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h
in
d
er
clin
ical
ad
o
p
tio
n
.
Desp
ite
n
u
m
er
o
u
s
s
tu
d
ies
o
n
ML
-
b
ased
C
VD
p
r
ed
ictio
n
,
n
o
co
m
p
r
eh
en
s
iv
e
s
y
n
th
esis
ev
alu
ates
th
e
co
m
b
in
ed
ef
f
ec
ts
o
f
p
r
ep
r
o
ce
s
s
in
g
,
f
ea
tu
r
e
s
elec
tio
n
,
alg
o
r
ith
m
ch
o
ice,
an
d
in
ter
p
r
etab
ilit
y
o
n
m
o
d
el
p
er
f
o
r
m
an
ce
.
T
h
is
s
y
s
tem
atic
liter
atu
r
e
r
ev
iew
(
SLR)
o
f
s
tu
d
ies
f
r
o
m
2
0
1
3
–
2
0
2
3
aim
s
to
ad
d
r
ess
th
is
g
ap
,
p
r
o
v
id
in
g
a
h
o
lis
tic
u
n
d
er
s
tan
d
in
g
o
f
m
eth
o
d
o
lo
g
ical
tr
en
d
s
,
ch
allen
g
es,
an
d
o
p
p
o
r
tu
n
ities
f
o
r
im
p
r
o
v
in
g
p
r
ed
ictiv
e
ac
cu
r
ac
y
an
d
clin
ical
ap
p
licab
ilit
y
.
T
h
is
s
y
s
tem
atic
r
ev
iew
in
v
esti
g
ates
ML
ap
p
licatio
n
s
in
C
VD
p
r
ed
ictio
n
to
d
ev
elo
p
an
ef
f
ec
tiv
e
r
is
k
ass
ess
m
en
t
f
r
am
ewo
r
k
,
em
p
lo
y
in
g
a
SLR
to
d
ef
in
e
s
co
p
e,
id
en
tify
an
d
ev
alu
ate
r
elev
an
t
s
tu
d
ies,
an
d
s
y
n
th
esize
o
u
tco
m
es
b
o
th
q
u
alitativ
ely
an
d
q
u
an
titativ
ely
.
T
h
e
r
ev
iew
s
p
ec
if
ically
ad
d
r
ess
es
th
e
f
o
llo
win
g
r
esear
ch
q
u
esti
o
n
s
:
R
Q1
: Wh
at
o
b
s
tacle
s
d
o
r
esear
ch
er
s
f
ac
e
wh
en
im
p
lem
en
tin
g
m
ac
h
in
e
lear
n
in
g
f
o
r
ca
r
d
io
v
ascu
lar
p
r
ed
ictio
n
,
an
d
h
o
w
h
av
e
s
u
cc
ess
f
u
l stu
d
ies o
v
er
co
m
e
th
ese
ch
allen
g
es?
R
Q2
:
W
h
ich
p
h
y
s
io
lo
g
ical,
b
eh
av
io
r
al,
an
d
d
em
o
g
r
ap
h
ic
f
ac
to
r
s
p
r
o
v
e
m
o
s
t
s
ig
n
if
ican
t
in
m
ac
h
in
e
lear
n
in
g
-
b
ased
ca
r
d
io
v
ascu
lar
r
is
k
ass
ess
m
en
t?
R
Q3
:
W
h
at
p
r
ep
r
o
ce
s
s
in
g
an
d
d
ata
p
r
o
ce
s
s
in
g
m
eth
o
d
s
b
est
im
p
r
o
v
e
p
r
ed
ictio
n
ac
cu
r
ac
y
in
ML
-
b
ased
C
VD
p
r
ed
ictio
n
?
R
Q4
:
W
h
ich
m
ac
h
in
e
lear
n
in
g
tech
n
iq
u
es
d
em
o
n
s
tr
ate
s
u
p
er
io
r
p
er
f
o
r
m
an
ce
in
ca
r
d
io
v
ascu
lar
p
r
ed
ictio
n
task
s
?
R
Q5
: Wh
at
m
eth
o
d
s
b
est ev
alu
ate
th
e
clin
ical
r
eliab
ilit
y
o
f
m
ac
h
in
e
lear
n
in
g
p
r
ed
ictio
n
s
?
T
h
e
p
r
ed
ictiv
e
s
u
cc
ess
o
f
ML
in
C
VD
r
is
k
ass
ess
m
en
t
d
ep
en
d
s
n
o
t
o
n
ly
o
n
alg
o
r
ith
m
ic
s
o
p
h
is
ticatio
n
b
u
t
also
o
n
ap
p
r
o
p
r
iate
p
r
ep
r
o
ce
s
s
in
g
,
f
ea
tu
r
e
en
g
in
ee
r
in
g
,
an
d
in
ter
p
r
etab
ilit
y
.
T
h
ese
in
ter
co
n
n
ec
ted
elem
en
ts
co
llectiv
ely
d
eter
m
in
e
wh
eth
er
ML
m
o
d
els
ca
n
d
eliv
er
clin
ically
r
eliab
le,
ex
p
lain
ab
le,
an
d
ac
tio
n
ab
le
p
r
ed
ictio
n
s
,
f
ac
ilit
atin
g
th
eir
tr
an
s
latio
n
in
to
r
ea
l
-
wo
r
ld
h
ea
lth
ca
r
e
s
ettin
g
s
.
T
h
e
r
em
ain
d
er
o
f
th
is
ar
ticle
is
o
r
g
an
ized
as
f
o
llo
ws.
Sectio
n
2
d
etails
th
e
m
eth
o
d
o
lo
g
y
o
f
th
e
r
ev
iew.
Sectio
n
3
p
r
esen
ts
r
esu
lt
an
d
d
is
cu
s
s
th
e
f
in
d
in
g
s
,
h
ig
h
lig
h
tin
g
m
eth
o
d
o
lo
g
ical
tr
en
d
s
,
ch
allen
g
es,
an
d
o
p
p
o
r
tu
n
ities
co
m
p
ar
ed
to
p
r
ev
io
u
s
s
tu
d
ies.
Sectio
n
4
co
n
clu
d
es
with
k
ey
im
p
licatio
n
s
,
lim
itatio
n
s
,
an
d
d
ir
ec
tio
n
s
f
o
r
f
u
tu
r
e
r
esear
ch
to
ad
v
an
ce
ML
-
b
ased
C
VD
r
is
k
p
r
ed
ictio
n
.
2.
RE
S
E
ARCH
M
E
T
H
O
D
T
h
is
r
esear
ch
b
eg
an
with
th
e
f
o
r
m
u
latio
n
o
f
r
esear
ch
q
u
esti
o
n
s
an
d
th
e
d
elin
ea
tio
n
o
f
th
e
s
tu
d
y
s
co
p
e,
wh
ich
in
f
o
r
m
ed
th
e
s
elec
tio
n
o
f
k
ey
wo
r
d
s
f
o
r
liter
atu
r
e
r
etr
iev
al.
