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CC B
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C
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p
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A
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:
Hen
n
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T
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ter
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Facu
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Scien
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,
Un
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Ma
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s
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tial
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ar
ti
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ata
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s
h
elp
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s
s
ex
ten
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clin
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in
f
o
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m
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co
llected
th
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g
h
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tin
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m
ed
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ac
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ati
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elate
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p
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o
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h
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teg
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ates
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co
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tin
g
,
an
d
in
f
o
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m
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etr
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al,
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a
v
e
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llect
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s
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m
e
d
d
is
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d
ictio
n
in
th
e
f
ield
o
f
b
io
in
f
o
r
m
ati
cs
[
1
]
–
[
4
]
.
On
e
o
f
t
h
e
m
ajo
r
d
is
ea
s
es
ad
d
r
ess
ed
th
r
o
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g
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t
h
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tech
n
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lo
g
ical
ap
p
licatio
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is
h
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p
atitis
,
a
s
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s
liv
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d
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ca
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s
ed
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f
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em
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p
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atic
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s
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lead
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s
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ch
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h
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s
is
an
d
liv
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f
ailu
r
e
[
5
]
–
[
8
]
.
C
o
m
m
o
n
s
y
m
p
to
m
s
,
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clu
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n
til ad
v
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ce
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s
tag
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f
liv
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d
am
a
g
e
[
9
]
,
[
1
0
]
.
R
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en
t
r
ep
o
r
ts
f
r
o
m
th
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ld
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izatio
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(
W
HO)
,
h
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s
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3
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Evaluation Warning : The document was created with Spire.PDF for Python.
I
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&
C
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p
Sci
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N:
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4
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C
o
mp
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a
tive
a
n
a
lysi
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o
f lin
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r
r
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r
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Lig
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tGB
M fo
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h
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titi
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…
(
Hen
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Tu
h
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teru
)
431
with
th
e
h
ig
h
est
h
e
p
atitis
b
u
r
d
en
[
1
1
]
.
Pre
d
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m
o
r
tality
an
d
s
u
r
v
iv
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r
ates
in
h
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r
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ch
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ter
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Pre
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ly
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b
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tially
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d
th
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esig
n
o
f
a
p
p
r
o
p
r
iate
th
er
ap
e
u
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tr
ateg
ies
[
1
2
]
.
T
h
e
in
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p
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f
tech
n
iq
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ch
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tech
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(
SMOT
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o
is
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d
ata
[
1
3
]
–
[
1
5
]
.
Stu
d
ies
h
av
e
r
ep
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ted
ly
s
h
o
w
n
th
at
t
h
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d
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m
f
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ith
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co
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h
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h
ac
cu
r
ac
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in
p
r
ed
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g
h
ep
atitis
an
d
r
elate
d
liv
er
co
n
d
itio
n
s
ac
h
iev
in
g
o
v
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9
0
%
ac
cu
r
ac
y
in
m
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atasets
[
2
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,
[
1
3
]
,
[
1
6
]
.
Me
an
wh
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lig
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ad
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tG
B
M)
h
as
em
er
g
ed
as
a
c
o
m
p
etitiv
e
alter
n
ativ
e,
o
u
tp
er
f
o
r
m
in
g
o
th
e
r
m
o
d
els
o
n
b
e
n
ch
m
ar
k
d
atasets
s
u
ch
as
I
n
d
ian
liv
e
r
p
atien
t
d
ataset
(
I
L
PD
)
[
1
7
]
–
[
1
9
]
.
W
h
ile
lin
ea
r
r
eg
r
ess
io
n
is
f
r
eq
u
en
tly
u
s
ed
as
a
b
aselin
e
m
o
d
el
in
m
ed
ical
s
tu
d
ies,
it
ten
d
s
to
p
er
f
o
r
m
less
ac
cu
r
ately
th
an
n
o
n
-
lin
ea
r
m
o
d
els s
u
ch
as
r
an
d
o
m
f
o
r
est
o
r
b
o
o
s
tin
g
m
eth
o
d
s
[
4
]
,
[
2
0
]
.
T
h
is
s
tu
d
y
aim
s
to
p
r
ed
ict
s
u
r
v
iv
al
o
u
tco
m
es
in
h
ep
atitis
p
atien
ts
b
y
co
m
p
ar
in
g
th
e
p
e
r
f
o
r
m
an
ce
o
f
th
r
ee
wid
ely
u
s
ed
m
ac
h
in
e
lear
n
in
g
alg
o
r
ith
m
s
:
lin
ea
r
r
eg
r
ess
io
n
,
r
an
d
o
m
f
o
r
est
,
an
d
L
ig
h
t
GB
M.
T
h
e
d
ataset
in
clu
d
es p
u
b
lic
d
ata
f
r
o
m
th
e
UC
I
m
ac
h
in
e
lear
n
in
g
r
ep
o
s
it
o
r
y
an
d
r
ea
l
-
w
o
r
ld
m
ed
ical
r
e
co
r
d
s
co
llected
f
r
o
m
h
o
s
p
itals
in
Am
b
o
n
city
,
Ma
l
u
k
u
–
I
n
d
o
n
esia.
T
h
e
g
o
al
is
to
id
en
tify
th
e
alg
o
r
ith
m
with
th
e
h
ig
h
est
p
r
ed
ictio
n
ac
cu
r
ac
y
an
d
d
eter
m
in
e
t
h
e
m
o
s
t
in
f
lu
en
tial
f
ac
to
r
s
a
f
f
ec
tin
g
p
atien
t
s
u
r
v
i
v
al,
p
ar
ti
cu
lar
ly
with
in
th
e
I
n
d
o
n
esian
co
n
tex
t
[
2
1
]
–
[
2
3
]
.
2.
M
AT
E
R
I
AL
S AN
D
M
E
T
H
O
D
A
wid
e
r
an
g
e
o
f
r
esear
ch
h
as
b
ee
n
co
n
d
u
cted
to
p
r
ed
ict
m
o
r
tality
r
ates
an
d
s
u
r
v
iv
al
o
u
t
co
m
es
in
h
ep
atitis
ca
s
es
u
s
in
g
m
ac
h
in
e
lear
n
in
g
an
d
ar
tific
ial
in
tellig
en
ce
(
AI
)
ap
p
r
o
ac
h
es,
aim
in
g
to
o
p
tim
ize
m
o
d
el
p
er
f
o
r
m
an
ce
f
o
r
r
ea
l
-
wo
r
l
d
ap
p
licab
ilit
y
[
1
0
]
,
[
2
4
]
–
[
2
7
]
.
T
h
ese
s
tu
d
ies
ap
p
l
y
d
iv
e
r
s
e
m
ac
h
in
e
lear
n
in
g
m
eth
o
d
s
ac
r
o
s
s
m
u
ltip
le
h
ep
atitis
ty
p
es
(
A,
B
,
C
,
D,
E
)
,
u
s
in
g
s
tr
u
ctu
r
e
d
d
atasets
f
o
r
b
o
th
clin
ical
an
d
d
em
o
g
r
a
p
h
i
c
f
ea
tu
r
es
[
5
]
,
[
2
8
]
.
