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Ca
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io
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sc
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ise
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re
m
a
in
s
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to
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l
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m
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Ac
c
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ra
te
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ictio
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ra
m
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n
t
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rly
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i
a
g
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n
d
re
d
u
c
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g
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talit
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tes
.
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iev
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n
t
CVD
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e
tec
ti
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n
a
n
d
p
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d
ictio
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re
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ires
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d
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lt
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t
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e
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o
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ise
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se
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ta
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ly
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st
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k
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a
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th
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larly
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las
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n
d
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lg
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rit
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m
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tely
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t
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ise
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se
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tatio
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ti
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m
e
a
su
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s.
K
ey
w
o
r
d
s
:
Ar
tific
ial
in
tellig
en
ce
C
ar
d
io
v
ascu
lar
d
is
ea
s
e
H
ea
r
t d
is
ea
s
e
Ma
ch
in
e
lear
n
in
g
Pre
d
ictio
n
Sy
s
tem
atic
liter
atu
r
e
r
ev
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T
h
is i
s
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n
o
p
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n
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c
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ss
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rticle
u
n
d
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CC B
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li
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se
.
C
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r
r
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s
p
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A
uth
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r
:
J
asim
Far
aj
Ham
m
ad
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lleg
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C
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g
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U
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Stre
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k
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PT2
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tu
d
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I
NT
RO
D
UCT
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C
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ascu
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s
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(
C
VD)
is
th
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lead
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ca
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s
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o
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wid
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r
esp
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x
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1
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9
m
illi
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n
d
ea
th
s
an
n
u
ally
.
E
ar
ly
d
etec
tio
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an
d
ac
c
u
r
ate
p
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o
f
C
VD
ar
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cr
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i
n
r
ed
u
cin
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m
o
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tality
r
ates
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p
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v
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p
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tco
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r
,
p
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d
ictin
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C
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em
ain
s
a
co
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p
lex
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allen
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ted
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r
e
o
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an
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th
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f
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p
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s
o
n
alize
d
a
p
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ac
h
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ca
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.
Ad
v
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m
en
ts
in
tech
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lo
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y
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d
ata
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aly
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tech
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m
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ag
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y
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ig
n
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ican
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g
a
p
s
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e
m
ain
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th
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g
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a
n
d
a
p
p
licatio
n
o
f
th
ese
ad
v
an
ce
m
en
ts
in
clin
ical
p
r
ac
tice
[
1
]
,
[
2
]
.
R
ec
en
t
d
ev
elo
p
m
en
t
s
in
m
ac
h
in
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lear
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in
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(
ML
)
an
d
a
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tific
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in
tellig
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(
AI
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,
p
a
r
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ly
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DL
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,
h
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v
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f
ield
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f
m
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ical
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ag
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tec
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n
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lo
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ab
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th
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au
to
m
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d
d
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d
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elate
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c
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p
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ter
to
m
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ap
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CT
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s
ca
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s
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an
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m
ag
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eso
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g
(
MRIs
)
with
in
cr
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s
in
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p
r
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io
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[
3
]
.
Fu
r
th
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m
o
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DL
m
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d
els
ar
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ad
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t
at
p
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s
s
in
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c
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m
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ata
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atte
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wh
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icativ
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o
f
a
p
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t'
s
ca
r
d
io
v
ascu
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h
ea
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[
4
]
.
Un
lik
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d
s
,
ML
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m
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Evaluation Warning : The document was created with Spire.PDF for Python.
I
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J
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&
C
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p
Sci
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5
0
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-
4
7
52
A
r
tifi
cia
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tellig
en
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a
p
p
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ch
es fo
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ca
r
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d
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i
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tr
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t
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5
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T
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d
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s
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p
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o
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s
.
Desp
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v
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m
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ts
in
ML
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AI
ap
p
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o
f
d
ata,
lim
itin
g
th
eir
ef
f
ec
tiv
en
ess
in
p
r
o
v
i
d
in
g
co
m
p
r
eh
e
n
s
iv
e
r
is
k
ass
es
s
m
en
ts
.
Ad
d
itio
n
ally
,
m
a
n
y
ML
m
o
d
els
u
s
ed
in
C
VD
p
r
ed
ictio
n
ar
e
co
m
p
le
x
an
d
la
ck
in
ter
p
r
etab
ilit
y
,
wh
ich
h
in
d
er
s
th
eir
ap
p
licatio
n
in
clin
ical
s
ettin
g
s
wh
er
e
tr
an
s
p
ar
en
t
an
d
ex
p
lain
a
b
le
d
ec
is
io
n
-
m
ak
in
g
is
ess
en
tial
[
6
]
.
T
h
ese
ch
allen
g
es
h
ig
h
lig
h
t
th
e
p
r
ess
in
g
n
ee
d
f
o
r
in
n
o
v
ativ
e
h
ea
lth
ca
r
e
s
y
s
tem
d
esig
n
s
tailo
r
ed
s
p
ec
if
ically
to
ad
d
r
ess
th
ese
g
ap
s
,
as
em
p
h
asized
in
r
ec
en
t
s
tu
d
ies.
T
h
is
s
y
s
tem
atic
liter
atu
r
e
r
e
v
iew
(
SLR)
aim
s
to
ad
d
r
ess
th
ese
g
ap
s
b
y
s
y
n
th
esizin
g
ex
is
tin
g
r
esear
c
h
o
n
ML
an
d
AI
ap
p
licatio
n
s
in
C
VD
p
r
ed
ictio
n
,
f
o
cu
s
in
g
o
n
s
tu
d
ies
th
at
in
teg
r
ate
m
u
ltip
le
d
ata
ty
p
es
to
en
h
an
ce
p
r
e
d
ictiv
e
p
er
f
o
r
m
an
ce
.
B
y
s
y
s
tem
atica
lly
an
aly
zin
g
th
ese
s
tu
d
ies,
we
s
ee
k
to
id
en
tify
th
e
m
o
s
t
ef
f
ec
tiv
e
m
o
d
els
an
d
tech
n
iq
u
es
an
d
h
i
g
h
lig
h
t
ar
ea
s
wh
er
e
f
u
r
th
er
r
esear
c
h
is
n
ee
d
ed
.
T
h
is
r
ev
iew
co
n
tr
ib
u
tes
to
th
e
f
ield
b
y
p
r
o
v
id
in
g
a
co
m
p
r
e
h
en
s
iv
e
o
v
er
v
iew
o
f
cu
r
r
en
t CVD p
r
ed
ictio
n
m
o
d
el
s
,
ass
es
s
in
g
th
eir
ca
p
ab
ilit
ies,
lim
itatio
n
s
,
an
d
p
o
te
n
tial f
o
r
f
u
tu
r
e
d
ev
elo
p
m
en
t.
T
h
e
p
ap
er
is
o
r
g
an
ized
as f
o
ll
o
ws:
s
ec
tio
n
2
d
etails th
e
m
et
h
o
d
o
lo
g
y
em
p
lo
y
ed
f
o
r
c
o
n
d
u
ctin
g
th
is
s
y
s
tem
atic
liter
atu
r
e
r
ev
iew,
in
clu
d
in
g
th
e
cr
iter
ia
f
o
r
s
tu
d
y
s
elec
tio
n
,
d
ata
ex
tr
ac
tio
n
,
a
n
d
s
y
n
th
esis
.
Sectio
n
3
p
r
esen
ts
th
e
r
esu
lts
,
o
f
f
er
in
g
a
d
etailed
an
aly
s
is
o
f
th
e
f
i
n
d
in
g
s
f
r
o
m
th
e
r
ev
iewe
d
s
tu
d
ies,
f
o
cu
s
in
g
o
n
th
e
s
tr
en
g
th
s
an
d
wea
k
n
ess
es
o
f
v
ar
io
u
s
ML
a
n
d
AI
m
o
d
els
in
p
r
e
d
ictin
g
C
VD.
