r
esen
t
d
ata
f
r
o
m
4
0
0
p
atien
ts
,
in
clu
d
in
g
2
5
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ca
s
es
o
f
C
K
D
an
d
1
5
0
ca
s
es
o
f
non
-
C
KD.
T
h
e
n
ex
t
p
h
ase,
k
n
o
wn
as
p
r
e
-
p
r
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s
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in
g
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in
cl
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es
s
o
lv
in
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th
e
m
is
s
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g
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alu
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s
u
e.
T
h
is
wo
r
k
esti
m
ate
m
is
s
in
g
d
ata
an
d
em
p
lo
y
s
tr
ateg
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to
g
et
r
id
o
f
n
o
is
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s
u
ch
as
o
u
tlier
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.
Ad
d
itio
n
ally
,
as
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ar
t
o
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th
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p
r
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-
p
r
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s
s
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g
s
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is
p
r
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o
r
ith
m
u
n
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ataset
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d
in
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a
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le
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u
r
e
2
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k
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wo
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k
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2
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Data
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h
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p
r
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is
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lly
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e.
Me
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tat
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n
,
a
s
tatis
tical
ap
p
r
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ac
h
,
is
u
s
ed
to
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.
3
6
,
No
.
3
,
Dec
em
b
er
20
24
:
1
7
98
-
1
8
0
6
1802
esti
m
ate
th
e
m
is
s
in
g
v
al
u
es
to
ad
d
r
ess
th
e
a
b
s
en
ce
o
f
n
u
m
er
ical
f
ea
tu
r
es.
On
th
e
o
th
er
h
a
n
d
,
th
e
m
o
d
e
ap
p
r
o
ac
h
is
u
s
ed
to
s
u
b
s
titu
te
th
e
m
is
s
in
g
v
alu
es
f
o
r
n
o
m
in
al
f
ea
tu
r
es.
Giv
en
th
at
m
o
s
t
d
ee
p
lear
n
in
g
alg
o
r
ith
m
s
o
n
ly
ac
ce
p
t
n
u
m
er
i
c
in
p
u
ts
,
ca
teg
o
r
ical
v
alu
es
n
ee
d
to
b
e
en
co
d
e
d
in
to
n
u
m
e
r
ical
r
ep
r
esen
tatio
n
s
.
T
h
e
b
in
ar
y
n
u
m
b
er
s
“
0
”
an
d
“
1
”
ar
e
u
s
ed
to
in
d
icate
ca
teg
o
r
y
tr
aits
lik
e
“
no
”
an
d
“
y
es
”
.
T
o
en
s
u
r
e
th
at
n
o
o
n
e
v
ar
iab
le
d
o
m
in
ates
th
e
o
th
er
s
,
d
ata
tr
an
s
f
o
r
m
atio
n
en
ta
ils
tr
an
s
f
o
r
m
in
g
n
u
m
b
er
s
to
th
e
s
am
e
s
ca
le.
T
h
is
s
to
p
lear
n
in
g
alg
o
r
ith
m
s
f
r
o
m
in
ter
p
r
etin
g
lar
g
er
v
alu
es
-
r
eg
ar
d
less
o
f
t
h
eir
r
ea
l
weig
h
t
o
r
s
ig
n
if
ica
n
ce
-
as
g
r
ea
ter
an
d
s
m
aller
v
alu
es
-
as
lo
wer
.
Data
tr
an
s
f
o
r
m
atio
n
s
ee
k
s
to
alter
d
ataset
v
alu
es
i
n
o
r
d
er
to
im
p
r
o
v
e
th
eir
s
u
itab
ilit
y
f
o
r
s
u
b
s
eq
u
en
t
p
r
o
ce
s
s
in
g
.
A
d
ata
n
o
r
m
alis
atio
n
tech
n
iq
u
e
is
u
s
ed
in
th
i
s
s
tu
d
y
to
im
p
r
o
v
e
th
e
p
r
ec
is
io
n
o
f
m
ac
h
in
e
lea
r
n
in
g
m
o
d
els.