Sev
er
al
alg
o
r
ith
m
s
h
av
e
b
ee
n
d
e
p
lo
y
e
d
in
h
ep
atitis
r
esear
ch
,
in
clu
d
i
n
g
lo
g
is
tic
r
eg
r
ess
io
n
[
1
2
]
,
r
a
n
d
o
m
f
o
r
est
an
d
n
aïv
e
B
ay
es,
as
well
as
h
y
b
r
i
d
m
o
d
els
s
u
ch
as
im
p
r
o
v
ed
r
an
d
o
m
f
o
r
ests
with
s
u
p
p
o
r
t
v
ec
to
r
m
ac
h
in
es
(
SVMs)
[
2
9
]
.
So
m
e
s
t
u
d
ies
h
av
e
ex
ten
d
ed
i
n
to
life
ex
p
ec
tan
cy
p
r
ed
ictio
n
u
s
in
g
K
-
n
ea
r
est
n
ei
g
h
b
o
r
s
(
KNN)
,
en
h
an
ce
d
with
g
en
etic
alg
o
r
ith
m
s
,
d
em
o
n
s
tr
atin
g
th
e
ex
p
a
n
s
iv
e
ex
p
lo
r
atio
n
o
f
al
g
o
r
ith
m
ic
s
o
l
u
tio
n
s
in
th
is
d
o
m
ai
n
[
6
]
,
[
3
0
]
,
[
3
1
]
.
T
h
is
s
tu
d
y
ev
alu
ates
th
r
ee
c
o
m
m
o
n
l
y
u
s
ed
m
ac
h
in
e
-
lear
n
in
g
tech
n
iq
u
es.
T
h
ese
in
clu
d
e
a
lin
ea
r
-
b
ased
m
o
d
el
(
lin
ea
r
r
e
g
r
ess
io
n
)
,
a
tr
ee
-
e
n
s
em
b
le
m
eth
o
d
(
r
an
d
o
m
f
o
r
est
)
,
an
d
a
g
r
a
d
ien
t
-
b
o
o
s
tin
g
f
r
am
ewo
r
k
k
n
o
wn
as
L
ig
h
tGB
M.
T
h
e
n
o
v
elty
lies
in
e
v
alu
atin
g
th
eir
co
m
p
ar
ativ
e
p
er
f
o
r
m
a
n
ce
in
p
r
ed
ictin
g
h
e
p
atitis
p
atien
t
s
u
r
v
iv
al
o
u
tco
m
es
b
ased
o
n
r
ea
l
-
wo
r
ld
an
d
b
en
ch
m
ar
k
d
atasets
.
Un
d
er
s
tan
d
in
g
th
e
th
eo
r
etica
l
f
o
u
n
d
atio
n
s
an
d
s
tr
en
g
th
s
o
f
t
h
ese
m
eth
o
d
s
is
ess
en
tial f
o
r
ju
s
tify
in
g
th
eir
s
elec
tio
n
a
n
d
in
ter
p
r
etin
g
r
esu
lts
.
2
.
1
.
L
inea
r
re
g
re
s
s
io
n
L
in
ea
r
r
eg
r
ess
io
n
s
er
v
es
as
a
f
u
n
d
am
e
n
tal
s
tatis
tica
l
ap
p
r
o
ac
h
f
o
r
ex
p
lo
r
in
g
h
o
w
p
r
ed
icto
r
v
ar
iab
les
co
n
tr
ib
u
te
to
v
ar
iatio
n
s
in
a
n
o
u
tco
m
e
v
a
r
iab
le.
I
t
is
f
r
e
q
u
e
n
tly
u
s
ed
as
a
b
aselin
e
alg
o
r
ith
m
in
clin
ical
d
ata
m
o
d
elin
g
d
u
e
to
its
in
ter
p
r
eta
b
ilit
y
an
d
s
im
p
licity
[
2
7
]
,
[
3
2
]
.
Desp
ite
its
lim
itatio
n
s
in
h
an
d
lin
g
n
o
n
-
lin
ea
r
r
elatio
n
s
h
ip
s
,
its
in
clu
s
io
n
in
t
h
is
s
tu
d
y
allo
ws f
o
r
co
m
p
ar
is
o
n
ag
ain
s
t m
o
r
e
co
m
p
le
x
m
o
d
e
ls
.
2
.
2
.
Ra
nd
o
m
f
o
re
s
t
R
an
d
o
m
f
o
r
est
o
p
er
ates
b
y
ag
g
r
eg
atin
g
t
h
e
o
u
tp
u
ts
o
f
n
u
m
e
r
o
u
s
d
ec
is
io
n
tr
ee
s
,
en
ab
li
n
g
t
h
e
m
o
d
el
to
g
en
e
r
alize
ef
f
ec
tiv
el
y
ac
r
o
s
s
h
eter
o
g
en
eo
u
s
clin
ical
f
ea
tu
r
es.
I
t
r
ed
u
ce
s
v
ar
ian
ce
b
y
av
er
ag
i
n
g
r
esu
lts
ac
r
o
s
s
tr
ee
s
an
d
is
k
n
o
wn
f
o
r
i
ts
r
o
b
u
s
tn
ess
in
h
an
d
lin
g
n
o
is
y
d
ata,
im
b
alan
ce
d
class
es,
an
d
h
ig
h
-
d
im
en
s
io
n
al
d
atasets
[
2
]
,
[
1
3
]
,
[
1
6
]
,
[
3
3
]
.
R
an
d
o
m
f
o
r
est
h
as
co
n
s
is
ten
tly
d
em
o
n
s
tr
ated
s
tr
o
n
g
p
r
e
d
ictiv
e
p
er
f
o
r
m
a
n
ce
in
h
ep
atitis
an
d
liv
er
d
is
ea
s
e
-
r
ela
ted
s
tu
d
ies
[
1
7
]
,
[
1
8
]
,
[
3
4
]
.
2
.
3
.
L
ig
htG
B
M
L
ig
h
tGB
M
ap
p
lies
g
r
ad
ien
t
-
b
o
o
s
ted
d
ec
is
io
n
tr
ee
s
t
o
lear
n
co
m
p
lex
p
atter
n
s
e
f
f
icie
n
tly
,
o
f
f
er
i
n
g
f
aster
tr
ain
in
g
an
d
s
tr
o
n
g
p
e
r
f
o
r
m
a
n
ce
o
n
s
tr
u
ctu
r
ed
m
e
d
ical
d
ata.
I
t
is
d
esig
n
ed
to
b
e
d
is
tr
ib
u
ted
an
d
ef
f
icien
t,
with
f
aster
tr
ain
in
g
s
p
ee
d
,
lo
we
r
m
em
o
r
y
u
s
ag
e,
an
d
b
etter
ac
c
u
r
ac
y
co
m
p
ar
ed
to
tr
ad
itio
n
al
b
o
o
s
tin
g
m
eth
o
d
s
[
2
4
]
,
[
2
6
]
.
L
ig
h
t
GB
M
h
as
s
h
o
wn
ex
ce
llen
t
r
esu
lts
in
b
io
m
ed
ical
d
atas
ets,
in
clu
d
in
g
I
L
PD
an
d
h
e
p
atitis
d
ata,
an
d
is
ca
p
a
b
le
o
f
h
a
n
d
lin
g
lar
g
e
-
s
ca
le,
h
i
g
h
-
d
im
e
n
s
io
n
al
d
ata
ef
f
icien
tly
[
1
8
]
,
[
1
9
]
,
[
3
5
]
.
2
.
4
.