Sectio
n
4
d
is
cu
s
s
es
th
e
im
p
licatio
n
s
o
f
th
ese
f
in
d
in
g
s
f
o
r
clin
ical
p
r
ac
tice
a
n
d
o
u
tlin
es
p
o
te
n
tial
d
ir
ec
tio
n
s
f
o
r
f
u
tu
r
e
r
esear
ch
.
Fi
n
ally
,
s
ec
tio
n
5
c
o
n
clu
d
es
th
e
p
ap
e
r
b
y
s
u
m
m
ar
izi
n
g
th
e
k
ey
i
n
s
ig
h
ts
an
d
p
r
o
v
id
in
g
r
ec
o
m
m
en
d
atio
n
s
f
o
r
a
d
v
an
ci
n
g
C
VD
p
r
ed
ictio
n
r
esear
ch
u
s
in
g
in
te
g
r
ativ
e
ML
an
d
AI
ap
p
r
o
ac
h
es.
2.
M
E
T
H
O
D
T
h
is
s
y
s
tem
atic
liter
atu
r
e
r
e
v
iew
(
SLR)
was
co
n
d
u
cted
to
e
v
alu
ate
th
e
ap
p
licatio
n
o
f
AI
t
ec
h
n
iq
u
es
f
o
r
C
VD
p
r
ed
ictio
n
.
T
h
e
r
ev
i
ew
p
r
o
ce
s
s
f
o
llo
wed
a
s
tr
u
ct
u
r
ed
p
r
o
to
co
l
to
e
n
s
u
r
e
r
ig
o
r
,
tr
an
s
p
ar
en
cy
,
an
d
r
ep
r
o
d
u
cib
ilit
y
.
T
h
e
m
eth
o
d
o
l
o
g
y
co
n
s
is
ted
o
f
th
r
ee
m
ai
n
p
h
ases
:
r
ev
iew
p
lan
n
in
g
,
r
ev
ie
w
co
n
d
u
ctin
g
,
an
d
r
ev
iew
r
ep
o
r
tin
g
.
2
.
1
.
R
ev
iew
pla
nn
ing
1)
Def
in
in
g
th
e
r
esear
ch
q
u
esti
o
n
s
:
T
h
e
r
esear
ch
q
u
esti
o
n
s
wer
e
f
o
r
m
u
lated
u
s
in
g
th
e
PICOC
f
r
am
ewo
r
k
to
ad
d
r
ess
g
ap
s
id
en
t
if
ied
in
th
e
I
n
tr
o
d
u
ctio
n
:
−
Po
p
u
latio
n
(
P):
st
u
d
ies in
v
o
lv
i
n
g
th
e
u
s
e
o
f
AI
f
o
r
C
VD
p
r
ed
ictio
n
.
−
I
n
ter
v
en
tio
n
(
I
)
: A
I
m
eth
o
d
s
,
i
n
clu
d
in
g
m
ac
h
in
e
lea
r
n
in
g
an
d
DL
tech
n
iq
u
es.
−
C
o
m
p
ar
is
o
n
(
C
)
:
va
r
io
u
s
AI
m
eth
o
d
s
an
d
t
r
ad
itio
n
al
s
tatis
tic
al
m
eth
o
d
s
.
−
Ou
tco
m
es (
O)
:
accu
r
ac
y
an
d
e
f
f
ec
tiv
en
ess
o
f
AI
m
o
d
els in
p
r
ed
ictin
g
C
VD.
−
C
o
n
tex
t (
C
)
:
clin
ica
l settin
g
s
an
d
d
atasets
u
s
ed
f
o
r
AI
m
o
d
el
d
ev
elo
p
m
e
n
t a
n
d
v
alid
atio
n
.
2)
Sp
ec
if
y
in
g
r
esear
c
h
d
atab
ases
:
A
co
m
p
r
e
h
en
s
iv
e
s
ea
r
c
h
wa
s
co
n
d
u
cte
d
in
th
r
ee
m
ajo
r
d
atab
ases
:
I
E
E
E
Xp
lo
r
e,
Sc
o
p
u
s
,
a
n
d
R
esear
ch
Gate
.
T
h
ese
d
atab
ases
wer
e
s
elec
ted
f
o
r
th
eir
ex
ten
s
iv
e
co
v
er
ag
e
o
f
h
ig
h
-
im
p
ac
t stu
d
ies in
m
ed
icin
e
,
en
g
in
ee
r
in
g
,
an
d
co
m
p
u
ter
s
ci
en
ce
.
3)
Dev
elo
p
in
g
a
s
ea
r
ch
s
tr
in
g
f
o
r
ar
ticle
ex
tr
ac
tio
n
:
T
h
e
s
ea
r
ch
s
tr
ateg
y
in
clu
d
ed
a
co
m
b
in
atio
n
o
f
k
ey
ter
m
s
r
elate
d
to
AI
an
d
C
VD
p
r
ed
icti
o
n
,
s
u
ch
as
"CVD,"
"AI
m
eth
o
d
s
,
"
"
ML
,
"
"
DL
,
"
"h
ea
r
t
d
is
ea
s
e
p
r
ed
ictio
n
,
"
an
d
"
p
r
ed
ictiv
e
m
o
d
elin
g
.
"
B
o
o
lean
o
p
er
at
o
r
s
(
AND,
OR
)
wer
e
u
s
ed
to
r
ef
in
e
th
e
s
ea
r
ch
q
u
er
ies.
4)
I
n
clu
s
io
n
cr
iter
ia
:
−
Stu
d
ies p
u
b
lis
h
ed
b
etwe
en
2
0
2
0
an
d
2
0
2
4
in
p
ee
r
-
r
ev
iewe
d
jo
u
r
n
als.
−
Ar
ticles u
tili
zin
g
AI
tech
n
iq
u
e
s
f
o
r
C
VD
p
r
ed
ictio
n
with
q
u
a
n
titativ
e
o
u
tco
m
es.
−
Stu
d
ies wr
itten
in
E
n
g
lis
h
.
5)
E
x
clu
s
io
n
cr
iter
i
a:
−
R
ev
iew
ar
ticles,
ed
ito
r
ials
,
an
d
n
o
n
-
p
ee
r
-
r
ev
iewe
d
a
r
ticles.
−
Stu
d
ies n
o
t f
o
cu
s
ed
o
n
AI
m
eth
o
d
s
o
r
n
o
t r
elate
d
to
C
VD.
−
Ar
ticles with
o
u
t e
m
p
ir
ical
d
at
a
o
n
AI
m
o
d
el
p
e
r
f
o
r
m
an
ce
.
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
8
,
No
.
2
,
May
20
2
5
:
1
2
0
8
-
1
2
1
8
1210
2
.
2
.
Co
nd
uct
ing
re
v
iew
1)
Prim
ar
y
an
d
s
ec
o
n
d
ar
y
s
ea
r
ch
:
−
Prim
ar
y
s
ea
r
ch
:
th
e
in
itial
s
ea
r
ch
ac
r
o
s
s
th
e
th
r
ee
d
atab
ases
i
d
en
tifie
d
a
to
tal
o
f
1
,
1
7
8
r
ec
o
r
d
s
(
I
E
E
E
:
1
6
2
,
Sco
p
u
s
:
6
7
5
,
R
esear
ch
Gate
:
3
4
1
)
.
A
f
ter
r
em
o
v
in
g
4
3
6
d
u
p
li
ca
tes,
7
4
2
r
ec
o
r
d
s
wer
e
s
cr
ee
n
ed
b
y
titl
es
an
d
ab
s
tr
ac
ts
.
−
Seco
n
d
ar
y
s
ea
r
ch
:
ad
d
itio
n
al
r
elev
an
t
s
tu
d
ies
wer
e
id
en
tifie
d
b
y
r
ev
iewin
g
t
h
e
r
ef
e
r
en
ce
s
o
f
th
e
s
elec
ted
ar
ticles d
u
r
in
g
th
e
f
u
ll
-
tex
t r
e
v
iew
s
tag
e.