Fo
u
r
m
ajo
r
d
atab
ases
:
AC
M
Dig
ital
L
ib
r
ar
y
,
I
E
E
E
Xp
lo
r
e,
Scien
ce
Dir
ec
t,
an
d
Sco
p
u
s
,
wer
e
s
y
s
tem
atica
lly
s
ea
r
ch
ed
.
R
etr
iev
ed
s
tu
d
ies
wer
e
s
u
b
jecte
d
to
a
r
ig
o
r
o
u
s
f
o
u
r
-
s
tag
e
s
cr
ee
n
in
g
p
r
o
ce
s
s
,
in
clu
d
in
g
k
ey
wo
r
d
f
ilter
in
g
,
titl
e
ass
ess
m
en
t,
co
n
ten
t
ev
alu
atio
n
,
an
d
r
eliab
ilit
y
v
er
if
icatio
n
,
to
en
s
u
r
e
th
e
in
clu
s
io
n
o
f
s
tu
d
ies
m
o
s
t
p
er
tin
en
t
to
th
e
r
esear
ch
o
b
jectiv
es.
A
s
y
s
tem
atic
liter
atu
r
e
r
ev
iew
was
co
n
d
u
cted
to
ex
am
in
e
m
ac
h
in
e
lear
n
in
g
ap
p
licatio
n
s
in
ca
r
d
io
v
ascu
lar
d
is
ea
s
e
r
is
k
p
r
ed
ictio
n
.
Fo
llo
win
g
estab
lis
h
ed
p
r
o
to
co
ls
,
th
e
r
ev
iew
h
ig
h
lig
h
ted
k
ey
p
atter
n
s
,
d
ata
in
ter
p
r
etatio
n
ch
allen
g
es,
an
d
in
s
ig
h
ts
o
n
r
is
k
f
ac
to
r
s
,
m
o
d
ellin
g
ap
p
r
o
ac
h
es,
an
d
clin
ical
v
alid
atio
n
[
7
]
,
em
p
h
asizin
g
AI
’
s
r
o
le
in
p
r
ep
r
o
ce
s
s
in
g
an
d
d
ee
p
lear
n
in
g
f
o
r
d
etec
tin
g
s
u
b
tle
r
is
k
in
d
icato
r
s
.
2
.
1
.
Sea
rc
h
s
t
ra
t
eg
y
A
s
y
s
tem
atic
s
ea
r
ch
was
p
er
f
o
r
m
ed
ac
r
o
s
s
AC
M
Dig
ital
L
ib
r
ar
y
,
I
E
E
E
Xp
lo
r
e,
Scien
ce
Dir
ec
t,
an
d
Sco
p
u
s
,
g
u
id
ed
b
y
r
esear
ch
q
u
esti
o
n
s
(
R
Q1
–
R
Q5
)
.
Sear
ch
s
tr
in
g
s
wer
e
co
n
s
tr
u
cted
u
s
in
g
p
r
ed
ef
in
ed
k
ey
wo
r
d
s
,
s
y
n
o
n
y
m
s
,
an
d
B
o
o
lean
o
p
er
ato
r
s
.
a.
Me
d
ical
co
n
tex
t:
C
VD
-
r
elate
d
s
tu
d
ies
wer
e
id
en
tifie
d
u
s
in
g
k
ey
wo
r
d
s
s
u
ch
as
“c
ar
d
io
v
ascu
lar
d
is
ea
s
e,
”
“CVD,”
“p
r
ed
ictio
n
,
”
“f
o
r
ec
asti
n
g
,
”
co
m
b
in
ed
as:
X
= {(
C
a
r
d
io
va
s
cu
la
r
Dis
ea
s
e
OR
C
V
D)
A
N
D
(
P
r
ed
ictio
n
OR
P
r
ed
ictin
g
OR
P
r
ed
ictive
OR
F
o
r
ec
a
s
t O
R
F
o
r
ec
a
s
tin
g
)
}
.
b.
T
ec
h
n
ical
co
n
te
x
t:
ML
s
tu
d
ies we
r
e
tar
g
eted
u
s
in
g
“m
ac
h
in
e
lear
n
in
g
”
a
n
d
“d
ee
p
lear
n
i
n
g
”
:
Y =
{M
a
ch
in
e
Lea
r
n
in
g
OR
Dee
p
Lea
r
n
in
g
}
Fo
r
ea
ch
RQ
,
X
,
an
d
Y
wer
e
co
m
b
in
ed
with
ad
d
itio
n
al
ter
m
s
(
e.
g
.
,
p
r
o
b
lem
,
f
ac
to
r
s
,
p
r
o
ce
s
s
,
alg
o
r
ith
m
,
ac
cu
r
a
cy
)
.
C
h
allen
g
es
s
u
ch
as
in
co
n
s
is
ten
t
ter
m
in
o
lo
g
y
,
m
is
s
in
g
ML
r
ef
er
en
ce
s
,
an
d
b
r
o
ad
ly
ca
teg
o
r
ized
s
tu
d
ies
wer
e
r
eso
lv
ed
u
s
in
g
s
y
n
o
n
y
m
lis
ts
,
iter
ativ
e
s
ea
r
ch
es,
an
d
m
an
u
al
s
cr
ee
n
in
g
,
co
n
s
is
ten
t
with
s
y
s
tem
atic
r
ev
iew
p
r
o
to
co
ls
.
Evaluation Warning : The document was created with Spire.PDF for Python.
I
SS
N
:
2
0
8
8
-
8
7
0
8
I
n
t J E
lec
&
C
o
m
p
E
n
g
,
Vo
l.
1
6
,
No
.
2
,
Ap
r
il
20
2
6
:
9
1
4
-
923
916
2
.
2
.
F
ilte
ring
p
ro
ce
s
s
Pra
ctica
l
ch
allen
g
es,
s
u
ch
as
in
co
n
s
is
ten
t
ter
m
in
o
lo
g
y
a
n
d
lab
elin
g
o
f
ML
s
tu
d
ies,
wer
e
ad
d
r
ess
ed
th
r
o
u
g
h
s
y
n
o
n
y
m
lis
ts
,
iter
ativ
e
s
ea
r
ch
es,
an
d
m
an
u
al
r
ev
iew
.
Ar
ticles
wer
e
s
eq
u
en
tially
f
ilter
ed
th
r
o
u
g
h
f
o
u
r
s
tag
es: k
ey
wo
r
d
-
b
ased
s
ea
r
c
h
,
titl
e
r
elev
an
ce
,
ab
s
tr
ac
t e
v
alu
atio
n
,
an
d
f
u
ll
-
tex
t a
s
s
ess
m
en
t.
a.