Cla
s
s
if
ica
t
io
n
perf
o
r
m
a
nce
m
ea
s
urem
ent
T
o
ev
al
u
ate
th
e
ef
f
ec
tiv
e
n
ess
o
f
class
if
icatio
n
m
o
d
els,
r
o
b
u
s
t
p
er
f
o
r
m
a
n
ce
m
et
r
ics
ar
e
ess
en
tial.
I
n
th
is
s
tu
d
y
,
a
co
n
f
u
s
io
n
m
at
r
ix
is
u
s
ed
to
m
ea
s
u
r
e
ac
cu
r
ac
y
,
p
r
ec
is
io
n
,
r
ec
all,
an
d
F1
-
s
co
r
e
b
y
co
m
p
ar
in
g
th
e
p
r
e
d
icted
class
if
icatio
n
s
with
th
e
ac
tu
al
o
u
t
co
m
es.
T
h
is
m
etr
ic
is
ef
f
ec
tiv
e
i
n
b
o
th
b
in
ar
y
an
d
m
u
lti
-
class
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
.
1
,
J
an
u
ar
y
20
2
6
:
430
-
4
3
8
432
class
if
icatio
n
p
r
o
b
lem
s
an
d
is
wid
ely
u
s
ed
in
m
ed
ical
p
r
ed
i
ctio
n
r
esear
ch
[
7
]
,
[
1
2
]
–
[
1
4
]
.
T
h
ese
p
er
f
o
r
m
an
ce
m
etr
ics ar
e
b
ased
o
n
f
o
u
r
d
if
f
e
r
en
t c
o
m
b
in
atio
n
s
o
f
p
r
ed
icted
an
d
ac
tu
al
v
alu
es.
Fu
r
t
h
er
ex
p
lan
atio
n
s
h
o
wn
in
th
e
T
ab
le
1
.
T
ab
le
1
.
C
o
n
f
u
s
io
n
m
atr
ix
P
r
e
d
i
c
t
i
o
n
r
e
su
l
t
R
e
a
l
s
i
t
u
a
t
i
o
n
P
o
si
t
i
v
e
c
l
a
ss
N
e
g
a
t
i
v
e
c
l
a
ss
P
o
si
t
i
v
e
c
l
a
ss
TP
FP
N
e
g
a
t
i
v
e
c
l
a
ss
FN
TN
−
T
r
u
e
p
o
s
itiv
e
(
T
P)
is
th
e
n
u
m
b
er
o
f
co
r
r
ec
t
p
r
ed
ictio
n
s
o
n
d
ata
wh
o
s
e
ac
tu
al
v
alu
e
is
also
tr
u
e.
−
Fals
e
n
eg
ativ
e
(
FN)
o
cc
u
r
s
wh
en
d
ata
th
at
s
h
o
u
ld
b
e
cla
s
s
if
ied
as
p
o
s
itiv
e
i
s
m
is
tak
en
ly
p
r
ed
icted
as
n
eg
ativ
e
b
y
th
e
m
o
d
el.
T
h
is
m
ea
n
s
th
e
m
o
d
el
f
ails
to
id
e
n
tify
p
o
s
itiv
e
d
ata
an
d
in
co
r
r
ec
tly
class
if
ies
it
as
n
eg
ativ
e.
−
Fals
e
po
s
itiv
e
(
FP
)
I
t
is
a
c
o
n
d
itio
n
o
f
t
h
e
ac
tu
al
d
ata
th
at
is
wr
o
n
g
(
n
eg
ativ
e
d
ata)
b
u
t
is
p
r
ed
icted
as
tr
u
e
d
ata.
−
T
r
u
e
n
eg
ativ
e
(
T
N
)
T
h
at
is
,
th
e
p
r
ed
ictio
n
is
co
r
r
ec
t
as
n
eg
ativ
e
d
ata
ac
co
r
d
i
n
g
to
th
e
ac
tu
al
d
ata
co
n
d
itio
n
is
tr
u
e
as n
eg
ativ
e
d
ata.
T
o
ev
alu
at
e
th
e
o
v
er
all
p
er
f
o
r
m
an
ce
o
f
t
h
e
m
o
d
el
’
s
p
r
ed
i
ctio
n
s
,
ac
cu
r
ac
y
m
etr
ics
ar
e
em
p
lo
y
ed
.
T
h
e
ac
cu
r
ac
y
s
co
r
e
is
ca
lcu
lated
u
s
in
g
a
s
tan
d
ar
d
f
o
r
m
u
la
d
er
iv
ed
f
r
o
m
th
e
elem
en
ts
o
f
th
e
co
n
f
u
s
io
n
m
atr
ix
,
as p
r
esen
ted
in
T
ab
le
1
,
u
s
in
g
th
e
f
o
llo
win
g
(
1
)
.
=
+
+
+
+
(
1
)
W
h
er
e
T
P
d
en
o
tes
tr
u
e
p
o
s
itiv
e
,
T
N
is
tr
u
e
n
eg
ativ
e
,
FP
is
f
alse
p
o
s
itiv
e
,
an
d
FN
is
f
alse
n
eg
ativ
e
.
Ad
d
itio
n
ally
,
th
is
c
h
ap
ter
o
u
t
lin
es
th
e
r
esear
ch
m
et
h
o
d
o
lo
g
y
ap
p
lied
in
th
e
s
tu
d
y
.
I
n
g
en
er
al,
th
e
r
esear
ch
p
r
o
ce
s
s
co
n
s
is
ts
o
f
s
ev
er
al
k
ey
s
tag
es,
wh
ich
ar
e
illu
s
tr
ated
in
Fig
u
r
e
1
.
Fig
u
r
e
1
.
R
esear
ch
s
tag
es
−
Data
s
et
co
llectio
n
:
in
th
i
s
s
ta
g
e,
th
e
d
atasets
r
eq
u
ir
ed
f
o
r
th
e
s
tu
d
y
ar
e
g
ath
er
ed
.
T
h
ese
d
atasets
in
clu
d
e
co
m
p
r
eh
e
n
s
iv
e
in
f
o
r
m
atio
n
o
n
m
ed
ical
h
is
to
r
y
,
lab
o
r
ato
r
y
t
est
r
esu
lts
,
an
d
d
iag
n
o
s
tic
d
at
a
r
elate
d
to
liv
e
r
h
ea
lth
.
T
h
e
p
r
im
ar
y
d
ataset
u
s
ed
in
th
is
r
esear
ch
is
o
b
tain
ed
f
r
o
m
t
h
e
UC
I
m
ac
h
in
e
lear
n
in
g
r
ep
o
s
ito
r
y
[
3
6
]
.
I
n
ad
d
itio
n
,
r
ea
l
-
wo
r
ld
clin
ical
d
ata
wer
e
c
o
llected
th
r
o
u
g
h
d
ir
ec
t
f
iel
d
s
tu
d
ies
at
s
ev
er
al
h
o
s
p
itals
in
Am
b
o
n
city
,
Ma
lu
k
u
,
I
n
d
o
n
esia.
−
Data
p
r
ep
r
o
ce
s
s
in
g
:
th
is
p
h
ase
in
v
o
lv
es
clea
n
in
g
th
e
d
ata
to
r
em
o
v
e
n
o
is
e
an
d
in
co
n
s
is
ten
cies,
n
o
r
m
alizin
g
v
alu
es,
an
d
elim
i
n
atin
g
r
ed
u
n
d
a
n
t
o
r
ir
r
ele
v
an
t
en
tr
ies.