2)
Stu
d
y
s
elec
tio
n
p
r
o
ce
s
s
:
T
h
e
s
tu
d
y
s
elec
tio
n
p
r
o
ce
s
s
f
o
llo
wed
th
e
PR
I
SMA
g
u
id
elin
es,
as
d
ep
icted
in
Fig
u
r
e
1
.
A
to
t
al
o
f
1
,
1
7
8
ar
ticles
wer
e
in
itially
id
en
tifie
d
th
r
o
u
g
h
s
ea
r
ch
es
in
th
r
ee
d
a
tab
ases
:
I
E
E
E
Xp
lo
r
e
(
1
6
2
ar
t
icles),
Sco
p
u
s
(
6
7
5
ar
ticles),
an
d
R
esear
ch
Gate
(
3
4
1
ar
ticles)
.
Af
ter
r
em
o
v
i
n
g
4
3
6
d
u
p
licate
ar
ticles,
7
4
2
r
ec
o
r
d
s
r
em
ain
ed
f
o
r
titl
e
an
d
ab
s
tr
ac
t
s
cr
ee
n
in
g
.
Fo
llo
win
g
th
is
s
cr
ee
n
in
g
,
6
0
3
ar
ticles
wer
e
ex
clu
d
ed
f
o
r
n
o
t
m
ee
tin
g
th
e
in
clu
s
io
n
cr
iter
ia
s
p
ec
if
ied
in
T
ab
le
1
(
Ap
p
en
d
ix
)
,
leav
in
g
1
3
9
ar
ticl
es
f
o
r
f
u
ll
-
tex
t
r
ev
iew.
Of
th
e
s
e,
3
3
ar
ticles
wer
e
ex
clu
d
ed
d
u
e
t
o
lack
o
f
f
u
ll
-
tex
t
av
ailab
ilit
y
d
esp
ite
r
ea
s
o
n
ab
le
ef
f
o
r
ts
to
o
b
tain
t
h
em
.
T
h
e
r
em
ain
in
g
1
0
6
ar
ticles
wer
e
ass
e
s
s
ed
f
o
r
elig
ib
ilit
y
,
with
4
7
b
ei
n
g
ex
cl
u
d
ed
f
o
r
n
o
t
a
d
eq
u
ately
a
d
d
r
e
s
s
in
g
th
e
r
esear
ch
q
u
esti
o
n
s
o
r
lack
in
g
clea
r
f
i
n
d
in
g
s
.
Ultim
ately
,
6
0
s
tu
d
ies w
er
e
in
clu
d
ed
in
th
e
f
in
al
s
y
n
th
esis
.
Fig
u
r
e
1
.
PR
I
SMA
f
lo
wch
ar
t
3)
Data
ex
tr
ac
tio
n
an
d
s
y
n
th
esis
:
A
s
tan
d
ar
d
ized
d
ata
ex
tr
ac
tio
n
f
o
r
m
was
u
s
ed
to
ca
p
t
u
r
e
k
e
y
in
f
o
r
m
atio
n
f
r
o
m
ea
ch
in
clu
d
ed
s
tu
d
y
.
T
h
e
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1211
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3
.
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.
E
f
f
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eness
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v
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us
m
a
chine le
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lg
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predict
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th
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s
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p
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ctio
n
u
s
in
g
d
if
f
er
en
t
d
atasets
.
W
ar
e
et
a
l.
[
7
]
co
n
d
u
cted
a
co
m
p
ar
ati
v
e
s
tu
d
y
o
f
v
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ch
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s
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th
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C
lev
elan
d
d
ataset.
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t
h
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ex
p
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im
en
tatio
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.
Sh
ek
h
ar
et
a
l.
[
8
]
u
tili
ze
d
s
ev
er
al
class
if
icatio
n
alg
o
r
ith
m
s
,
in
clu
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h
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ex
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ataset,
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Hem
alath
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an
d
Po
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[
9
]
co
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co
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p
a
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o
f
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Ozh
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Ku
c
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k
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lı
[
1
0
]
em
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XG
B
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m
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el
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Yil
m
az
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Hilal
[
1
1
]
p
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m
o
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el
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d
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o
r
ith
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s
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o
r
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.
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h
e
p
er
f
o
r
m
an
ce
o
f
th
ese
m
o
d
els
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as
s
es
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ed
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s
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e
h
ea
r
t
d
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d
ataset
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r
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Data
Po
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p
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f
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J
o
lo
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d
ar
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et
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l.
[
1
2
]
u
tili
ze
d
v
ar
io
u
s
d
ata
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if
icatio
n
m
o
d
els,
s
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ch
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to
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atic
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ter
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to
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r
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co
r
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ar
y
h
ea
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t
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e
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HD)
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lo
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-
Alizad
eh
San
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ataset,
f
r
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th
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I
n
th
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cted
b
y
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en
g
[
1
3
]
,
DT
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SVM,
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alg
o
r
ith
m
s
wer
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u
til
ized
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o
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els,
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e
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o
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ith
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d
e
m
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ated
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r
m
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ce
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n
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F
e
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t
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enha
ncing
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l a
lg
o
rit
hm
s
Ak
y
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l
an
d
Atilla
[
1
4
]
u
n
d
e
r
to
o
k
a
s
tu
d
y
to
co
m
p
ar
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r
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d
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o
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tin
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m
ac
h
in
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NB
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o
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ith
m
s
f
o
r
d
etec
tin
g
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T
h
ey
u
tili
ze
d
r
ec
u
r
s
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f
ea
tu
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e
elim
in
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u
p
led
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tify
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h
e
m
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s
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d
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im
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ativ
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ea
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es.
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h
e
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x
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e
c
o
n
d
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cted
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n
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o
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Statlo
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ataset
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et.
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em
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atasets
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o
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ith
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o
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ig
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f
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d
7
7
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7
8
%,
r
esp
ec
tiv
ely
.
R
ah
im
et
a
l.
[
1
5
]
tac
k
led
th
e
i
s
s
u
e
o
f
im
b
alan
ce
d
d
ata
b
y
em
p
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an
o
v
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am
p
lin
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iq
u
e.
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d
itio
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ally
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ey
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tili
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t
h
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m
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eth
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Evaluation Warning : The document was created with Spire.PDF for Python.
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52
In
d
o
n
esian
J
E
lec
E
n
g
&
C
o
m
p
Sci
,
Vo
l.
3
8
,
No
.
2
,
May
20
2
5
:
1
2
0
8
-
1
2
1
8
1212
f
ea
tu
r
e
s
elec
tio
n
was
in
teg
r
at
ed
.
Kh
u
r
an
a
et
a
l.
[
1
6
]
em
p
lo
y
ed
c
h
i
-
s
q
u
ar
e
an
d
in
f
o
r
m
atio
n
g
ain
,
r
esu
ltin
g
in
v
ar
ied
im
p
r
o
v
em
e
n
ts
in
p
r
ed
i
ctio
n
ac
cu
r
ac
y
ac
r
o
s
s
d
if
f
er
e
n
t
alg
o
r
ith
m
s
.
Ad
d
itio
n
ally
,
t
h
ey
em
p
lo
y
ed
f
i
v
e
d
is
tin
ct
f
ea
tu
r
e
s
elec
tio
n
tech
n
iq
u
es
in
th
eir
s
tu
d
y
.
T
h
eir
f
in
d
in
g
s
r
e
v
ea
led
th
at
SVM
o
u
tp
er
f
o
r
m
ed
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th
er
alg
o
r
ith
m
s
in
ter
m
s
o
f
p
r
ed
ict
iv
e
p
er
f
o
r
m
an
ce
.
T
h
e
y
ac
h
iev
ed
an
im
p
r
ess
iv
e
ac
cu
r
ac
y
r
at
e
o
f
8
3
.