T
h
e
d
ata
a
r
e
s
ca
led
th
r
o
u
g
h
o
u
t
th
is
n
o
r
m
alis
atio
n
p
r
o
ce
d
u
r
e
b
etwe
en
-
1
an
d
+
1
.
As
a
r
esu
lt,
th
e
tr
an
s
lated
d
ata
will
b
e
s
tan
d
ar
d
is
ed
with
a
s
tan
d
ar
d
d
ev
iatio
n
o
f
1
a
n
d
a
m
ea
n
o
f
0
.
T
h
e
p
r
e
-
p
r
o
ce
s
s
ed
d
ata
is
th
en
s
p
lit
in
to
d
is
tin
ct
tr
ain
in
g
a
n
d
test
in
g
s
ets.
T
h
e
c
h
o
s
en
C
NN
m
o
d
el
is
s
u
b
s
eq
u
en
tly
tr
ain
ed
u
s
in
g
th
e
tr
ain
in
g
d
ata.
On
e
o
f
th
e
m
o
s
t w
ell
-
lik
ed
DNN
,
p
ar
ticu
lar
ly
in
th
e
co
n
tex
t o
f
co
m
p
u
ter
v
is
io
n
ap
p
licatio
n
s
,
is
C
NN
s
o
r
C
o
n
v
Nets.
T
h
ese
n
etwo
r
k
s
ar
e
cr
ea
te
d
p
r
im
a
r
ily
to
an
aly
s
e
v
is
u
a
l
im
ag
er
y
.
C
o
n
tr
ar
y
to
co
n
v
en
ti
o
n
al
NN
,
wh
ich
m
o
s
tly
d
e
p
en
d
o
n
m
atr
ix
m
u
ltip
licatio
n
s
,
C
NNs
u
s
e
a
u
n
iq
u
e
m
eth
o
d
ca
lled
“
co
n
v
o
lu
tio
n
”
.
C
o
n
v
o
lu
tio
n
in
m
ath
em
atics
is
a
m
ath
em
atica
l
o
p
er
atio
n
th
at
is
u
s
ed
to
t
wo
f
u
n
ctio
n
s
to
p
r
o
d
u
ce
a
th
ir
d
f
u
n
ctio
n
th
at
ex
p
lain
s
h
o
w
th
e
s
h
ap
e
o
f
o
n
e
f
u
n
ctio
n
is
ch
an
g
e
d
b
y
th
e
o
th
e
r
.
T
h
e
co
n
v
o
l
u
tio
n
p
r
o
ce
d
u
r
e
in
th
e
co
n
tex
t
o
f
C
NNs
en
tails
ap
p
l
y
in
g
a
f
ilter
(
s
o
m
etim
es
r
ef
er
r
ed
to
as
a
k
e
r
n
el)
o
v
er
th
e
i
n
p
u
t im
a
g
e
to
id
e
n
tif
y
d
if
f
er
e
n
t f
ea
tu
r
es a
n
d
p
atter
n
s
.
T
h
is
f
ilter
m
o
v
es
ac
r
o
s
s
th
e
f
u
ll
in
p
u
t
a
n
d
m
u
ltip
lies
elem
en
ts
-
by
-
elem
en
ts
with
th
e
m
atch
in
g
p
ix
els
o
f
th
e
im
ag
e
at
ea
ch
p
lace
.
T
h
e
r
esu
lts
ar
e
th
en
u
s
ed
to
cr
e
ate
f
ea
tu
r
e
m
ap
s
th
at
d
r
aw
att
en
tio
n
to
p
ar
ticu
la
r
p
atter
n
s
o
r
in
f
o
r
m
atio
n
in
th
e
im
ag
e.
C
NN
s
ar
e
p
ar
ticu
lar
l
y
ef
f
icien
t
in
task
s
lik
e
o
b
ject
r
ec
o
g
n
i
tio
n
,
im
a
g
e
class
if
icatio
n
,
an
d
o
b
ject
d
et
ec
tio
n
in
co
m
p
u
ter
v
is
io
n
a
p
p
licatio
n
s
b
ec
au
s
e
th
ey
ca
n
au
to
m
atica
lly
lear
n
s
ig
n
if
ican
t c
h
ar
ac
ter
is
tics
an
d
p
atter
n
s
f
r
o
m
th
e
in
p
u
t.