Featu
r
e
s
elec
tio
n
is
al
s
o
co
n
d
u
cted
t
o
r
em
o
v
e
attr
ib
u
tes
th
at
d
o
n
o
t
s
ig
n
if
ican
tly
c
o
n
tr
ib
u
te
to
th
e
class
if
icatio
n
an
d
p
r
e
d
ictio
n
p
r
o
ce
s
s
es.
T
h
is
en
s
u
r
es th
at
th
e
d
ataset
is
co
n
s
is
ten
t a
n
d
s
u
itab
le
f
o
r
th
e
m
ac
h
in
e
lear
n
i
n
g
alg
o
r
ith
m
s
t
o
b
e
ap
p
lied
.
−
I
m
p
lem
en
tatio
n
o
f
m
ac
h
in
e
l
ea
r
n
in
g
alg
o
r
ith
m
s
:
o
n
ce
th
e
d
ataset
h
as
b
ee
n
p
r
ep
r
o
ce
s
s
e
d
,
it
is
s
p
lit
in
to
two
s
u
b
s
ets:
tr
ain
in
g
an
d
v
alid
atio
n
/tes
tin
g
.
T
h
is
d
iv
is
io
n
allo
ws
f
o
r
m
o
d
el
o
p
tim
izatio
n
d
u
r
in
g
tr
ai
n
in
g
an
d
p
e
r
f
o
r
m
an
ce
e
v
alu
atio
n
d
u
r
in
g
v
alid
atio
n
.
T
h
is
r
esear
ch
em
p
lo
y
s
th
r
ee
ca
teg
o
r
ies
o
f
p
r
ed
ictiv
e
m
o
d
ellin
g
tech
n
i
q
u
es:
a
lin
ea
r
-
b
ased
m
eth
o
d
r
ep
r
esen
ted
b
y
lin
ea
r
r
eg
r
ess
io
n
,
a
tr
ee
-
en
s
em
b
le
s
tr
ateg
y
ex
em
p
lifie
d
b
y
r
an
d
o
m
f
o
r
e
s
t
,
an
d
a
n
a
d
v
an
ce
d
g
r
ad
ie
n
t
-
boo
s
tin
g
f
r
am
ewo
r
k
co
m
m
o
n
ly
k
n
o
w
n
as
L
ig
h
tGB
M.
All
im
p
lem
en
tatio
n
p
r
o
ce
d
u
r
es
ar
e
co
n
d
u
cted
u
s
in
g
Go
o
g
le
C
o
lab
as
th
e
co
m
p
u
tatio
n
al
en
v
ir
o
n
m
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
C
o
mp
a
r
a
tive
a
n
a
lysi
s
o
f lin
ea
r
r
eg
r
es
s
io
n
,
r
a
n
d
o
m
fo
r
est,
a
n
d
Lig
h
tGB
M fo
r
h
ep
a
titi
s
…
(
Hen
n
ie
Tu
h
u
teru
)
433
−
Mo
d
el
v
alid
atio
n
:
th
is
s
tag
e
in
v
o
lv
es
v
alid
atin
g
th
e
tr
a
in
ed
m
o
d
els
u
s
in
g
th
e
v
ali
d
atio
n
d
ataset.
T
h
e
p
e
r
f
o
r
m
an
ce
o
f
ea
ch
alg
o
r
ith
m
is
ass
ess
ed
b
ased
o
n
a
cc
u
r
ac
y
m
etr
ics,
wh
ich
s
er
v
e
as
in
d
icato
r
s
o
f
p
r
ed
ictio
n
r
elia
b
ilit
y
.
E
ac
h
m
o
d
el
is
ev
alu
ated
u
s
in
g
t
h
e
s
am
e
v
alid
atio
n
p
r
o
to
c
o
l
to
en
s
u
r
e
f
air
co
m
p
ar
is
o
n
.
−
Alg
o
r
ith
m
p
er
f
o
r
m
a
n
ce
co
m
p
ar
is
o
n
:
in
th
e
f
in
al
s
tag
e,
th
e
p
er
f
o
r
m
an
ce
o
f
all
th
r
e
e
alg
o
r
ith
m
s
is
co
m
p
ar
ed
.
Af
ter
o
b
tain
in
g
th
e
ac
cu
r
ac
y
m
etr
ics
f
r
o
m
th
e
v
alid
atio
n
p
h
ase,
a
co
m
p
a
r
a
tiv
e
an
aly
s
is
is
p
er
f
o
r
m
ed
t
o
id
e
n
tify
th
e
alg
o
r
ith
m
with
th
e
m
o
s
t
r
e
liab
le
an
d
ac
cu
r
ate
p
r
e
d
ictiv
e
ca
p
a
b
ilit
ies.
T
h
is
an
aly
s
is
s
u
p
p
o
r
ts
th
e
s
elec
tio
n
o
f
th
e
m
o
s
t e
f
f
ec
tiv
e
m
o
d
el
f
o
r
h
ep
atitis
s
u
r
v
iv
al
p
r
e
d
i
ctio
n
.
3.
RE
SU
L
T
S AN
D
D
I
SCU
SS
I
O
N
3
.
1
.
Da
t
a
s
et
co
llect
i
o
n
T
h
is
r
esear
ch
u
tili
ze
d
a
d
ataset
o
b
tain
ed
f
r
o
m
th
e
UC
I
m
ac
h
in
e
lear
n
in
g
r
e
p
o
s
ito
r
y
[
1
2
]
,
[
3
6
]
,
wh
ich
was
s
u
p
p
lem
en
ted
with
o
r
i
g
in
al
clin
ical
d
ata
co
llected
f
r
o
m
v
ar
io
u
s
h
o
s
p
itals
an
d
h
ea
lth
f
ac
ilit
ies
in
Am
b
o
n
city
,
Ma
lu
k
u
,
I
n
d
o
n
esia.
A
to
tal
o
f
1
5
4
p
atien
t
r
ec
o
r
d
s
wer
e
u
s
ed
,
ea
ch
co
n
tain
in
g
1
9
in
d
ep
en
d
en
t
v
a
r
iab
les
,
in
clu
d
in
g
clin
ical
s
y
m
p
to
m
s
an
d
lab
o
r
ato
r
y
test
r
esu
lts
r
e
lev
an
t
to
h
ep
atitis
d
iag
n
o
s
is
.
T
h
e
in
d
ep
en
d
en
t
v
ar
iab
les
in
clu
d
e
d
:
Ag
e,
Sex
,
Ster
o
id
s
,
An
tiv
ir
al,
Fatig
u
e,
Ma
lais
e,
An
o
r
ex
ia,
L
iv
er
B
ig
,
L
iv
er
Firm
,
Sp
leen
Palp
ab
le,
Sp
id
er
s
,
Ascite
s
,
V
ar
ices,
B
iliru
b
in
,
Alk
Ph
o
s
p
h
ate,
SGOT
,
Alb
u
m
in
,
Pro
tim
e,
an
d
His
to
lo
g
y
.
T
h
e
d
ep
en
d
e
n
t
v
ar
iab
le
was
th
e
s
u
r
v
iv
al
s
tatu
s
o
f
ea
ch
h
ep
atitis
p
atien
t,
lab
eled
as
eith
er
“L
iv
e”
o
r
“Die
”.