4
1
p
e
r
ce
n
t.
T
h
ese
s
tu
d
ies
h
ig
h
lig
h
t
th
e
i
m
p
o
r
tan
ce
o
f
r
o
b
u
s
t
f
ea
tu
r
e
s
elec
tio
n
m
eth
o
d
s
in
im
p
r
o
v
in
g
th
e
p
r
e
d
ictiv
e
p
er
f
o
r
m
an
ce
o
f
ML
m
o
d
els f
o
r
C
VD.
3
.
3
.
E
ns
em
ble
lea
rning
m
o
d
els
T
h
ese
s
tu
d
ies
h
ig
h
lig
h
t
th
e
im
p
o
r
tan
ce
o
f
en
s
em
b
le
lear
n
i
n
g
in
C
VD
p
r
ed
ictio
n
h
as
d
em
o
n
s
tr
ated
n
o
tab
le
im
p
r
o
v
em
en
ts
in
ac
cu
r
ac
y
,
m
ak
in
g
it
a
p
r
o
m
is
in
g
a
p
p
r
o
ac
h
f
o
r
d
ev
el
o
p
in
g
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eliab
l
e
p
r
ed
ictiv
e
m
o
d
els
f
o
r
C
VD.
Mo
h
ap
atr
a
et
a
l.
[
1
7
]
em
p
lo
y
ed
a
s
tack
ed
en
s
em
b
le
lear
n
in
g
(
E
L
)
m
o
d
el
o
n
t
h
e
C
lev
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h
ea
r
t
d
is
ea
s
e
d
ataset
f
o
r
p
r
ed
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g
C
VD.
T
h
ey
u
tili
ze
d
ten
d
is
tin
ct
class
if
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s
a
s
b
ase
lear
n
er
s
an
d
ev
alu
ated
th
e
class
if
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n
p
er
f
o
r
m
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ce
o
f
th
e
p
r
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p
o
s
ed
E
L
m
o
d
el
ag
ain
s
t
th
ese
b
ase
cla
s
s
if
ier
s
.
T
h
e
r
esu
lts
r
ev
ea
led
th
at
th
e
s
u
g
g
ested
m
o
d
el
ac
h
iev
ed
an
im
p
r
ess
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e
a
cc
u
r
ac
y
o
f
9
2
%.
T
h
is
s
tu
d
y
u
n
d
e
r
s
co
r
es th
e
ef
f
ec
tiv
en
ess
o
f
EL
alg
o
r
ith
m
s
in
en
h
an
cin
g
class
if
icatio
n
p
e
r
f
o
r
m
an
ce
.
Das
an
d
Sin
h
a
[
1
8
]
p
r
o
p
o
s
ed
a
v
o
tin
g
-
b
ased
EL
m
o
d
el
f
o
r
p
r
ed
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g
C
VD.
T
h
eir
ex
p
er
i
m
en
ts
wer
e
co
n
d
u
cted
u
s
in
g
t
h
e
Statlo
g
h
ea
r
t
d
is
ea
s
e
d
atas
et.
R
em
ar
k
ab
ly
,
th
e
s
u
g
g
ested
m
o
d
el
ac
h
iev
ed
a
n
ac
cu
r
ac
y
o
f
9
0
.
7
4
%
wh
en
c
o
m
p
ar
ed
ag
ain
s
t
KNN,
SVM,
NB
,
DT
,
L
R
,
an
d
ar
tific
ial
n
eu
r
al
n
etwo
r
k
(
AN
N)
alg
o
r
ith
m
s
.
T
h
is
s
tu
d
y
d
em
o
n
s
tr
ated
th
at
E
L
m
o
d
els
o
f
f
er
h
ig
h
e
r
s
u
cc
ess
r
ates
co
m
p
ar
ed
to
class
ical
class
if
ier
s
.
Kh
an
et
a
l.
[
1
9
]
.
d
ev
el
o
p
ed
a
n
o
v
el
en
s
em
b
le
s
tack
in
g
class
if
ier
f
o
r
d
iag
n
o
s
in
g
an
d
p
r
e
d
ictin
g
C
VD
an
d
d
ia
b
etes.
T
h
e
m
o
d
el
d
em
o
n
s
tr
ated
s
u
p
er
io
r
p
er
f
o
r
m
an
ce
,
ac
h
ie
v
in
g
an
ac
cu
r
ac
y
o
f
8
8
.
7
1
%
f
o
r
C
VD,
s
u
r
p
ass
in
g
in
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iv
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al
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o
d
els
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u
ch
as
DT
(
8
5
.
2
3
%)
an
d
SVM
(
8
4
.
7
2
%).
Do
p
p
ala
et
a
l.
[
2
0
]
e
m
p
lo
y
e
d
an
EL
ap
p
r
o
ac
h
f
o
r
C
VD
p
r
ed
ictio
n
.
T
h
e
y
u
tili
ze
d
NB
,
RF
,
SVM,
an
d
XGB
alg
o
r
ith
m
s
as
b
ase
clas
s
if
ier
s
.
T
h
e
m
ajo
r
ity
v
o
tin
g
tech
n
iq
u
e
was
em
p
lo
y
ed
as
th
e
E
L
ap
p
r
o
ac
h
,
u
tili
zin
g
th
e
C
lev
elan
d
,
I
E
E
E
Data
p
o
r
t,
a
n
d
Me
n
d
eley
d
ata
ce
n
ter
d
at
asets
,
r
esp
ec
tiv
ely
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T
h
eir
p
r
o
p
o
s
ed
E
L
m
eth
o
d
ex
h
ib
ited
ac
c
u
r
ac
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r
ates
o
f
8
8
.
2
4
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9
3
.
3
9
%,
an
d
9
6
.
7
5
%.
No
tab
ly
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th
e
s
u
g
g
ested
m
o
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el
ac
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iev
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ig
h
er
class
if
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n
r
ates c
o
m
p
ar
ed
t
o
class
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clas
s
if
ier
s
.
3
.
4
.
Adv
a
nced
t
ec
hn
iqu
es a
nd
s
y
s
t
em
s
Gar
cía
-
Or
d
ás
et
a
l.
[
2
1
]
ap
p
lied
a
C
NN
alg
o
r
ith
m
f
o
r
p
r
ed
ict
in
g
C
VD.
T
h
e
y
em
p
lo
y
e
d
a
1
0
-
f
o
ld
C
V
ap
p
r
o
ac
h
to
m
itig
ate
r
an
d
o
m
n
ess
in
th
eir
ex
p
e
r
im
en
ts
.
T
h
eir
p
r
o
p
o
s
ed
m
o
d
el
o
u
tp
er
f
o
r
m
e
d
co
n
v
en
tio
n
al
ML
alg
o
r
ith
m
s
,
ac
h
iev
in
g
an
im
p
r
ess
iv
e
ac
cu
r
ac
y
r
ate
o
f
9
0
.
0
9
%.
A
n
o
v
el
s
y
s
tem
,
B
io
L
ea
r
n
er
,
h
as
b
ee
n
in
tr
o
d
u
ce
d
to
id
en
tify
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r
itical
b
io
m
ed
ica
l
m
ar
k
er
s
f
o
r
p
r
e
d
ictin
g
h
ea
r
t
d
is
ea
s
e.
E
m
p
lo
y
in
g
ML
tec
h
n
iq
u
es
lik
e
KNN
,
n
eu
r
al
n
etwo
r
k
s
,
an
d
SVM
,
B
io
L
ea
r
n
er
ac
h
iev
es
an
im
p
r
ess
iv
e
ac
cu
r
ac
y
r
ate
o
f
9
5
%
[
2
2
]
.
Ab
d
el
-
J
ab
er
et
a
l.