T
h
e
C
NN
ar
ch
itectu
r
e
is
s
h
o
wn
in
F
ig
u
r
e
3
.
Fig
u
r
e
3
.
C
NN
f
o
r
C
KD
p
r
e
d
ictio
n
4.
E
XP
E
R
I
M
E
N
T
A
L
SE
T
UP
AND
RE
SUL
T
ANA
L
YS
I
S
Pre
-
p
r
o
ce
s
s
in
g
,
s
y
s
tem
h
y
p
er
p
ar
am
eter
-
t
u
n
in
g
,
an
d
c
ca
teg
o
r
izatio
n
ar
e
t
h
e
th
r
e
e
p
r
im
ar
y
co
m
p
o
n
en
ts
o
f
th
e
s
u
g
g
ested
m
o
d
el.
Pre
p
r
o
ce
s
s
in
g
is
o
n
e
o
f
th
ese
s
tag
es
th
at
is
p
ar
ticu
la
r
ly
im
p
o
r
tan
t
s
in
ce
th
e
d
atab
ase
co
u
l
d
co
m
p
r
eh
e
n
d
n
o
is
e
an
d
r
ed
u
n
d
a
n
t stan
d
ar
d
s
.
T
h
e
m
an
ag
em
e
n
t o
f
ab
s
en
t
v
alu
es,
ca
teg
o
r
ical
b
ased
d
ata
en
co
d
in
g
,
d
ata
co
n
v
er
s
io
n
,
o
u
tlier
an
d
ex
tr
em
e
v
alu
e
r
em
o
v
al,
an
d
f
ea
tu
r
e
s
ele
ctio
n
ar
e
ju
s
t
a
f
ew
o
f
th
e
tech
n
iq
u
es
u
s
ed
in
th
is
p
h
ase.
T
h
e
C
KD
d
ata
s
et
is
p
r
e
-
p
r
o
ce
s
s
ed
an
d
th
en
d
iv
i
d
e
d
in
to
tr
ain
in
g
an
d
test
in
g
d
ata
s
ets
in
o
r
d
er
to
p
r
o
p
er
l
y
p
r
e
p
ar
e
it.
On
ly
a
f
ew
f
ea
tu
r
es
ar
e
k
ep
t
in
t
h
is
p
r
ep
ar
atio
n
,
an
d
t
h
e
tr
ea
ted
d
ata
is
th
en
s
en
t
f
o
r
a
d
d
itio
n
al
p
r
o
ce
s
s
in
g
.
A
C
NN
m
o
d
el
is
u
s
ed
f
o
r
m
o
d
el
tr
ain
i
n
g
.
Ho
we
v
er
,
th
er
e
ar
e
a
n
u
m
b
e
r
o
f
h
y
p
er
p
ar
am
eter
s
in
th
is
C
NN
m
o
d
el
th
at
n
ee
d
to
b
e
o
p
tim
is
ed
.
A
ti
m
e
-
co
n
s
u
m
in
g
an
d
d
if
f
icu
lt
ex
p
er
im
en
tal
p
r
o
ce
s
s
is
ch
o
o
s
in
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Evaluation Warning : The document was created with Spire.PDF for Python.
I
n
d
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n
esian
J
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g
&
C
o
m
p
Sci
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N:
2
5
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-
4
7
52
A
u
to
ma
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p
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o
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th
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ataset
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d
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Fig
u
r
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h
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d
ataset
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e
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ly
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ib
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ted
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u
ar
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ich
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u
r
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.
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e
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d
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th
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le
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.
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cisi
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ch
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ar
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ic
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atasets
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alse
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eg
ativ
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h
e
p
r
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ed
m
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el
ac
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ie
v
ed
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e
9
7
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ac
c
u
r
ac
y
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ate
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m
in
im
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m
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s
s
f
u
n
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.
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m
th
e
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lt
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aly
s
is
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o
m
e
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th
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o
m
e
o
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th
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m
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v
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d
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ed
b
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ell
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R
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lev
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th
at
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e
n
o
r
m
al
s
u
g
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est
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is
k
o
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th
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d
,
a
b
n
o
r
m
al
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r
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f
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s
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s
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p
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d
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h
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atter
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f
th
e
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s
ce
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e
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m
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l
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g
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r
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o
t.