I
n
itial
k
ey
wo
r
d
-
b
ased
s
ea
r
ch
p
h
ase
:
T
h
e
in
itial
s
ea
r
ch
ac
r
o
s
s
AC
M,
I
E
E
E
,
Scien
ce
Dir
ec
t,
an
d
Sco
p
u
s
d
atab
ases
id
en
tifie
d
6
,
2
6
5
a
r
ticles u
s
in
g
p
r
ed
ef
i
n
ed
k
e
y
wo
r
d
s
alig
n
ed
with
th
e
r
esear
ch
o
b
j
ec
tiv
es.
b.
T
itle
r
elev
an
ce
f
iltra
tio
n
p
h
a
s
e
:
T
itles
wer
e
s
cr
ee
n
ed
f
o
r
ex
p
licit
r
elev
an
ce
an
d
clar
it
y
in
ad
d
r
ess
in
g
ca
r
d
io
v
ascu
lar
d
is
ea
s
e
p
r
ed
ictio
n
with
m
ac
h
i
n
e
lear
n
in
g
.
T
h
i
s
s
tep
r
ed
u
ce
d
th
e
p
o
o
l t
o
6
2
1
ar
ticles.
c.
Ab
s
tr
ac
t
an
aly
s
is
ev
alu
atio
n
p
h
ase
:
Ab
s
tr
ac
ts
o
f
th
e
s
h
o
r
tlis
ted
s
tu
d
ies
wer
e
ex
am
i
n
ed
to
co
n
f
ir
m
s
u
b
s
tan
tiv
e
m
eth
o
d
o
lo
g
ical
ali
g
n
m
en
t a
n
d
d
o
cu
m
e
n
t a
v
ailab
ilit
y
,
n
ar
r
o
win
g
th
e
s
elec
tio
n
t
o
2
5
6
ar
ticles.
d.
Fu
ll
-
tex
t
co
n
ten
t
ev
alu
atio
n
p
h
ase
:
C
o
m
p
r
eh
e
n
s
iv
e
r
e
v
ie
w
o
f
f
u
ll
-
tex
t
m
an
u
s
cr
ip
ts
e
n
s
u
r
ed
co
n
tex
tu
al
v
alid
ity
,
m
et
h
o
d
o
lo
g
ical
s
o
u
n
d
n
ess
,
an
d
co
m
p
lete
alig
n
m
e
n
t
with
t
h
e
r
esear
c
h
q
u
esti
o
n
s
,
lead
in
g
to
th
e
f
in
al
in
clu
s
io
n
o
f
9
1
ar
ticles in
th
e
s
y
s
tem
atic
r
ev
iew.
2
.
3
.
Da
t
a
e
x
t
ra
ct
i
o
n
Data
ex
tr
ac
tio
n
in
v
o
lv
es
s
elec
tin
g
r
elev
an
t
s
tu
d
ies
f
o
r
an
aly
s
is
an
d
d
o
cu
m
en
tin
g
th
em
ac
co
r
d
in
g
to
r
ev
iew
p
r
o
to
co
ls
,
en
s
u
r
in
g
ex
tr
ac
ted
d
ata
alig
n
with
th
e
tar
g
eted
s
tu
d
y
ca
teg
o
r
ies
[
8
]
.
T
h
is
p
h
ase
r
eq
u
ir
es
ca
r
ef
u
l
co
n
s
id
er
atio
n
o
f
p
o
ten
tial
co
n
f
licts
an
d
d
ata
lim
itatio
n
s
,
n
ec
ess
itatin
g
s
y
s
tem
atic
co
llectio
n
p
r
ac
tices
alig
n
ed
with
r
esear
ch
d
esig
n
an
d
im
p
lem
en
tatio
n
p
ar
am
eter
s
[
7
]
,
en
ab
lin
g
ef
f
icien
t
ex
tr
ac
tio
n
an
d
ag
g
r
eg
atio
n
o
f
r
elev
an
t in
f
o
r
m
atio
n
[
9
]
.
2
.
4
.
Da
t
a
a
na
ly
t
ic
T
h
e
an
aly
tical
p
r
o
ce
s
s
in
co
r
p
o
r
ates
s
tatis
tical
an
d
s
cien
tific
m
eth
o
d
o
lo
g
ies
to
s
y
n
th
esize
in
d
iv
id
u
al
s
tu
d
y
ef
f
ec
ts
,
g
en
er
atin
g
co
m
p
r
eh
en
s
iv
e
r
esu
lts
f
r
o
m
ag
g
r
eg
ated
s
tu
d
y
d
ata
[
7
]
.
T
h
e
s
y
n
th
esis
m
ay
en
co
m
p
ass
b
o
th
q
u
alitativ
e
an
d
q
u
an
titativ
e
d
ata
f
r
o
m
v
er
if
ied
s
o
u
r
ce
s
,
en
h
an
cin
g
th
e
r
eliab
ilit
y
o
f
r
esear
ch
o
u
tco
m
es
[
1
0
]
.
T
h
e
b
r
ea
d
th
o
f
in
f
o
r
m
atio
n
in
teg
r
ated
with
in
th
e
s
y
s
tem
atic
liter
atu
r
e
r
ev
iew
co
r
r
elate
s
p
o
s
itiv
ely
with
th
e
co
n
f
id
en
ce
lev
el
in
an
aly
tical
co
n
clu
s
io
n
s
.
2
.
5
.
Da
t
a
s
y
nthesis
pro
ce
s
s
T
h
e
an
aly
tical
f
r
am
ewo
r
k
g
u
id
ed
s
tu
d
y
ca
teg
o
r
izatio
n
an
d
r
ef
er
en
ce
m
an
ag
em
en
t,
with
E
n
d
No
te
X9
an
d
Go
o
g
le
Sp
r
ea
d
s
h
ee
t su
p
p
o
r
tin
g
d
ata
s
to
r
ag
e
an
d
tr
ac
k
in
g
.
So
m
e
lim
itatio
n
s
r
em
ain
,
in
clu
d
in
g
in
co
m
p
lete
d
atab
ase
co
v
er
ag
e,
ter
m
in
o
lo
g
y
v
ar
iatio
n
s
,
an
d
th
e
lack
o
f
s
tan
d
ar
d
ized
m
etr
ics
f
o
r
m
ac
h
in
e
lear
n
in
g
d
ata
ev
alu
atio
n
.
3.
RE
SUL
T
S
AND
DIS
CUSSI
O
N
T
h
is
s
y
s
tem
atic
r
ev
iew
ex
am
in
ed
7
4
s
tu
d
ies
(
2
0
1
3
–
2
0
2
3
)
o
n
ML
ap
p
licatio
n
s
in
C
VD
p
r
ed
ictio
n
.
Gu
id
ed
b
y
f
iv
e
r
esear
ch
q
u
esti
o
n
s
,
th
e
r
ev
iew
h
ig
h
lig
h
ts
ad
v
an
ce
in
ML
-
b
ased
r
is
k
ass
ess
m
en
t,
r
an
g
in
g
f
r
o
m
ea
r
ly
d
etec
tio
n
to
ac
u
te
ev
en
t p
r
ed
ictio
n
,
an
d
id
en
tifie
s
k
ey
p
atter
n
s
ac
r
o
s
s
ca
r
d
io
lo
g
y
r
esear
ch
.