[
2
3
]
r
ec
u
r
r
e
n
t
n
eu
r
al
n
etwo
r
k
s
(
R
NNs)
ar
e
em
p
lo
y
ed
to
an
aly
ze
s
eq
u
en
tial
o
r
tim
e
-
s
er
ies
d
ata
ac
r
o
s
s
d
iv
er
s
e
d
o
m
ain
s
in
clu
d
in
g
n
atu
r
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g
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ag
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p
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s
in
g
,
s
p
ee
ch
r
ec
o
g
n
itio
n
,
m
eteo
r
o
lo
g
ical
d
ata
an
aly
s
is
,
an
d
p
r
ed
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e
h
ea
lth
ca
r
e.
3
.
5
.
I
nte
rnet
o
f
t
hin
g
s
a
nd
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er
g
ing
t
ec
hn
o
lo
g
ies
T
h
e
em
er
g
en
ce
o
f
in
ter
n
et
o
f
t
h
in
g
s
(
I
o
T
)
-
f
o
g
-
clo
u
d
-
b
ased
m
o
d
els
f
o
r
p
r
ed
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e
an
aly
tic
s
h
as
g
ain
ed
p
r
o
m
in
e
n
ce
d
u
e
t
o
th
eir
ad
v
an
tag
eo
u
s
f
ea
tu
r
es.
Fo
g
c
o
m
p
u
tin
g
d
e
m
o
n
s
tr
ates
ef
f
ic
ien
cy
in
h
an
d
lin
g
co
m
p
u
tatio
n
al
task
s
r
elate
d
to
h
ea
lth
ca
r
e
d
ata,
s
o
u
r
ce
d
f
r
o
m
v
ar
io
u
s
I
o
T
d
ev
ices
lik
e
wea
r
ab
le
s
en
s
o
r
s
,
alo
n
g
s
id
e
p
r
ev
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u
s
ly
s
to
r
ed
el
ec
tr
o
n
ic
clin
ical
d
ata
in
t
h
e
cl
o
u
d
[
2
4
]
.
A
s
m
ar
t
I
o
T
s
y
s
tem
d
esig
n
ed
t
o
p
r
ed
ict
h
ea
r
t
d
is
ea
s
e,
u
tili
zin
g
k
er
n
e
l
d
is
cr
im
in
an
t
a
n
aly
s
is
an
d
a
cu
s
to
m
ized
s
elf
-
ad
ap
tiv
e
B
ay
esian
alg
o
r
ith
m
,
ac
h
iev
es
an
ac
cu
r
ac
y
r
ate
o
f
9
0
%
[
2
5
]
.
Su
b
a
h
i
et
a
l.
[
2
5
]
p
r
o
p
o
s
ed
an
in
tellig
en
t
I
o
T
s
y
s
te
m
d
esig
n
ed
f
o
r
h
ea
r
t
d
is
ea
s
e
p
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n
,
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tili
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n
el
d
is
cr
im
in
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t
an
aly
s
is
an
d
an
ad
a
p
ted
s
elf
-
ad
ap
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B
ay
esian
alg
o
r
ith
m
,
ac
h
iev
in
g
a
9
0
% a
cc
u
r
ac
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r
ate
.
3
.
6
.
E
lect
ro
c
a
rdio
g
ra
m
a
nd
wea
ra
ble dev
ices
Hin
ai
et
a
l
.
[
2
6
]
co
n
d
u
ct
ed
a
s
tu
d
y
f
o
cu
s
ed
o
n
th
e
co
m
p
r
eh
en
s
iv
e
an
aly
s
is
o
f
r
esti
n
g
e
lectr
o
ca
r
d
io
g
r
am
(
E
C
G
)
s
ig
n
als
u
s
in
g
DL
m
eth
o
d
s
f
o
r
th
e
id
en
tific
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n
o
f
s
tr
u
ctu
r
al
ca
r
d
iac
ab
n
o
r
m
alities
.
T
h
eir
r
e
v
iew
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en
tifie
d
a
to
ta
l
o
f
1
2
ar
ticles:
3
a
r
ticles
ad
d
r
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ed
th
e
d
etec
tio
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f
lef
t
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ticle
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o
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n
lef
t
v
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lar
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y
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tr
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6
ar
ticles
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te
m
y
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ca
r
d
ial
in
f
ar
ctio
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,
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d
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ticles
f
o
cu
s
ed
o
n
s
tab
l
e
is
ch
em
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h
ea
r
t
d
is
ea
s
e.
T
h
e
ev
alu
atio
n
m
etr
ics
u
tili
ze
d
in
th
ese
s
tu
d
ies
in
c
lu
d
ed
AUC
(
ar
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n
d
er
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cu
r
v
e
)
an
d
ac
cu
r
ac
y
.
C
h
ak
r
a
b
ar
ti
et
a
l.
[
2
7
]
d
is
cu
s
s
ed
th
e
d
iag
n
o
s
tic
ca
p
ab
ilit
ies
o
f
wr
is
t
-
wo
r
n
d
ev
ices
in
d
etec
tin
g
v
a
r
io
u
s
d
is
ea
s
es,
n
o
tab
ly
ca
r
d
io
v
ascu
lar
c
o
n
d
itio
n
s
.
Ad
d
itio
n
ally
,
th
e
y
o
f
f
er
e
d
in
s
ig
h
ts
in
to
th
e
u
tili
za
tio
n
o
f
ML
alg
o
r
ith
m
s
f
o
r
an
aly
zi
n
g
wea
r
ab
le
d
ata.
T
h
e
s
tu
d
y
a
ls
o
h
ig
h
lig
h
ted
th
e
ex
is
tin
g
ch
allen
g
es p
er
tain
i
n
g
to
wea
r
ab
les an
d
m
e
d
ical
d
ata
an
aly
s
is
.
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
A
r
tifi
cia
l in
tellig
en
ce
a
p
p
r
o
a
ch
es fo
r
ca
r
d
io
va
s
cu
la
r
d
is
ea
s
e
p
r
ed
ictio
n
…
(
Ja
s
im
F
a
r
a
j
Ha
mma
d
i
)
1213
4.
DIS
CU
SS
I
O
N
T
h
is
s
y
s
tem
atic
liter
atu
r
e
r
ev
iew
an
aly
ze
d
6
0
s
tu
d
ies
to
ev
alu
ate
th
e
ap
p
licatio
n
o
f
v
ar
io
u
s
AI
tech
n
iq
u
es
in
p
r
e
d
ictin
g
C
VD.
T
h
e
r
ev
iew
f
o
cu
s
ed
o
n
id
en
tify
in
g
tr
e
n
d
s
in
AI
m
eth
o
d
o
lo
g
y
,
p
er
f
o
r
m
a
n
ce
o
u
tco
m
es,
an
d
g
ap
s
th
at
r
eq
u
ir
e
f
u
r
th
er
ex
p
lo
r
atio
n
.
T
h
e
d
is
c
u
s
s
io
n
h
er
e
in
teg
r
ates
k
ey
f
in
d
in
g
s
,
co
n
tex
tu
alize
s
th
em
with
in
th
e
b
r
o
ad
e
r
f
ield
,
an
d
o
u
tlin
es im
p
licatio
n
s
f
o
r
f
u
tu
r
e
r
esear
ch
an
d
clin
ical
p
r
a
ctice
.
4
.
1
.
I
nte
rpre
t
a
t
io
n ba
s
ed
o
n
k
ey
f
ind
ing
s
T
h
e
r
e
v
iew
r
e
v
ea
led
a
wid
e
r
an
g
e
o
f
A
I
m
et
h
o
d
s
u
tili
ze
d
i
n
C
VD
p
r
e
d
ictio
n
,
in
clu
d
in
g
tr
ad
itio
n
al
m
ac
h
in
e
lear
n
i
n
g
alg
o
r
ith
m
s
li
k
e
DT
,
RF
,
NB
,
L
R
,
ANN,
KNN,
an
d
SVM,
as
well
as
ad
v
a
n
ce
d
DL
tech
n
iq
u
es
s
u
ch
as
C
NN
s
an
d
h
y
b
r
i
d
m
o
d
els.