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d
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s
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g
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eg
ates,
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at
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ical
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s
ce
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t
to
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s
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ll
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m
p
s
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n
d
ab
er
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t
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s
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n
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e
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g
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t
o
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as
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s
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th
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is
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o
f
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KD.
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KD
is
lin
k
ed
to
t
h
e
p
r
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ce
o
f
d
is
ea
s
es
s
u
ch
h
y
p
er
ten
s
io
n
(
HT
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iab
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(
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M)
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co
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n
ar
y
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ter
y
d
is
ea
s
e
(
C
AD)
,
s
wo
llen
f
ee
t
(
PE)
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an
ae
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a
n
d
p
o
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r
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p
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ev
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e
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y
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er
ten
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o
r
d
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g
t
o
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n
aly
s
is
p
r
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ted
ab
o
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e,
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th
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t
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a
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l
ls
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o
r
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r
y
a
r
t
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s
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l
a
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p
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n
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e
m
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r
t
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g
n
i
f
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n
t
l
y
r
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is
es
th
e
r
i
s
k
o
f
d
e
v
e
l
o
p
i
n
g
C
KD
.
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.
3
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3
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20
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Fig
u
r
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5
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ac
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d
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s
f
u
n
ctio
n
T
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4
.
Per
f
o
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aly
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is
P
r
e
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CO
NCLU
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O
N
T
h
e
m
ain
ca
u
s
es
o
f
C
KD,
in
clu
d
e
ex
ce
s
s
iv
e
b
l
o
o
d
p
r
ess
u
r
e
an
d
d
iab
etes.
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an
d
k
id
n
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in
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u
r
y
in
d
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ar
e
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s
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y
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ize
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d
is
o
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d
er
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at
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s
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eter
io
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atin
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u
n
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n
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er
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n
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o
r
tu
n
ately
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r
em
atu
r
e
m
o
r
t
ality
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m
o
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e
c
o
m
m
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n
in
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r
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o
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r
s
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to
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th
e
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r
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e
o
f
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r
ly
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iag
n
o
s
is
o
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t
h
e
d
if
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er
en
t
d
is
o
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er
s
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n
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t
ed
to
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is
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if
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icu
lt.
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esear
ch
er
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tes
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o
lu
tio
n
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t
o
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p
r
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b
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d
r
esto
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e
d
m
is
s
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g
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alu
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th
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atab
ase
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y
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er
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in
g
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elate
d
attr
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u
tes.
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h
ey
th
en
p
e
r
f
o
r
m
ed
n
u
m
er
o
u
s
attem
p
ts
to
ad
ju
s
t
th
e
NN
p
ar
am
eter
s
u
n
til
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ey
f
o
u
n
d
th
e
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ea
l
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alu
es.
Sig
n
if
ican
t
in
d
icato
r
s
f
o
r
p
r
ed
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g
C
KD
in
clu
d
e
k
ey
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ar
ac
ter
is
tics
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e
h
em
o
g
l
o
b
in
,
r
ed
b
lo
o
d
ce
ll
co
u
n
t,
s
p
ec
if
ic
g
r
a
v
ity
,
p
ac
k
ed
ce
ll
v
o
lu
m
e,
s
er
u
m
cr
ea
ti
n
in
e,
alb
u
m
in
,
an
d
h
y
p
er
ten
s
io
n
.
Dee
p
lear
n
in
g
m
o
d
els
wer
e
ap
p
lied
f
o
r
ca
t
eg
o
r
izatio
n
u
s
in
g
th
ese
c
h
o
s
en
f
ea
tu
r
es.
T
h
e
p
r
o
p
o
s
ed
d
ee
p
n
eu
r
al
m
o
d
el
d
em
o
n
s
tr
ated
g
o
o
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o
u
tco
m
e
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in
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KD
p
r
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n
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o
u
tp
e
r
f
o
r
m
in
g
o
th
e
r
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n
v
en
tio
n
al
m
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52
In
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24
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