T
h
e
s
e
l
e
c
t
e
d
f
e
a
t
u
r
e
s
w
e
r
e
c
h
o
s
e
n
b
a
s
e
d
o
n
t
h
e
i
r
c
l
i
n
i
c
a
l
r
e
l
e
v
a
n
c
e
t
o
h
e
p
a
t
i
t
i
s
p
r
o
g
r
e
s
s
i
o
n
a
n
d
p
r
o
g
n
o
s
i
s
[
5
]
,
[
6
]
.
3
.
2
.
Da
t
a
prepro
ce
s
s
ing
T
h
e
s
tatg
e
o
f
p
r
ep
r
o
ce
s
s
in
g
i
n
v
o
lv
e
d
clea
n
in
g
th
e
d
ataset
b
y
h
a
n
d
lin
g
m
is
s
in
g
v
alu
es,
co
r
r
ec
tin
g
in
co
n
s
is
ten
t
d
ata
ty
p
es,
an
d
r
em
o
v
in
g
d
u
p
licate
en
tr
ies.
C
ateg
o
r
ical
v
ar
iab
les
wer
e
tr
an
s
f
o
r
m
ed
i
n
to
n
u
m
er
ical
f
o
r
m
at
to
s
u
it
th
e
m
ac
h
in
e
lear
n
in
g
alg
o
r
ith
m
s
.
An
ex
p
lo
r
at
o
r
y
d
ata
an
aly
s
is
(
E
DA)
was
also
p
er
f
o
r
m
ed
,
in
cl
u
d
in
g
co
r
r
elati
o
n
an
aly
s
is
b
etwe
en
f
ea
tu
r
es to
ass
ess
in
ter
-
v
ar
iab
le
r
elatio
n
s
h
ip
s
.
I
n
g
en
er
al,
f
r
o
m
th
e
r
esu
lts
o
f
d
ata
ex
p
lo
r
atio
n
,
it
is
k
n
o
wn
th
at
th
er
e
i
s
a
p
o
s
itiv
e
co
r
r
elat
io
n
in
th
e
v
ar
iab
les
‘
b
iliru
b
in
’
an
d
‘
al
k
_
p
h
o
s
p
h
ate
’
s
h
o
w
n
in
Fig
u
r
e
2
.
T
h
e
g
r
ea
ter
t
h
e
v
alu
e,
th
e
g
r
ea
ter
th
e
p
o
s
itiv
e
co
r
r
elatio
n
s
h
o
wn
.
T
h
is
co
r
r
elatio
n
is
im
p
o
r
tan
t
to
s
ee
th
e
e
x
ten
t
o
f
th
e
r
elatio
n
s
h
ip
b
etw
ee
n
v
ar
iab
les
in
th
e
d
at
a.
Af
ter
all
p
r
ep
r
o
ce
s
s
in
g
p
r
o
ce
d
u
r
es,
th
e
d
ataset
was
s
ep
ar
ated
in
to
two
s
eg
m
en
t
s
,
wh
er
e
th
e
lar
g
er
s
eg
m
en
t su
p
p
o
r
ted
m
o
d
el
tr
ai
n
in
g
an
d
th
e
s
m
aller
s
eg
m
en
t
s
er
v
ed
f
o
r
test
in
g
an
d
e
v
alu
ati
o
n
.
Fig
u
r
e
2
.
C
o
r
r
elatio
n
o
f
v
ar
io
u
s
in
d
ep
en
d
en
t v
a
r
iab
les in
th
e
d
ataset
u
s
in
g
Hea
tm
ap
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
.
1
,
J
an
u
ar
y
20
2
6
:
430
-
4
3
8
434
3.
3
.
I
m
ple
m
ent
a
t
io
n o
f
ma
c
hin
e
lea
rning
a
lg
o
rit
hm
s
T
h
r
ee
m
ac
h
in
e
lear
n
in
g
m
o
d
els
wer
e
im
p
lem
en
ted
:
lin
ea
r
r
eg
r
ess
io
n
,
r
an
d
o
m
f
o
r
est,
an
d
L
ig
h
tGB
M.
T
h
e
clea
n
e
d
d
ata
s
et
was
tr
ain
ed
an
d
test
ed
o
n
ea
ch
m
o
d
el
to
ass
ess
its
ab
i
lity
to
class
if
y
th
e
s
u
r
v
iv
al
s
tatu
s
o
f
h
ep
atitis
p
atien
ts
in
to
two
class
es:
liv
e
an
d
d
ie
.
T
h
e
p
r
o
ce
s
s
es
o
f
tr
ai
n
in
g
an
d
ev
al
u
atin
g
th
e
m
o
d
els
wer
e
ex
ec
u
ted
i
n
an
o
n
lin
e
co
m
p
u
tin
g
en
v
ir
o
n
m
en
t,
with
Go
o
g
le
C
o
lab
s
er
v
in
g
as
th
e
m
ain
p
latf
o
r
m
.
E
ac
h
alg
o
r
ith
m
was tr
ain
ed
u
s
in
g
id
en
tical
d
ata
s
p
lits
an
d
e
v
alu
atio
n
cr
iter
ia
to
e
n
s
u
r
e
f
air
co
m
p
ar
is
o
n
.
3.
4
.
M
o
del
v
a
lid
a
t
io
n
T
o
ev
alu
ate
m
o
d
el
p
er
f
o
r
m
a
n
ce
,
class
if
icatio
n
r
esu
lts
wer
e
an
aly
ze
d
u
s
in
g
co
n
f
u
s
io
n
m
a
tr
ices
an
d
s
ev
er
al
ev
alu
atio
n
m
etr
ics:
ac
cu
r
ac
y
,
p
r
ec
is
io
n
,
r
ec
all,
an
d
F1
-
s
co
r
e.
T
h
e
co
n
f
u
s
io
n
m
atr
i
x
f
o
r
ea
c
h
alg
o
r
ith
m
is
p
r
esen
ted
b
elo
w
o
n
T
ab
le
2
.
T
h
e
i
n
ter
p
r
etatio
n
o
f
T
ab
le
2
illu
s
tr
ates
h
o
w
th
e
m
o
d
el
ca
teg
o
r
ized
th
e
test
s
am
p
les,
d
etailin
g
th
e
d
is
tr
ib
u
tio
n
o
f
co
r
r
ec
t a
n
d
in
c
o
r
r
ec
t p
r
ed
ictio
n
s
ac
r
o
s
s
all
f
o
u
r
o
u
tco
m
e
ty
p
es.
As m
an
y
as 2
0
% o
f
th
e
to
tal
d
ataset,
n
am
ely
1
5
4
d
ata
,
3
1
d
ata
wer
e
u
s
ed
as test
s
et
s
an
d
p
r
o
v
id
e
d
p
r
ed
ictio
n
r
esu
lts
f
o
r
po
s
itiv
e
an
d
co
r
r
ec
tly
p
r
ed
ict
ed
d
ata
co
n
d
itio
n
s
as
m
an
y
as
8
d
ata,
1
4
d
ata
wer
e
co
r
r
ec
tly
p
r
ed
icted
b
u
t
p
r
ed
icted
in
co
r
r
ec
tly
b
y
th
e
m
o
d
el,
7
d
ata
wer
e
in
co
r
r
ec
tly
p
r
ed
icted
(
as
p
o
s
itiv
e
d
ata)
an
d
2
d
ata
co
n
d
itio
n
s
wer
e
n
eg
ativ
ely
p
r
e
d
icted
(
ac
tu
al
d
ata
was
wr
o
n
g
)
.