T
h
e
m
ajo
r
ity
o
f
s
tu
d
ies
em
p
lo
y
ed
d
atasets
f
r
o
m
well
-
k
n
o
wn
r
e
p
o
s
ito
r
ies
s
u
ch
as
th
e
UC
I
m
ac
h
in
e
lear
n
in
g
r
ep
o
s
ito
r
y
,
h
o
s
p
ital
r
ec
o
r
d
s
,
an
d
o
th
er
clin
ical
d
atab
ases
.
T
h
e
ac
cu
r
ac
y
o
f
th
ese
m
o
d
els v
ar
ied
s
ig
n
if
ica
n
tly
,
r
an
g
in
g
f
r
o
m
7
0
% to
1
0
0
%,
d
ep
en
d
in
g
o
n
th
e
d
ataset
s
ize,
f
ea
tu
r
e
s
elec
tio
n
tech
n
iq
u
es,
an
d
alg
o
r
ith
m
ic
c
o
m
p
lex
ity
.
Fig
u
r
e
3
illu
s
tr
ates
th
e
d
is
tr
ib
u
tio
n
o
f
AI
tech
n
i
q
u
e
s
u
s
ed
in
th
e
s
tu
d
ies
in
clu
d
ed
in
th
is
r
ev
iew.
T
h
e
p
r
ed
o
m
in
a
n
t
u
s
e
o
f
in
d
iv
id
u
al
class
if
ier
s
(
e.
g
.
,
SVM,
DT
,
an
d
L
R
)
s
u
g
g
ests
th
at
r
esear
ch
er
s
o
f
ten
f
o
cu
s
o
n
o
p
tim
izin
g
s
in
g
le
-
m
o
d
el
p
er
f
o
r
m
an
ce
.
Ho
wev
er
,
o
u
r
f
in
d
in
g
s
also
in
d
icate
a
g
r
o
win
g
tr
en
d
to
war
d
en
s
em
b
l
e
m
eth
o
d
s
,
wh
ich
co
m
b
i
n
e
m
u
ltip
le
m
o
d
el
p
r
ed
ictio
n
s
to
en
h
an
ce
ac
cu
r
ac
y
an
d
r
o
b
u
s
tn
ess
—
an
ap
p
r
o
ac
h
th
at
r
em
ai
n
s
u
n
d
e
r
u
tili
ze
d
in
th
e
c
u
r
r
en
t liter
atu
r
e
.
Fig
u
r
e
3
.
I
ll
u
s
tr
ates
th
e
v
ar
io
u
s
tech
n
iq
u
es a
n
d
m
eth
o
d
s
u
s
e
d
in
th
e
c
u
r
r
en
t
r
esear
ch
4
.
2
.
Co
m
pa
riso
n
wit
h pre
v
io
us
s
t
ud
ie
s
a
nd
s
t
u
dy
lim
it
a
t
io
ns
C
o
m
p
ar
ed
to
p
r
ev
i
o
u
s
r
ev
iews
,
th
is
s
tu
d
y
p
r
o
v
id
es
a
m
o
r
e
n
u
an
ce
d
u
n
d
er
s
tan
d
in
g
o
f
AI
'
s
r
o
le
in
C
VD
p
r
ed
ictio
n
.
O
u
r
r
e
v
iew
h
ig
h
li
g
h
ts
th
at
SVM
an
d
R
F
alg
o
r
ith
m
s
r
em
ain
p
o
p
u
lar
d
u
e
to
t
h
eir
r
o
b
u
s
tn
ess
an
d
r
elativ
ely
h
ig
h
p
er
f
o
r
m
a
n
ce
o
n
m
ed
iu
m
-
s
ized
d
atasets
.
Ho
wev
er
,
a
s
ig
n
if
ican
t
g
ap
i
d
en
ti
f
ied
ac
r
o
s
s
m
u
ltip
le
s
tu
d
ies
is
th
e
lack
o
f
en
s
em
b
l
e
lear
n
in
g
ap
p
r
o
ac
h
es.
W
h
il
e
en
s
em
b
le
tech
n
iq
u
es,
s
u
ch
as
R
F
co
m
b
in
ed
with
o
th
er
class
if
ier
s
,
wer
e
s
h
o
wn
t
o
en
h
an
ce
p
r
ed
ictiv
e
ac
cu
r
ac
y
(
as
in
th
e
s
tu
d
y
b
y
Asi
f
et
a
l.
[
4
1
]
,
wh
ich
r
ep
o
r
ted
9
8
.
1
5
%
ac
cu
r
ac
y
)
,
th
ey
r
em
ai
n
u
n
d
er
u
tili
ze
d
.
T
h
is
g
ap
p
r
es
en
ts
an
o
p
p
o
r
tu
n
ity
f
o
r
f
u
tu
r
e
r
esear
ch
to
ex
p
lo
r
e
th
e
b
en
e
f
its
o
f
in
te
g
r
atin
g
m
u
ltip
le
AI
m
o
d
els
to
im
p
r
o
v
e
p
r
ed
ictio
n
r
o
b
u
s
tn
ess
an
d
r
elia
b
ilit
y
.
Ad
d
itio
n
ally
,
m
an
y
s
tu
d
ies
r
elied
h
ea
v
ily
o
n
s
in
g
le,
s
m
all
d
atasets
,
s
u
ch
as
th
e
C
lev
elan
d
d
ataset,
wh
ich
lim
its
th
e
g
en
er
aliza
b
ilit
y
o
f
th
eir
f
in
d
i
n
g
s
.
Fo
r
in
s
tan
ce
,
Sar
r
a
et
a
l
.
[
5
2
]
an
d
Ko
lu
k
u
la
et
a
l.
[
5
3
]
u
s
ed
d
atasets
o
f
ap
p
r
o
x
im
ately
3
0
0
r
ec
o
r
d
s
,
wh
ich
m
ay
n
o
t
p
r
o
v
id
e
a
c
o
m
p
r
eh
e
n
s
iv
e
r
ep
r
esen
tatio
n
o
f
d
iv
er
s
e
p
atien
t
p
o
p
u
latio
n
s
.
T
h
is
r
elian
ce
o
n
s
m
all
d
atasets
an
d
lack
o
f
d
iv
e
r
s
e
d
ata
s
o
u
r
ce
s
r
estricts
th
e
ab
i
lity
o
f
th
ese
m
o
d
els
to
g
en
er
alize
ac
r
o
s
s
d
if
f
er
en
t
d
em
o
g
r
a
p
h
ic
an
d
clin
ical
s
ettin
g
s
.
4
.
3
.
I
m
pli
ca
t
i
o
ns
f
o
r
re
s
ea
rc
h
T
h
e
f
in
d
in
g
s
f
r
o
m
th
is
r
ev
iew
h
ig
h
lig
h
t
s
ev
er
al
im
p
licatio
n
s
f
o
r
b
o
th
r
esear
ch
a
n
d
clin
ic
al
p
r
ac
tice.
Firstl
y
,
th
e
h
ig
h
ac
c
u
r
ac
y
ac
h
i
ev
ed
b
y
s
o
m
e
DL
m
o
d
els,
s
u
c
h
as
C
NNs
Am
ar
b
ay
asg
alan
et
a
l.
[3
5
]
an
d
h
y
b
r
id
ap
p
r
o
ac
h
es
co
m
b
in
i
n
g
n
e
u
r
al
n
etwo
r
k
s
with
f
u
zz
y
in
f
er
e
n
c
e
s
y
s
tem
s
(
e.
g
.
,
A.
Nan
cy
et
a
l.