3
.
1
.
O
bs
t
a
cles in
m
a
chine le
a
rning
-
ba
s
ed
ca
rdio
v
a
s
cula
r
risk
a
na
ly
s
is
a
nd
predict
io
n
R
Q1
ex
am
in
ed
tech
n
ical
ch
allen
g
es
in
ML
-
b
ased
ca
r
d
io
v
ascu
lar
p
r
ed
ictio
n
,
id
en
tify
in
g
f
o
u
r
k
ey
o
b
s
tacle
s
af
f
ec
tin
g
ac
cu
r
ac
y
[
1
1
]
.
T
h
ese
ch
allen
g
es
in
clu
d
e
d
ata
q
u
ality
,
f
ea
tu
r
e
s
elec
tio
n
,
m
o
d
el
tr
ain
in
g
,
an
d
v
alid
atio
n
m
eth
o
d
o
lo
g
ies
[
1
2
]
.
E
ac
h
r
ep
r
esen
ts
a
cr
itical
co
n
s
id
er
atio
n
f
o
r
d
ev
elo
p
in
g
r
o
b
u
s
t
ML
s
o
lu
tio
n
s
f
o
r
ca
r
d
io
v
ascu
lar
r
is
k
p
r
ed
ictio
n
[
1
3
]
,
as d
etailed
in
T
ab
le
1
.
T
ab
le
1
.
T
a
b
le
s
u
m
m
ar
izin
g
wh
at
p
r
o
b
lem
s
o
r
lim
itatio
n
s
ar
e
o
b
s
tacle
s
in
an
aly
zin
g
a
n
d
p
r
ed
ictin
g
ca
r
d
io
v
ascu
lar
d
is
ea
s
e
r
is
k
b
y
m
ac
h
in
e
lear
n
in
g
No
P
r
o
b
l
e
ms
/
Li
m
i
t
a
t
i
o
n
s
S
o
l
u
t
i
o
n
R
e
se
a
r
c
h
1
U
si
n
g
sm
a
l
l
d
a
t
a
se
t
s
a
n
d
t
h
e
i
r
r
e
l
i
a
b
i
l
i
t
y
U
se
b
i
g
d
a
t
a
se
t
s a
n
d
b
e
mo
r
e
c
o
m
p
r
e
h
e
n
si
v
e
b
y
i
n
c
r
e
a
si
n
g
t
h
e
si
z
e
o
f
t
h
e
d
a
t
a
se
t
a
n
d
u
si
n
g
d
a
t
a
s
e
t
s
f
r
o
m rel
i
a
b
l
e
a
n
d
a
c
c
e
p
t
e
d
so
u
r
c
e
s.
[
1
1
]
–
[
1
7
]
2
Ex
a
m
i
n
i
n
g
d
a
t
a
se
t
s
p
o
t
e
n
t
i
a
l
,
mi
ssi
n
g
n
e
ss,
a
n
d
d
a
t
a
a
s
y
mm
e
t
r
i
e
s
C
l
e
a
n
si
n
g
d
a
t
a
,
e
x
t
r
a
c
t
i
n
g
d
a
t
a
,
a
n
d
s
e
l
e
c
t
i
o
n
a
r
e
p
e
r
f
o
r
me
d
t
o
r
e
d
u
c
e
v
u
l
n
e
r
a
b
i
l
i
t
i
e
s
i
n
t
h
e
d
a
t
a
se
t
t
h
a
t
n
e
g
a
t
i
v
e
l
y
i
m
p
a
c
t
a
n
a
l
y
s
i
s.
[
1
1
]
,
[
1
3
]
–
[
1
5
]
,
[
1
8
]
–
[
2
1
]
3
I
mp
o
r
t
a
n
t
f
e
a
t
u
r
e
s
i
n
a
d
a
t
a
se
t
u
se
d
f
o
r
p
r
e
d
i
c
t
i
o
n
S
e
l
e
c
t
a
d
a
t
a
s
e
t
t
h
a
t
h
a
s
a
r
e
l
e
v
a
n
t
a
n
d
c
o
m
p
r
e
h
e
n
si
v
e
f
e
a
t
u
r
e
s
e
t
.
[
1
1
]
,
[
1
4
]
,
[
1
6
]
,
[
1
7
]
,
[
2
1
]
–
[
2
5
]
4
S
p
l
i
t
d
a
t
a
f
o
r
t
r
a
i
n
i
n
g
a
n
d
t
e
s
t
i
n
g
t
h
e
mo
d
e
l
D
i
v
i
d
e
t
h
e
t
r
a
i
n
i
n
g
a
n
d
t
e
st
i
n
g
d
a
t
a
se
t
i
n
t
o
mu
l
t
i
p
l
e
r
a
t
i
o
s
a
n
d
c
o
m
p
a
r
e
t
h
e
p
e
r
f
o
r
ma
n
c
e
o
f
t
h
e
m
o
d
e
l
f
o
r
e
a
c
h
r
a
t
i
o
.
[
1
4
]
,
[
1
5
]
,
[
1
9
]
Evaluation Warning : The document was created with Spire.PDF for Python.
I
n
t J E
lec
&
C
o
m
p
E
n
g
I
SS
N:
2088
-
8
7
0
8
Da
ta
a
n
a
lytics a
n
d
p
r
ed
ictio
n
o
f c
a
r
d
io
v
a
s
cu
la
r
d
is
ea
s
e
w
i
th
ma
ch
in
e
…
(
R
a
vip
a
S
o
n
th
a
n
a
)
917
F
r
o
m
T
a
b
l
e
1
,
t
h
e
a
n
a
l
y
s
i
s
i
d
e
n
t
i
f
i
e
d
f
o
u
r
p
r
i
m
a
r
y
l
i
m
i
t
a
t
i
o
n
s
i
n
c
a
r
d
i
o
v
a
s
c
u
l
a
r
d
i
s
e
a
s
e
r
i
s
k
p
r
e
d
i
c
t
i
o
n
:
a.
Data
s
et
s
ize
an
d
r
eliab
ilit
y
co
n
s
tr
ain
ts
:
Sm
all
d
atasets
with
lim
ited
f
ea
tu
r
es
an
d
d
ata
p
o
in
ts
f
r
o
m
u
n
r
eliab
le
s
o
u
r
ce
s
f
r
eq
u
en
tly
r
esu
lt
in
r
ed
u
ce
d
an
aly
s
is
ac
cu
r
ac
y
.