Acc
o
r
d
in
g
to
th
e
r
esu
lts
o
f
th
e
p
r
ed
i
ctio
n
,
th
e
ac
cu
r
ac
y
lev
el
o
b
tain
ed
b
y
th
e
r
eg
r
ess
io
n
lin
ea
r
lear
n
i
n
g
m
o
d
el
is
0
.
3
2
2
5
8
0
o
r
3
2
%.
T
ab
le
2
.
C
o
n
f
u
s
io
n
m
atr
ix
–
lin
ea
r
r
eg
r
ess
io
n
P
r
e
d
i
c
t
i
o
n
r
e
su
l
t
R
e
a
l
s
i
t
u
a
t
i
o
n
P
o
si
t
i
v
e
c
l
a
ss
N
e
g
a
t
i
v
e
c
l
a
ss
P
o
si
t
i
v
e
c
l
a
ss
8
7
N
e
g
a
t
i
v
e
c
l
a
ss
14
2
I
n
T
ab
le
3
,
th
e
class
if
icatio
n
b
ased
o
n
co
n
f
u
s
io
n
m
atr
ix
also
s
h
o
ws
th
e
p
r
ed
ictio
n
r
esu
lts
f
o
r
2
0
%
o
f
th
e
test
d
ata
f
r
o
m
th
e
to
tal
d
at
a
o
wn
ed
.
T
h
e
d
ata
c
o
n
d
itio
n
i
s
co
r
r
ec
t
an
d
p
r
e
d
icted
c
o
r
r
ec
t
ly
b
y
th
is
lear
n
in
g
m
o
d
el
as
m
an
y
as
1
6
d
ata,
th
e
d
ata
is
co
r
r
ec
t
b
u
t
p
r
ed
icted
in
co
r
r
ec
tly
as
m
an
y
as
5
,
th
e
d
ata
co
n
d
itio
n
is
wr
o
n
g
an
d
p
r
ed
icted
co
r
r
ec
tl
y
0
d
ata
an
d
th
e
d
ata
co
n
d
itio
n
is
in
c
o
r
r
ec
tly
p
r
ed
icted
as
in
co
r
r
ec
t
d
ata
as
1
0
d
ata.
Fro
m
th
e
r
esu
lts
o
f
t
h
is
p
r
ed
ictio
n
,
t
h
e
ac
cu
r
ac
y
lev
el
o
b
tain
ed
b
y
th
e
r
a
n
d
o
m
f
o
r
est
lear
n
in
g
m
o
d
el
is
0
.
8
3
8
7
0
9
o
r
8
4
%.
Acc
o
r
d
in
g
to
t
h
e
class
if
icatio
n
r
esu
lts
in
T
ab
le
4
,
it
ca
n
b
e
s
ee
n
th
at
t
h
e
m
o
d
el
s
u
cc
ess
f
u
lly
p
r
ed
icted
t
h
e
d
ata
c
o
r
r
ec
tly
f
o
r
p
o
s
itiv
e
d
ata
as
m
an
y
as
1
4
d
ata,
th
e
c
o
r
r
ec
t d
ata
an
d
p
r
ed
icted
wr
o
n
g
d
ata
b
y
th
e
m
o
d
el
as
m
a
n
y
as
7
d
ata,
th
e
wr
o
n
g
d
ata
a
n
d
p
r
ed
icted
as
tr
u
e
d
ata
0
d
ata
an
d
th
e
wr
o
n
g
d
ata
(
n
eg
ativ
e
)
d
ata
p
r
ed
icted
co
r
r
ec
tly
as
wr
o
n
g
d
ata
as
m
an
y
as
1
0
d
ata
.
T
h
is
s
h
o
ws
th
e
lev
el
o
f
ac
c
u
r
ac
y
o
b
tain
e
d
b
y
th
e
lear
n
in
g
m
o
d
el,
wh
ich
is
0
.
7
7
4
1
o
r
7
7
%.
T
ab
le
3
.
C
o
n
f
u
s
io
n
m
atr
ix
–
r
a
n
d
o
m
f
o
r
est
P
r
e
d
i
c
t
i
o
n
r
e
su
l
t
R
e
a
l
s
i
t
u
a
t
i
o
n
P
o
si
t
i
v
e
c
l
a
ss
N
e
g
a
t
i
v
e
c
l
a
ss
P
o
si
t
i
v
e
c
l
a
ss
16
0
N
e
g
a
t
i
v
e
c
l
a
ss
5
10
T
ab
le
4
.
C
o
n
f
u
s
io
n
m
atr
ix
–
L
i
g
h
tGB
M
P
r
e
d
i
c
t
i
o
n
r
e
su
l
t
R
e
a
l
s
i
t
u
a
t
i
o
n
P
o
si
t
i
v
e
c
l
a
ss
N
e
g
a
t
i
v
e
c
l
a
ss
P
o
si
t
i
v
e
c
l
a
ss
8
7
N
e
g
a
t
i
v
e
c
l
a
ss
14
2
3.
5
.
Alg
o
rit
hm
perf
o
r
m
a
nce
co
m
pa
riso
n
B
ased
o
n
th
e
class
if
icatio
n
an
d
p
r
ed
ictio
n
r
esu
lts
o
b
tai
n
ed
f
r
o
m
th
e
lear
n
in
g
m
o
d
els
,
lin
ea
r
r
eg
r
ess
io
n
,
t
h
e
co
m
p
ar
ativ
e
a
s
s
es
s
m
en
t
r
ev
ea
ls
th
at
r
an
d
o
m
f
o
r
est
o
u
tp
e
r
f
o
r
m
ed
t
h
e
o
th
er
m
o
d
els,
attain
in
g
an
ac
cu
r
ac
y
o
f
8
4
%.
L
ig
h
tGB
M
ac
h
iev
ed
7
7
%,
an
d
lin
ea
r
r
eg
r
ess
io
n
s
h
o
wed
th
e
wea
k
est
p
er
f
o
r
m
an
ce
with
3
2
%
ac
cu
r
ac
y
.
T
h
ese
f
in
d
in
g
s
s
u
p
p
o
r
t
ea
r
lier
s
tu
d
ies
[
1
]
,
[
2
]
,
wh
ic
h
also
h
i
g
h
lig
h
te
d
th
e
s
tr
o
n
g
p
r
ed
ictiv
e
p
er
f
o
r
m
an
ce
o
f
r
an
d
o
m
f
o
r
est
in
liv
er
d
is
ea
s
e
class
if
icatio
n
.
Ho
wev
er
,
th
is
s
tu
d
y
g
o
es
f
u
r
th
er
b
y
c
o
m
b
in
i
n
g
r
ef
er
en
ce
d
ata
with
r
ea
l
-
wo
r
ld
clin
ical
d
ata
co
llected
f
r
o
m
ac
tu
al
h
ea
lth
ca
r
e
s
ettin
g
s
.