[
54
]
,
s
u
g
g
ests
a
p
r
o
m
is
in
g
d
ir
ec
tio
n
f
o
r
d
ev
el
o
p
in
g
m
o
r
e
ac
cu
r
ate
an
d
p
er
s
o
n
alize
d
C
VD
r
is
k
p
r
e
d
ictio
n
to
o
ls
.
Ho
wev
er
,
th
e
r
ev
iew
also
u
n
d
er
s
co
r
es
th
e
n
e
ed
f
o
r
lar
g
e
r
,
m
o
r
e
d
iv
er
s
e
d
atasets
to
en
h
an
ce
m
o
d
el
r
eliab
il
ity
an
d
ap
p
licab
ilit
y
in
r
ea
l
-
wo
r
ld
s
ettin
g
s
.
Stu
d
ies
lik
e
th
o
s
e
b
y
Patr
o
et
a
l.
[3
1
]
an
d
Ulah
et
a
l.
[3
2
]
in
d
icat
e
th
at
u
s
in
g
s
m
all,
h
o
m
o
g
en
eo
u
s
d
atasets
lim
its
th
e
m
o
d
els'
ef
f
ec
tiv
en
ess
in
b
r
o
ad
e
r
clin
ical
ap
p
licatio
n
s
.
T
h
er
ef
o
r
e,
f
u
tu
r
e
r
esear
ch
s
h
o
u
ld
f
o
cu
s
o
n
in
teg
r
atin
g
m
o
r
e
co
m
p
r
eh
en
s
iv
e
d
a
tasets
an
d
ex
p
lo
r
in
g
d
ata
au
g
m
en
tatio
n
tech
n
iq
u
es
0
2
4
6
8
10
XG
Bo
o
s
t
A
N
N
S
V
M
DN
N
RF
KN
N
DT
NB
LR
NB
CN
N
ML
P
DN
N
RBF
S
G
L
V
NN
K-me
an
s
N
um
be
r
o
f
St
ud
i
e
s
A
I
m
e
t
ho
ds
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
8
,
No
.
2
,
May
20
2
5
:
1
2
0
8
-
1
2
1
8
1214
to
im
p
r
o
v
e
m
o
d
el
tr
ain
in
g
an
d
v
alid
atio
n
p
r
o
ce
s
s
es.
Fu
r
th
er
m
o
r
e,
t
h
e
s
ig
n
if
ican
t
r
elian
ce
o
n
s
in
g
le
class
if
ier
s
,
as
o
p
p
o
s
ed
to
en
s
em
b
le
m
eth
o
d
s
,
s
u
g
g
ests
a
p
o
ten
tial
ar
ea
f
o
r
im
p
r
o
v
em
en
t.
E
n
s
em
b
le
lear
n
in
g
tech
n
i
q
u
es,
wh
ich
co
m
b
in
e
p
r
ed
ictio
n
s
f
r
o
m
m
u
ltip
le
m
o
d
els,
h
av
e
b
ee
n
s
h
o
wn
to
en
h
a
n
ce
p
r
ed
ictiv
e
a
cc
u
r
ac
y
an
d
m
o
d
el
r
o
b
u
s
tn
ess
.
Fu
tu
r
e
s
tu
d
ies s
h
o
u
ld
in
v
esti
g
ate
th
e
u
tili
ty
o
f
th
ese
m
eth
o
d
s
in
C
VD
p
r
ed
ictio
n
.
4
.
4
.
L
im
it
a
t
io
ns
a
nd
f
uture
re
s
ea
rc
h direc
t
io
ns
T
h
is
r
ev
iew
h
as
s
ev
er
al
lim
i
tat
io
n
s
th
at
s
h
o
u
ld
b
e
co
n
s
id
er
ed
wh
en
in
ter
p
r
etin
g
th
e
f
in
d
in
g
s
.
First,
th
e
ex
clu
s
io
n
o
f
n
o
n
-
E
n
g
lis
h
s
tu
d
ies
m
ay
h
av
e
r
esu
lted
in
th
e
o
m
is
s
io
n
o
f
r
elev
an
t
r
esear
ch
,
p
o
ten
tially
in
tr
o
d
u
cin
g
lan
g
u
ag
e
b
ias.
Ad
d
itio
n
ally
,
t
h
e
v
ar
ia
b
ilit
y
in
s
tu
d
y
q
u
ality
,
p
ar
ticu
lar
ly
co
n
ce
r
n
in
g
d
atas
et
s
ize
an
d
r
e
p
o
r
tin
g
tr
an
s
p
ar
en
cy
,
p
o
s
es
ch
allen
g
es
in
d
ir
ec
tly
co
m
p
ar
in
g
AI
m
o
d
el
p
er
f
o
r
m
an
ce
.
Ma
n
y
s
tu
d
ies
d
id
n
o
t
s
p
ec
if
y
th
ei
r
f
ea
tu
r
e
s
elec
tio
n
p
r
o
ce
s
s
es
o
r
p
r
o
v
id
e
s
u
f
f
icien
t
d
etails
o
n
h
y
p
e
r
p
ar
am
eter
tu
n
in
g
,
wh
ic
h
co
u
ld
af
f
ec
t
t
h
e
r
ep
r
o
d
u
cib
ilit
y
an
d
g
en
e
r
aliza
b
ilit
y
o
f
th
eir
f
in
d
in
g
s
5.
CO
NCLU
SI
O
N
T
h
is
s
y
s
tem
atic
li
ter
atu
r
e
r
ev
iew
co
n
d
u
cted
a
co
m
p
r
e
h
en
s
iv
e
an
aly
s
is
o
f
ex
is
tin
g
AI
m
eth
o
d
o
lo
g
ies,
co
m
p
ar
in
g
a
n
d
ev
alu
atin
g
th
e
m
to
id
en
tify
th
e
m
o
s
t
a
cc
u
r
at
e
an
d
ef
f
icie
n
t
tech
n
iq
u
es
f
o
r
p
r
ed
ictin
g
C
VD.
T
h
e
r
ev
iew
ex
am
in
ed
ap
p
r
o
x
im
at
ely
s
ev
en
teen
m
eth
o
d
s
an
d
a
lg
o
r
ith
m
s
ac
r
o
s
s
6
0
s
tu
d
ies,
d
o
cu
m
e
n
tin
g
th
eir
p
er
f
o
r
m
an
ce
,
ac
cu
r
ac
y
,
an
d
th
e
g
ap
s
in
cu
r
r
en
t r
esear
c
h
.
T
h
e
f
in
d
in
g
s
d
em
o
n
s
tr
ate
th
at
AI
-
b
ased
tech
n
o
lo
g
ies
h
av
e
s
ig
n
if
ican
t
p
o
ten
tial
to
r
ev
o
lu
tio
n
ize
h
ea
lth
ca
r
e,
p
a
r
ticu
lar
ly
in
en
h
an
ci
n
g
th
e
a
cc
u
r
ac
y
o
f
d
is
ea
s
e
p
r
ed
ictio
n
a
n
d
th
e
p
er
s
o
n
aliza
tio
n
o
f
th
e
r
ap
y
r
ec
o
m
m
e
n
d
ati
o
n
s
.
Am
o
n
g
th
e
alg
o
r
ith
m
s
a
n
aly
ze
d
,
R
F,
SVM,
C
NN,
an
d
L
R
em
er
g
ed
as
th
e
m
o
s
t
f
r
eq
u
e
n
tly
u
tili
ze
d
tec
h
n
iq
u
es.
T
h
ese
m
eth
o
d
s
,
p
ar
t
icu
lar
ly
C
NNs
an
d
h
y
b
r
id
m
o
d
els
th
at
co
m
b
in
e
n
eu
r
al
n
etwo
r
k
s
with
f
u
zz
y
in
f
er
en
ce
s
y
s
tem
s
,
h
av
e
s
h
o
wn
h
ig
h
ac
cu
r
ac
y
,
s
u
g
g
esti
n
g
a
p
r
o
m
is
in
g
d
ir
ec
t
io
n
f
o
r
d
ev
elo
p
in
g
m
o
r
e
p
r
ec
is
e
an
d
p
e
r
s
o
n
alize
d
C
VD
r
is
k
p
r
e
d
ictio
n
t
o
o
ls
.