R
ec
en
t
im
p
r
o
v
em
en
ts
h
av
e
f
o
cu
s
ed
o
n
u
tili
zin
g
co
m
p
r
eh
en
s
iv
e,
r
eliab
le
d
atasets
to
en
h
an
ce
p
r
ed
ictio
n
ac
cu
r
ac
y
[
1
2
]
.
Mu
h
am
m
ad
et
a
l.
[
1
6
]
h
ig
h
lig
h
ted
th
at
s
m
all
d
atasets
an
d
u
n
r
eliab
le
s
o
u
r
ce
s
r
aise c
o
n
ce
r
n
s
r
eg
ar
d
in
g
r
esu
lt g
en
er
aliza
b
ilit
y
.
b.
Data
q
u
ality
ass
ess
m
en
t:
E
v
alu
atin
g
d
ataset
p
o
ten
tial,
m
is
s
in
g
v
alu
es,
an
d
d
ata
asy
m
m
etr
ies
is
cr
u
cial.
Data
clea
n
in
g
p
r
o
ce
s
s
es
ar
e
ess
en
tial
f
o
r
en
s
u
r
in
g
an
aly
s
is
ef
f
icac
y
,
as
im
p
r
o
p
er
h
an
d
lin
g
m
ay
n
eg
ativ
ely
im
p
ac
t
f
o
r
ec
asti
n
g
p
er
f
o
r
m
an
ce
[
1
8
]
.
R
am
esh
an
d
Path
in
ar
u
p
o
th
i
[
2
1
]
d
eter
m
in
ed
th
at
ab
n
o
r
m
al
v
alu
es,
m
is
s
in
g
d
ata,
an
d
in
co
m
p
lete
en
tr
ies s
ig
n
if
ican
tly
r
ed
u
ce
m
o
d
el
p
er
f
o
r
m
an
ce
.
c.
Featu
r
e
s
elec
tio
n
co
m
p
lex
ity
:
T
h
e
m
u
ltifa
cto
r
ial
n
atu
r
e
o
f
ca
r
d
io
v
ascu
lar
d
is
ea
s
e
co
m
p
licates
th
e
id
en
tific
atio
n
o
f
k
ey
p
r
ed
ictiv
e
f
ea
tu
r
es.
Ho
s
s
ain
et
a
l.
[
1
1
]
p
r
o
p
o
s
ed
em
p
lo
y
in
g
f
ea
tu
r
e
en
g
in
ee
r
in
g
to
ex
tr
ac
t
an
d
tr
an
s
f
o
r
m
p
r
ed
ictio
n
-
r
elev
an
t
f
ea
tu
r
es,
en
h
an
cin
g
m
o
d
el
p
er
f
o
r
m
an
ce
.
Nag
ar
aju
et
a
l.
[
2
2
]
s
u
g
g
ested
th
e
R
elief
m
eth
o
d
to
f
ilter
d
ata
an
d
s
elec
t in
ter
co
n
n
ec
ted
r
elev
an
t f
ea
tu
r
es.
d.
T
r
ain
in
g
-
test
in
g
d
ata
d
is
tr
ib
u
tio
n
:
T
h
e
ab
s
en
ce
o
f
s
tan
d
ar
d
ized
ap
p
r
o
ac
h
es
f
o
r
d
ataset
d
iv
is
io
n
p
r
esen
ts
o
n
g
o
in
g
ch
allen
g
e
s
[
1
4
]
.
T
h
er
ef
o
r
e
,
Ud
d
in
an
d
Hald
er
[
1
9
]
im
p
lem
en
ted
th
e
"tr
ain
-
test
-
s
p
lit" m
eth
o
d
o
lo
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y
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alu
ate
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u
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ata
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r
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p
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n
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8
0
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0
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0
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0
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0
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0
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5
0
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0
)
f
o
r
o
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tim
al
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lts
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r
ess
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g
tr
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in
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d
ataset
im
b
alan
ce
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s
u
es.
3
.
2
.
F
a
ct
o
rs in
ca
rdio
v
a
s
cula
r
risk
a
na
ly
s
is
a
nd
predict
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n
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Q2
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am
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k
ey
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h
y
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eh
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io
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d
d
em
o
g
r
a
p
h
ic
p
r
ed
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r
s
o
f
ca
r
d
io
v
ascu
lar
r
is
k
in
ML
ap
p
licatio
n
s
[
1
2
]
.
Sev
e
n
m
ajo
r
C
VD
d
atasets
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e
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tifie
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tly
h
o
s
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l
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p
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lic
s
o
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r
ce
s
[
2
6
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,
with
f
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r
e
s
elec
tio
n
em
p
lo
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ed
to
o
p
tim
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el
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in
g
[
2
7
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o
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p
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r
s
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clu
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s
m
o
k
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n
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g
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er
[
2
8
]
.
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ite
th
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ef
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o
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ts
,
d
ataset
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iv
er
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ity
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em
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; f
u
r
th
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r
d
etails ar
e
in
T
a
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le
2
.
T
ab
le
2
p
r
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ts
1
3
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ey
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s
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o
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t,
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s
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d
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s
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h
e
f
r
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k
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m
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eig
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m
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id
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s
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d
C
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tar
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aim
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g
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h
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cu
r
ac
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ab
le
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Facto
r
s
in
ca
r
d
io
v
asc
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lar
r
is
k
an
aly
s
is
an
d
p
r
ed
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n
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s
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g
m
ac
h
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lear
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n
g
No
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a
t
a
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r
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e
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t
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t
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n
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t
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p
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r
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h
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d
p
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k
,
sl
o
p
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a
,
t
h
a
l
[
1
2
]
,
[
2
0
]
,
[
2
6
]
,
[
2
7
]
,
[
2
9
]
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[
3
3
]
[
3
4
]
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[
4
1
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Lu
d
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3
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15
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[
4
2
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4
3
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t
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[
1
3
]
,
[
1
9
]
,
[
2
4
]
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[
3
7
]
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[
4
4
]
–
[
4
7
]
,
5
K
a
g
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d
a
t
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14
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g
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h
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w
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e
,
p
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si
c
a
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t
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v
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t
y
[
2
8
]
6
M
O
N
I
C
A
d
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t
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se
t
11
a
g
e
,
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e
x
,
Y
r
o
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n
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st
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o
s
p
[
4
8
]
7
D
e
p
a
r
t
me
n
t
o
f
C
o
mp
u
t
i
n
g
o
f
G
o
l
d
smi
t
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s Un
i
v
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si
t
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f
Lo
n
d
o
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14
a
g
e
,
b
l
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d
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ssu
r
e
,
c
h
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st
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o
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,
m
a
x
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mu
m
h
e
a
r
t
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a
t
e
,
p
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a
k
,
c
o
l
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e
d
v
e
ss
e
l
s
,
se
x
,
c
h
e
s
t
p
a
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n
t
y
p
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,
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e
s
t
i
n
g
e
c
g
,
sl
o
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t
h
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l
,
F
a
st
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n
g
b
l
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d
s
u
g
a
r
<
1
2
0
’
,
‘
a
n
g
i
n
a
’
[
4
9
]
3
.