T
h
is
in
teg
r
at
io
n
p
r
o
v
id
es
a
m
o
r
e
lo
ca
lized
an
d
r
ea
lis
tic
v
iew
o
f
h
o
w
th
e
m
o
d
els
p
er
f
o
r
m
in
p
r
ac
tice,
esp
ec
ially
in
en
v
ir
o
n
m
en
ts
wh
er
e
v
a
r
iab
ilit
y
an
d
d
ata
q
u
ality
o
f
ten
d
if
f
er
f
r
o
m
c
o
n
tr
o
lled
r
esear
ch
d
atas
ets
.
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
C
o
mp
a
r
a
tive
a
n
a
lysi
s
o
f lin
ea
r
r
eg
r
es
s
io
n
,
r
a
n
d
o
m
fo
r
est,
a
n
d
Lig
h
tGB
M fo
r
h
ep
a
titi
s
…
(
Hen
n
ie
Tu
h
u
teru
)
435
L
o
o
k
in
g
at
th
e
ac
cu
r
ac
y
r
esu
l
ts
th
r
o
u
g
h
th
e
co
n
f
u
s
io
n
m
atr
ix
,
r
an
d
o
m
f
o
r
est
co
n
s
is
ten
tly
d
eliv
er
ed
th
e
m
o
s
t
ac
c
u
r
ate
p
r
ed
ictio
n
s
,
p
lacin
g
it
at
th
e
t
o
p
,
f
o
llo
w
ed
b
y
L
ig
h
tGB
M.
lin
ea
r
r
eg
r
ess
io
n
,
b
y
co
n
tr
ast,
lag
g
ed
b
e
h
in
d
,
an
d
t
h
is
m
ay
b
e
d
u
e
t
o
h
o
w
t
h
e
m
o
d
el
o
p
e
r
ates
d
if
f
er
en
tly
f
r
o
m
th
e
o
th
er
two
alg
o
r
ith
m
s
.
In
th
e
ca
s
e
o
f
r
an
d
o
m
f
o
r
est
an
d
L
ig
h
tGB
M,
class
if
icatio
n
an
d
p
r
ed
ictio
n
p
r
o
ce
s
s
es
wer
e
ap
p
lied
d
ir
ec
tly
to
th
e
m
o
d
els.
B
u
t
with
lin
ea
r
r
e
g
r
ess
io
n
,
a
co
n
v
er
s
io
n
s
tep
w
as
n
ee
d
ed
to
tu
r
n
co
n
tin
u
o
u
s
o
u
tp
u
ts
in
t
o
b
in
ar
y
f
o
r
m
b
ef
o
r
e
th
e
m
o
d
el
co
u
ld
b
e
ev
alu
ated
f
o
r
class
if
icatio
n
task
s
.
T
h
is
n
o
t
o
n
ly
ad
d
s
an
ex
tr
a
lay
er
o
f
co
m
p
lex
ity
b
u
t
also
ex
p
o
s
es
o
n
e
o
f
th
e
m
o
d
el
’
s
m
ain
wea
k
n
ess
es,
its
lim
ited
ab
ilit
y
to
h
an
d
le
b
in
ar
y
clin
ical
class
if
icatio
n
,
esp
ec
ially
wh
en
wo
r
k
in
g
with
n
o
n
-
lin
ea
r
d
ata
lik
e
h
ep
atitis
p
r
o
g
r
ess
io
n
.
T
h
is
u
n
d
er
s
co
r
es
th
e
im
p
o
r
ta
n
ce
o
f
s
elec
tin
g
alg
o
r
ith
m
s
t
h
at
ar
e
n
o
t
o
n
ly
ac
c
u
r
ate
b
u
t
also
well
-
m
atch
ed
to
th
e
s
tr
u
ct
u
r
e
a
n
d
ch
ar
ac
ter
is
tics
o
f
th
e
d
ata.
I
n
th
is
r
esear
ch
,
th
e
r
an
d
o
m
f
o
r
e
s
t
m
o
d
el
is
clea
r
l
y
th
e
m
o
s
t
ef
f
ec
tiv
e
a
m
o
n
g
th
o
s
e
ev
alu
ated
.
H
o
wev
er
,
t
h
e
p
er
f
o
r
m
an
ce
o
f
t
h
e
m
o
d
el
is
n
o
t
s
o
lely
d
ep
e
n
d
en
t o
n
th
e
alg
o
r
ith
m
s
elec
tio
n
b
u
t
is
also
s
ig
n
if
ican
tly
in
f
lu
e
n
ce
d
b
y
f
ac
to
r
s
s
u
ch
as
th
e
d
ataset
s
ize,
th
e
r
elev
a
n
ce
o
f
th
e
f
ea
tu
r
es,
an
d
th
e
q
u
ality
o
f
th
e
in
p
u
t
d
ata
.
Go
in
g
f
o
r
war
d
,
f
u
r
th
er
ev
al
u
atio
n
o
f
o
th
e
r
m
o
d
els
u
s
in
g
lar
g
er
an
d
m
o
r
e
d
i
v
er
s
e
d
atasets
wo
u
ld
b
e
v
alu
ab
le
to
b
etter
u
n
d
e
r
s
tan
d
th
e
r
o
b
u
s
tn
ess
an
d
g
en
er
aliza
b
ilit
y
o
f
ea
ch
lear
n
in
g
a
p
p
r
o
ac
h
.
I
n
ad
d
iti
o
n
,
th
e
f
in
d
in
g
s
p
o
in
t
t
o
th
e
p
r
o
m
is
in
g
r
o
le
o
f
en
s
em
b
l
e
-
b
ased
alg
o
r
ith
m
s
,
p
ar
ticu
la
r
ly
r
a
n
d
o
m
f
o
r
est
,
as
p
r
ac
tical
to
o
ls
in
in
tellig
en
t
clin
ical
d
ec
is
io
n
-
s
u
p
p
o
r
t
s
y
s
tem
s
f
o
r
ea
r
ly
d
etec
tio
n
an
d
tr
ea
tm
en
t p
la
n
n
i
n
g
o
f
h
ep
atitis
.
3.
6
.
Clini
ca
l
ins
ig
hts
C
lin
ical
ex
p
er
ts
em
p
h
asized
t
h
at
h
ep
atitis
v
ir
u
s
es
ar
e
class
if
ied
in
to
f
iv
e
m
aj
o
r
f
o
r
m
s
,
w
h
ich
d
if
f
er
in
h
o
w
th
ey
s
p
r
ea
d
,
h
o
w
th
ey
p
r
esen
t
clin
ically
,
an
d
th
e
s
ev
er
ity
o
f
th
eir
m
o
r
tal
ity
r
is
k
.
Acc
u
r
ate
class
if
icatio
n
is
e
s
s
en
tial
i
n
g
u
id
in
g
d
iag
n
o
s
tic
an
d
tr
ea
tm
en
t
d
ec
is
io
n
s
.
Mo
r
eo
v
er
,
u
n
d
er
s
tan
d
in
g
tr
an
s
m
is
s
io
n
p
ath
way
s
an
d
p
a
tien
t
b
eh
av
i
o
r
s
is
cr
u
cial
f
o
r
p
r
ev
en
tio
n
,
r
ein
f
o
r
cin
g
th
e
im
p
o
r
tan
ce
o
f
h
y
g
ie
n
e
an
d
d
ietar
y
m
an
ag
e
m
en
t in
m
itig
atin
g
h
ep
atitis
tr
an
s
m
is
s
io
n
r
is
k
s
.
4.