Ho
wev
er
,
th
e
s
tu
d
y
also
r
ev
ea
led
s
ev
er
al
cr
itical
g
a
p
s
in
th
e
cu
r
r
e
n
t
r
esear
ch
lan
d
s
ca
p
e.
Ma
n
y
s
tu
d
ies
ten
d
e
d
to
f
o
cu
s
o
n
i
n
d
iv
id
u
al
class
if
ie
r
s
,
wh
ich
lim
ited
th
e
p
o
ten
tial
f
o
r
m
ax
im
izin
g
p
r
e
d
ictiv
e
ac
c
u
r
ac
y
.
Ad
d
itio
n
ally
,
h
y
p
er
p
ar
am
eter
o
p
tim
izatio
n
a
k
ey
f
ac
to
r
in
e
n
h
an
ci
n
g
m
o
d
el
p
er
f
o
r
m
a
n
ce
was
o
f
ten
o
v
e
r
lo
o
k
ed
,
p
o
ten
tially
r
estrictin
g
th
e
ef
f
ec
tiv
en
ess
o
f
th
ese
p
r
e
d
ictiv
e
m
o
d
els.
A
s
ig
n
if
ican
t
lim
itatio
n
id
en
tifie
d
in
th
e
r
ev
iewe
d
s
tu
d
ies
was
th
e
r
elian
ce
o
n
s
m
all,
h
o
m
o
g
en
e
o
u
s
d
atasets
,
wh
ich
af
f
ec
ts
th
e
g
en
e
r
aliza
b
ilit
y
an
d
r
o
b
u
s
tn
ess
o
f
AI
m
o
d
els
i
n
b
r
o
ad
e
r
clin
ical
ap
p
licatio
n
s
.
T
h
is
lim
ita
tio
n
h
i
g
h
lig
h
ts
th
e
n
ee
d
f
o
r
lar
g
er
,
m
o
r
e
d
i
v
er
s
e
d
atasets
an
d
th
e
e
x
p
lo
r
atio
n
o
f
d
ata
a
u
g
m
en
tatio
n
tech
n
iq
u
es
to
i
m
p
r
o
v
e
m
o
d
el
tr
ain
in
g
an
d
v
alid
atio
n
p
r
o
ce
s
s
es.
Fu
r
th
er
m
o
r
e
,
th
e
is
s
u
e
o
f
im
b
a
lan
ce
d
d
atasets
lead
in
g
to
b
iased
p
r
ed
ictio
n
s
f
o
r
m
in
o
r
it
y
clas
s
es
was
a
co
m
m
o
n
ch
allen
g
e.
T
h
is
u
n
d
er
s
co
r
es
t
h
e
n
ec
ess
ity
o
f
im
p
lem
e
n
tin
g
tech
n
i
q
u
es
s
u
ch
as
r
esam
p
lin
g
,
co
s
t
-
s
en
s
itiv
e
lear
n
in
g
,
a
n
d
en
s
em
b
le
m
eth
o
d
s
to
im
p
r
o
v
e
m
o
d
el
b
alan
ce
an
d
r
eliab
ilit
y
.
L
o
o
k
in
g
f
o
r
war
d
,
f
u
t
u
r
e
r
esear
ch
s
h
o
u
ld
f
o
c
u
s
o
n
s
ev
er
al
k
e
y
a
r
ea
s
.
First,
th
er
e
is
a
cr
itical
n
ee
d
to
ex
p
lo
r
e
an
d
d
ev
elo
p
e
x
p
lain
ab
le
AI
(
XAI
)
tech
n
iq
u
es
th
at
m
ak
e
c
o
m
p
le
x
AI
m
o
d
els
m
o
r
e
tr
an
s
p
a
r
en
t
an
d
u
n
d
e
r
s
tan
d
ab
le
to
clin
i
cian
s
.
T
h
is
will
b
e
ess
en
tial
f
o
r
g
ai
n
in
g
tr
u
s
t
a
n
d
en
s
u
r
in
g
th
e
p
r
ac
tical
ap
p
licatio
n
o
f
th
ese
m
o
d
els
in
clin
ic
al
s
ettin
g
s
.
Seco
n
d
,
r
esear
ch
s
h
o
u
ld
p
r
io
r
itize
th
e
co
llectio
n
an
d
in
te
g
r
atio
n
o
f
d
iv
er
s
e
d
atasets
,
in
clu
d
in
g
m
u
lt
i
-
ce
n
ter
an
d
m
u
lti
-
m
o
d
al
d
ata,
to
im
p
r
o
v
e
t
h
e
r
o
b
u
s
tn
ess
an
d
g
en
er
aliza
b
ilit
y
o
f
AI
m
o
d
els.
Fin
ally
,
th
e
p
o
t
en
tial
o
f
en
s
em
b
le
lear
n
in
g
m
et
h
o
d
s
to
a
d
d
r
ess
th
e
lim
itatio
n
s
o
f
in
d
iv
i
d
u
al
class
if
ier
s
s
h
o
u
ld
b
e
f
u
r
th
er
i
n
v
esti
g
ated
,
p
ar
ticu
lar
l
y
in
m
an
ag
in
g
d
ata
im
b
alan
ce
an
d
im
p
r
o
v
in
g
p
r
e
d
ictiv
e
ac
c
u
r
ac
y
.
B
y
f
o
cu
s
in
g
o
n
th
ese
ar
ea
s
,
th
e
r
esear
ch
co
m
m
u
n
ity
ca
n
d
ev
el
o
p
AI
to
o
ls
th
at
n
o
t
o
n
ly
p
r
ed
ict
C
VD
wi
th
g
r
ea
ter
ac
cu
r
ac
y
b
u
t
also
s
ig
n
if
ican
tly
im
p
r
o
v
e
p
atien
t o
u
tco
m
es th
r
o
u
g
h
m
o
r
e
p
er
s
o
n
alize
d
a
n
d
eq
u
itab
le
h
ea
lth
ca
r
e
s
o
lu
tio
n
s
.
AP
P
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le
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Dis
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ataset
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g
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AI
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r
ed
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VD
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is
ea
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t
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a
n
d
P
e
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r
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s
[
2
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C
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2
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H
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H
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1215
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.
Dis
p
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ataset
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o
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d
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v
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p
in
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AI
m
o
d
el
to
p
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C
VD
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is
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(
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W
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7
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3
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d
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[
3
4
]
C
l
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H
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3
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[
3
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7
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[
3
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9
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8
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8
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8
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8
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2
0
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[
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]
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k
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[
3
9
]
D
a
t
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t
a
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s
3
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3
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9
7
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t
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l
.
[
4
0
]
D
a
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t
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t
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n
s
9
1
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d
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s
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9
2
%
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si
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g
l
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sm
a
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w
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h
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si
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.
[
4
1
]
T
h
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d
a
t
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se
t
s fr
o
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a
g
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e
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t
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l
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t
h
m
9
8
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1
5
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e
f
o
c
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s
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p
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c
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o
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[
4
2
]
To
w
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t
a
s
e
t
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s
3
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n
d
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o
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d
2
9
9
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e
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d
s
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M
9
8
.
7
5
%
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[
4
3
]
D
a
t
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t
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t
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n
s
3
0
3
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9
0
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7
%
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n
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e
d
t
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f
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r
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h
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r
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v
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d
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t
a
set
s
.
Y
o
u
s
e
f
i
[
4
4
]
D
a
t
a
s
e
t
o
f
2
9
4
p
e
o
p
l
e
DT
8
3
%
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l
l
c
l
a
s
si
f
i
c
a
t
i
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t
e
c
h
n
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s re
l
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t
.
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