3
.
E
f
f
ec
t
iv
e
da
t
a
pro
ce
s
s
ing
m
et
ho
do
lo
g
y
f
o
r
enha
nced
predict
io
n a
cc
ura
cy
R
Q3
ex
am
in
ed
ad
v
an
ce
d
f
r
am
ewo
r
k
s
f
o
r
ca
r
d
io
v
ascu
lar
r
is
k
p
r
ed
ictio
n
,
co
m
p
r
is
in
g
f
iv
e
s
t
ag
es.
a.
Data
ac
q
u
is
itio
n
an
d
p
r
ep
r
o
ce
s
s
in
g
en
s
u
r
e
d
ataset
v
alid
ity
th
r
o
u
g
h
o
u
tlier
r
em
o
v
al
an
d
attr
ib
u
te
o
p
tim
izatio
n
[
1
9
]
,
to
f
ac
ilit
ate
th
e
ac
q
u
is
itio
n
o
f
co
m
p
r
e
h
en
s
iv
e
q
u
a
n
titativ
e
an
d
q
u
a
litativ
e
d
ataset
s
alig
n
ed
with
ca
r
d
i
o
v
ascu
lar
r
i
s
k
ass
es
s
m
en
t
o
b
jectiv
es
[
2
7
]
,
wh
ile
en
s
u
r
in
g
m
eth
o
d
o
lo
g
ic
al
r
o
b
u
s
tn
ess
.
b.
Data
an
aly
s
is
an
d
tr
an
s
f
o
r
m
atio
n
em
p
lo
y
c
o
m
p
u
tatio
n
al
tech
n
iq
u
es
[
2
6
]
,
to
o
p
tim
ize
s
tr
u
ctu
r
e
an
d
s
tan
d
ar
d
ize
h
eter
o
g
en
eo
u
s
p
ar
am
eter
s
.
Evaluation Warning : The document was created with Spire.PDF for Python.
I
SS
N
:
2
0
8
8
-
8
7
0
8
I
n
t J E
lec
&
C
o
m
p
E
n
g
,
Vo
l.
1
6
,
No
.
2
,
Ap
r
il
20
2
6
:
9
1
4
-
923
918
c.
Featu
r
e
s
elec
tio
n
o
p
tim
izatio
n
en
h
a
n
ce
s
class
if
icatio
n
p
r
e
cisi
o
n
wh
ile
m
itig
atin
g
s
p
ar
s
i
ty
an
d
r
ed
u
ci
n
g
co
m
p
u
tatio
n
al
c
o
m
p
lex
ity
[
3
4
]
.
T
h
is
ap
p
r
o
ac
h
f
ac
ilit
ates
th
e
id
en
tific
atio
n
o
f
c
r
itical
ca
r
d
io
v
ascu
lar
r
is
k
p
r
ed
icto
r
s
.
d.
Mo
d
el
d
e
v
elo
p
m
e
n
t
e
n
h
an
ce
m
en
t
in
teg
r
ates
clin
ical
p
ar
a
m
eter
s
to
im
p
r
o
v
e
p
r
e
d
ictiv
e
ca
p
ab
ilit
ies
an
d
lear
n
in
g
ef
f
icien
cy
[
3
2
]
.
e.
P
e
r
f
o
r
m
a
n
c
e
e
v
a
l
u
a
ti
o
n
a
p
p
l
i
es
e
s
t
a
b
l
is
h
e
d
m
e
t
r
ic
s
[
3
3
]
t
o
v
al
i
d
a
t
e
m
o
d
e
ls
a
n
d
r
e
f
i
n
e
p
r
e
d
i
ct
i
v
e
a
c
c
u
r
a
c
y
.
3
.
4
.
M
a
chine
lea
rning
t
ec
hn
iqu
es
f
o
r
ca
rdio
v
a
s
cula
r
dis
ea
s
e
predict
io
n
R
Q4
in
v
esti
g
ated
th
e
p
er
f
o
r
m
an
ce
o
f
m
ac
h
in
e
lear
n
in
g
alg
o
r
ith
m
s
f
o
r
ca
r
d
i
o
v
ascu
l
ar
d
is
ea
s
e
p
r
ed
ictio
n
.
T
h
e
r
e
v
iew
id
en
tif
ied
twen
ty
d
is
tin
ct
tech
n
iq
u
es
,
ea
ch
ex
h
i
b
itin
g
v
a
r
y
in
g
lev
e
ls
o
f
ef
f
ec
tiv
en
ess
an
d
im
p
lem
e
n
tatio
n
c
h
allen
g
es,
as
illu
s
tr
ated
in
Fig
u
r
e
1
.
B
ased
o
n
t
h
is
an
aly
s
is
,
eig
h
t
r
ep
r
esen
tativ
e
m
eth
o
d
s
wer
e
s
elec
ted
,
with
t
h
e
ad
d
itio
n
o
f
ANN
to
p
r
o
v
i
d
e
a
m
o
r
e
c
o
m
p
r
e
h
en
s
iv
e
co
m
p
ar
ativ
e
ev
alu
atio
n
.
Fig
u
r
e
1
.
Ma
ch
i
n
e
lear
n
in
g
te
ch
n
iq
u
es f
o
r
ca
r
d
io
v
ascu
lar
d
is
ea
s
e
p
r
ed
ictio
n
B
ased
o
n
Fig
u
r
e
1
,
th
e
r
e
s
ea
r
ch
m
eth
o
d
o
lo
g
y
in
c
o
r
p
o
r
ated
eig
h
t
an
aly
tical
a
n
d
f
o
r
ec
asti
n
g
tech
n
iq
u
es,
in
clu
d
in
g
an
a
d
d
i
tio
n
al
ANN
ap
p
r
o
ac
h
,
r
ep
r
es
en
tin
g
a
n
o
v
el
co
m
p
r
eh
en
s
iv
e
co
m
p
ar
is
o
n
.
T
h
e
tech
n
iq
u
es c
an
b
e
ca
teg
o
r
ized
as f
o
llo
ws:
a.
T
r
ee
-
b
ased
m
eth
o
d
s
:
−
R
an
d
o
m
f
o
r
est
(
RF
)
:
E
x
ce
ls
in
b
o
th
r
eg
r
ess
io
n
an
d
class
if
icatio
n
task
s
,
p
ar
ticu
lar
ly
with
lar
g
e
-
s
ca
le
n
o
n
lin
ea
r
d
atasets
.
I
ts
f
ea
tu
r
e
s
elec
tio
n
ca
p
ab
ilit
y
en
h
an
ce
s
p
r
ed
icto
r
id
en
tific
atio
n
,
o
p
tim
izin
g
ef
f
icien
cy
an
d
ac
cu
r
ac
y
.