CO
NCLU
SI
O
N
T
h
is
wo
r
k
ap
p
lied
an
d
ass
ess
ed
th
r
ee
p
r
e
d
ictiv
e
ap
p
r
o
ac
h
e
s
,
lin
ea
r
r
eg
r
ess
io
n
,
r
an
d
o
m
f
o
r
est
,
an
d
L
ig
h
tG
BM
,
to
esti
m
ate
s
u
r
v
iv
al
o
u
tco
m
es
in
h
ep
atitis
ca
s
es.
T
h
e
co
m
p
ar
ativ
e
r
esu
lts
i
n
d
icate
th
at
r
an
d
o
m
f
o
r
est
d
eliv
er
ed
th
e
s
tr
o
n
g
e
s
t
p
er
f
o
r
m
an
ce
with
8
4
%
a
cc
u
r
ac
y
,
L
ig
h
tGB
M
attain
ed
7
7
%,
an
d
lin
ea
r
r
eg
r
ess
io
n
s
h
o
wed
th
e
wea
k
est
r
esu
lt
at
3
2
%.
T
h
ese
r
e
s
u
lts
ar
e
s
ig
n
if
ican
t
b
ec
au
s
e
th
ey
v
alid
ate
th
e
ap
p
licab
ilit
y
o
f
e
n
s
em
b
le
lear
n
in
g
m
o
d
els,
p
ar
ticu
lar
ly
r
a
n
d
o
m
f
o
r
est
,
in
clin
ical
p
r
ed
icti
o
n
task
s
u
s
in
g
r
ea
l
-
wo
r
ld
p
atien
t
d
ata.
C
o
m
p
ar
ed
to
ex
is
tin
g
r
esear
ch
,
th
is
s
tu
d
y
co
n
tr
ib
u
tes
a
co
n
tex
t
-
s
p
ec
if
i
c
m
o
d
el
tailo
r
ed
to
h
ea
lth
ca
r
e
co
n
d
itio
n
s
in
Am
b
o
n
,
I
n
d
o
n
esia,
b
r
id
g
in
g
t
h
e
g
ap
b
etwe
en
th
eo
r
etica
l
m
o
d
els
an
d
f
ield
ap
p
licab
ilit
y
.
T
h
e
lo
wer
p
er
f
o
r
m
an
ce
o
f
lin
ea
r
r
e
g
r
ess
io
n
r
ein
f
o
r
ce
s
th
e
im
p
o
r
tan
ce
o
f
alg
o
r
ith
m
s
elec
tio
n
b
ased
o
n
d
ata
ch
ar
ac
ter
is
tics
an
d
th
e
n
atu
r
e
o
f
th
e
p
r
ed
ictio
n
task
.
Ultim
ately
,
th
ese
f
in
d
in
g
s
d
e
m
o
n
s
tr
ate
th
at
th
e
r
an
d
o
m
f
o
r
est
alg
o
r
ith
m
o
f
f
er
s
an
ac
cu
r
ate
an
d
ad
ap
tiv
e
s
o
lu
tio
n
f
o
r
p
r
ed
ictin
g
s
u
r
v
iv
al
in
h
ep
atitis
ca
s
es,
e
s
p
ec
ially
wh
en
tr
ain
ed
u
s
in
g
r
ea
l
-
wo
r
ld
m
ed
ical
d
ata.
I
ts
p
er
f
o
r
m
an
ce
d
em
o
n
s
tr
ates
th
at
th
is
alg
o
r
ith
m
h
a
s
s
tr
o
n
g
p
o
te
n
tial
f
o
r
i
n
teg
r
at
io
n
in
to
i
n
tellig
en
t
h
ea
lth
ca
r
e
s
y
s
tem
s
,
p
ar
ticu
lar
ly
in
r
eso
u
r
ce
-
lim
ited
s
ettin
g
s
.
Fo
r
f
u
tu
r
e
r
esear
ch
,
s
ev
er
al
im
p
r
o
v
em
e
n
ts
ar
e
s
u
g
g
ested
,
in
clu
d
in
g
ex
p
a
n
d
in
g
th
e
d
ataset
s
ize
to
r
e
d
u
ce
t
h
e
r
is
k
o
f
o
v
er
f
itti
n
g
an
d
im
p
r
o
v
e
g
en
e
r
aliza
b
ilit
y
,
ev
alu
atin
g
ad
d
itio
n
al
m
ac
h
in
e
lear
n
in
g
al
g
o
r
ith
m
s
s
u
ch
a
s
d
ee
p
lear
n
in
g
ap
p
r
o
ac
h
es
to
ex
p
lo
r
e
f
u
r
th
er
p
er
f
o
r
m
an
ce
g
ain
s
,
an
d
class
if
y
in
g
p
r
ed
ictio
n
s
b
ased
o
n
h
ep
atitis
ty
p
es
(
A,
B
,
C
,
an
d
D)
to
en
a
b
le
m
o
r
e
g
r
a
n
u
lar
an
d
d
is
ea
s
e
-
s
p
ec
if
ic
p
r
o
g
n
o
s
tic
m
o
d
els.
T
h
ese
e
n
h
an
ce
m
e
n
ts
ar
e
e
x
p
ec
ted
to
co
n
tr
i
b
u
te
t
o
th
e
d
ev
elo
p
m
e
n
t
o
f
m
o
r
e
ac
c
u
r
a
te,
r
eliab
le,
an
d
clin
ically
a
p
p
licab
le
d
ec
is
io
n
-
s
u
p
p
o
r
t
s
y
s
tem
s
f
o
r
h
ep
atitis
d
iag
n
o
s
is
an
d
p
r
o
g
n
o
s
is
.
ACK
NO
WL
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DG
M
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h
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th
o
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s
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eir
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ce
r
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g
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atitu
d
e
t
o
th
e
R
esear
ch
I
n
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titu
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o
f
U
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s
itas
Kr
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I
n
d
o
n
esia
Ma
lu
k
u
f
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e
s
u
p
p
o
r
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a
n
d
f
ac
ilit
ies
p
r
o
v
id
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th
r
o
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g
h
o
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t
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r
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is
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y
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Sp
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an
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SUP
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Evaluation Warning : The document was created with Spire.PDF for Python.
I
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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
.
1
,
J
an
u
ar
y
20
2
6
:
430
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4
3
8
436
AUTHO
R
CO
NT
RI
B
UT
I
O
NS ST
A
T
E
M
E
N
T
T
h
is
jo
u
r
n
al
u
s
es
th
e
C
o
n
t
r
ib
u
to
r
R
o
les
T
a
x
o
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m
y
(
C
R
ed
iT
)
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ec
o
g
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ize
in
d
iv
i
d
u
al
au
th
o
r
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tio
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r
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th
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r
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ip
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is
p
u
tes,
an
d
f
ac
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co
llab
o
r
atio
n
.
Na
m
e
o
f
Aut
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M
So
Va
Fo
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Vi
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u
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y
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ia
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✓
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Ma
r
v
elo
u
s
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r
v
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R
ijo
ly
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J
o
s
elin
a
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u
h
u
ter
u
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C
:
C
o
n
c
e
p
t
u
a
l
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a
t
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M
:
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f
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so
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D
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r
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t
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DATA AV
AI
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AB
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h
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at
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RE
F
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NC
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S
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jo
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t
u
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m
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c
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.
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