−
Dec
is
io
n
t
r
ee
(
DT
)
:
Of
f
er
s
s
tr
aig
h
t
f
o
r
war
d
im
p
lem
e
n
t
ati
o
n
f
o
r
r
eg
r
ess
io
n
an
d
class
if
icatio
n
,
ef
f
ec
tiv
el
y
s
u
m
m
ar
izin
g
co
m
p
lex
d
ec
is
io
n
s
.
W
id
ely
ad
o
p
ted
in
m
ed
ical
ap
p
licatio
n
s
.
−
E
x
tr
em
e
g
r
ad
ien
t
b
o
o
s
tin
g
(
XGBo
o
s
tin
g
)
:
An
en
h
an
ce
d
iter
atio
n
o
f
g
r
ad
ien
t
b
o
o
s
tin
g
,
u
tili
zin
g
s
eq
u
en
tial
d
ec
is
io
n
tr
ee
s
f
o
r
m
o
d
el
tr
ain
in
g
.
E
ac
h
iter
atio
n
lear
n
s
f
r
o
m
p
r
ed
ec
ess
o
r
er
r
o
r
v
alu
es,
m
ax
im
izin
g
p
r
ed
ictiv
e
ac
cu
r
ac
y
wh
ile
m
in
im
izin
g
co
m
p
u
tatio
n
al
r
eso
u
r
ce
s
.
b.
Statis
tical
lear
n
in
g
m
eth
o
d
s
:
−
Su
p
p
o
r
t
v
ec
to
r
m
ac
h
in
e
(
SVM
):
Sp
ec
ializes
in
b
in
ar
y
class
if
icatio
n
f
o
r
co
m
p
lex
,
h
ig
h
-
d
im
en
s
io
n
al
d
atasets
.
Dem
o
n
s
tr
ates
p
ar
ticu
lar
ef
f
icac
y
in
am
b
ig
u
o
u
s
d
ata
class
if
icatio
n
with
m
o
d
er
ate
s
am
p
le
s
izes
.
Evaluation Warning : The document was created with Spire.PDF for Python.
I
n
t J E
lec
&
C
o
m
p
E
n
g
I
SS
N:
2088
-
8
7
0
8
Da
ta
a
n
a
lytics a
n
d
p
r
ed
ictio
n
o
f c
a
r
d
io
v
a
s
cu
la
r
d
is
ea
s
e
w
i
th
ma
ch
in
e
…
(
R
a
vip
a
S
o
n
th
a
n
a
)
919
−
L
o
g
is
tic
r
eg
r
ess
io
n
(
LR
)
:
Prim
ar
ily
ap
p
lied
in
b
in
ar
y
class
if
icatio
n
f
o
r
d
ec
is
io
n
-
m
ak
in
g
an
d
r
is
k
ass
ess
m
en
t,
with
ex
ten
s
iv
e
im
p
lem
en
tatio
n
ac
r
o
s
s
m
ed
ical
r
esear
ch
.
−
Naïv
e
B
ay
es
(
NB
)
:
E
m
p
lo
y
s
B
ay
esian
p
r
o
b
ab
ilit
y
th
eo
r
y
f
o
r
class
if
icatio
n
task
s
,
r
eq
u
ir
in
g
lab
eled
d
ata
f
o
r
s
u
p
er
v
is
ed
lear
n
in
g
.
Sp
ec
ializes in
p
r
ed
ictiv
e
an
aly
s
is
b
ased
o
n
h
is
to
r
ical
p
r
o
b
ab
ilit
y
p
atter
n
s
.
c.
I
n
s
tan
ce
-
b
ased
l
ea
r
n
in
g
:
K
-
n
ea
r
est
n
eig
h
b
o
r
(K
-
NN)
:
Ver
s
atile
in
b
o
th
class
if
icatio
n
an
d
r
eg
r
ess
io
n
,
u
tili
zin
g
p
r
o
x
im
ity
p
r
in
cip
les
f
o
r
class
ass
ig
n
m
en
t.
Par
ticu
lar
ly
s
u
ited
f
o
r
n
u
m
er
ical
d
ata
an
d
m
u
lti
-
class
class
if
icatio
n
th
r
o
u
g
h
f
ea
tu
r
e
-
d
is
tan
ce
m
ea
s
u
r
em
en
t
.
d.
Neu
r
al
n
etwo
r
k
ap
p
r
o
ac
h
:
Ar
tific
ial
n
eu
r
al
n
etwo
r
k
(
ANN)
:
Dem
o
n
s
tr
ates
v
er
s
atility
in
r
eg
r
ess
io
n
an
d
class
if
icatio
n
task
s
,
em
p
lo
y
in
g
b
r
ain
-
in
s
p
ir
ed
lear
n
in
g
m
ec
h
an
is
m
s
f
o
r
co
m
p
lex
p
r
o
b
lem
-
s
o
lv
in
g
.
E
x
h
ib
its
p
ar
ticu
lar
s
tr
en
g
th
in
p
r
ed
ictiv
e
m
o
d
ellin
g
th
r
o
u
g
h
ex
p
er
ien
tial le
ar
n
in
g
.
Fre
q
u
en
cy
an
aly
s
is
f
r
o
m
Fig
u
r
e
1
in
d
icate
d
th
at
RF
(
1
5
im
p
lem
en
tatio
n
s
)
,
SVM
(
1
4
)
,
DT
(
1
3
)
,
LR
(
1
2
)
,
NB
(
1
2
)
,
K
-
NN
(
9
)
,
an
d
XGBo
o
s
tin
g
(
5
)
wer
e
th
e
m
o
s
t
co
m
m
o
n
l
y
ap
p
lied
alg
o
r
ith
m
s
in
ca
r
d
io
v
ascu
lar
p
r
ed
ictio
n
task
s
,
with
RF
b
ein
g
th
e
m
o
s
t f
r
e
q
u
en
tly
ad
o
p
ted
.
3
.
5
.
M
o
del
v
a
lid
a
t
io
n str
a
t
eg
ies f
o
r
clinica
l r
elia
bil
it
y
R
Q5
in
v
esti
g
ated
m
eth
o
d
o
lo
g
ies
f
o
r
v
alid
atin
g
ca
r
d
io
v
ascu
lar
p
r
ed
ictio
n
m
o
d
els.
Nin
e
e
v
alu
atio
n
ap
p
r
o
ac
h
es we
r
e
id
en
tifie
d
,
g
r
o
u
p
ed
i
n
to
th
r
ee
ca
teg
o
r
ies:
−
Prim
ar
y
p
er
f
o
r
m
an
ce
m
etr
ics:
Acc
u
r
ac
y
,
R
OC
–
AU
C
cu
r
v
e
,
an
d
co
n
f
u
s
io
n
m
atr
i
x
,
p
r
o
v
id
i
n
g
f
u
n
d
am
en
tal
ass
es
s
m
en
t o
f
o
v
er
all
p
r
ed
icti
v
e
ca
p
ab
ilit
y
an
d
class
if
icatio
n
ef
f
ec
tiv
en
ess
.
−
Ad
v
an
ce
d
p
er
f
o
r
m
an
ce
in
d
icato
r
s
:
F1
-
Sco
r
e,
s
en
s
itiv
ity
/r
ec
all,
an
d
s
p
ec
if
icity
,
en
a
b
lin
g
d
etailed
p
er
f
o
r
m
an
ce
an
al
y
s
is
f
o
r
in
d
iv
id
u
al
class
es a
n
d
m
ea
s
u
r
em
en
t o
f
tr
u
e
n
eg
ativ
e
r
ates.
−
Statis
t
ical
v
alid
atio
n
m
et
h
o
d
s
:
Pre
cisi
o
n
,
m
ac
r
o
a
v
er
ag
e
,
a
n
d
weig
h
ted
a
v
er
ag
e,
o
f
f
er
in
g
class
-
s
p
ec
if
ic
ac
cu
r
ac
y
ev
al
u
atio
n
a
n
d
av
e
r
a
g
in
g
m
eth
o
d
s
s
u
itab
le
f
o
r
d
ata
s
ets with
u
n
eq
u
al
class
d
is
tr
ib
u
tio
n
.
T
ab
le
3
s
u
m
m
ar
izes
th
e
ap
p
licatio
n
f
r
eq
u
en
cy
o
f
th
ese
m
etr
ics
ac
r
o
s
s
th
e
r
ev
iewe
d
s
tu
d
ies,
h
ig
h
lig
h
tin
g
ac
cu
r
ac
y
,
s
en
s
itiv
ity
/r
ec
all,
p
r
ec
is
io
n
,
F1
-
Sco
r
e,
R
OC
–
AUC,
co
n
f
u
s
io
n
m
atr
ix
,
an
d
s
p
ec
if
icity
as
th
e
m
o
s
t
co
m
m
o
n
ly
u
tili
ze
d
.
B
ased
o
n
im
p
lem
en
tatio
n
n
ee
d
s
an
d
r
esear
ch
o
b
jectiv
es,
f
iv
e
k
ey
m
etr
ics
—
ac
cu
r
ac
y
,
s
en
s
itiv
ity
/r
ec
all,
p
r
ec
is
io
n
,
F1
-
Sco
r
e,
an
d
co
n
f
u
s
io
n
m
atr
ix
—
wer
e
s
elec
ted
f
o
r
r
o
b
u
s
t
m
o
d
el
v
alid
atio
n
.
T
ab
le
3
.
Mo
d
el
v
alid
atio
n
s
tr
ateg
ies f
o
r
clin
ical
r
eliab
ilit
y
No
M
o
d
e
l
Ev
a
l
u
a
t
i
o
n
R
e
se
a
r
c
h
1
A
c
c
u
r
a
c
y
[
1
2
]
,
[
2
6
]
,
[
2
8
]
,
[
2
9
]
,
[
3
3
]
,
[
4
2
]
,
[
4
6
]
,
[
4
7
]
,
[
5
0
]
–
[
5
4
]
2
R
O
C
–
A
U
C
C
u
r
v
e
[
1
2
]
,
[
1
5
]
,
[
2
1
]
,
[
2
8
]
,
[
3
3
]
,
[
5
0
]
,
[
5
1
]
3
C
o
n
f
u
s
i
o
n
M
a
t
r
i
x
[
1
2
]
,
[
2
1
]
,
[
3
3
]
,
[
4
6
]
,
[
5
0
]
4
F1
-
S
c
o
r
e
[
2
1
]
,
[
2
9
]
,
[
4
2
]
,
[
4
6
]
,
[
4
7
]
,
[
5
0
]
,
[
5
1
]
,
[
5
3
]
,
[
5
4
]
5
S
e
n
s
i
t
i
v
i
t
y
/
R
e
c
a
l
l
[
2
1
]
,
[
2
6
]
,
[
2
9
]
,
[
3
3
]
,
[
4
2
]
,
[
4
6
]
,
[
4
7
]
,
[
5
0
]
,
[
5
1
]
,
[
5
3
]
,
[
5
4
]
6
S
p
e
c
i
f
i
c
i
t
y
[
2
6
]
,
[
2
9
]
,
[
3
3
]
,
[
5
0
]
7
P
r
e
c
i
s
i
o
n
[
2
1
]
,
[
2
6
]
,
[
2
9
]
,
[
4
2
]
,
[
4
6
]
,
[
4
7
]
,
[
5
0
]
,
[
5
1
]
,
[
5
3
]
,
[
5
4
]
8
M
a
c
r
o
A
v
e
r
a
g
e
[
5
3
]
9
W
e
i
g
h
t
A
v
e
r
a
g
e
[
5
3
]
T
h
e
s
y
s
tem
atic
r
ev
iew
o
f
ex
is
tin
g
ca
r
d
io
v
ascu
lar
d
is
ea
s
e
p
r
ed
ictio
n
r
esear
ch
f
ac
ilit
ated
th
e
d
ev
elo
p
m
en
t
o
f
an
o
p
tim
ized
an
aly
tical
f
r
am
ewo
r
k
.
T
h
is
f
r
am
ewo
r
k
,
illu
s
tr
ated
in
Fig
u
r
e
2
,
in
teg
r
ates
estab
lis
h
ed
m
eth
o
d
o
lo
g
ies to
en
h
an
ce
co
m
p
u
tatio
n
al
ef
f
icien
cy
an
d
m
itig
ate
an
aly
tical
ch
allen
g
es in
m
ac
h
in
e
lear
n
in
g
-
b
ased
ca
r
d
io
v
ascu
lar
r
is
k
p
r
ed
ictio
n
.
T
h
e
p
r
o
p
o
s
ed
s
tr
u
ctu
r
e
s
tr
ea
m
lin
es
d
ata
p
r
o
ce
s
s
in
g
wh
ile
m
ain
tain
in
g
r
o
b
u
s
t p
r
ed
ictiv
e
ca
p
ab
ilit
ies.
As
illu
s
tr
ated
in
Fig
u
r
e
2
,
th
e
an
aly
tical
f
r
am
ewo
r
k
f
o
r
ca
r
d
io
v
ascu
lar
d
is
ea
s
e
r
is
k
p
r
ed
ictio
n
in
teg
r
ates
s
y
s
tem
atic
m
ac
h
in
e
lear
n
in
g
m
eth
o
d
o
lo
g
ies
ac
r
o
s
s
th
r
ee
d
is
tin
ct
o
p
er
atio
n
al
s
tep
s
.
B
ased
o
n
ex
ten
s
iv
e
liter
atu
r
e
r
ev
iew,
th
is
f
r
am
ewo
r
k
o
p
tim
izes
co
m
p
u
tatio
n
al
p
r
o
ce
s
s
es
wh
ile
en
s
u
r
in
g
p
r
ed
ictio
n
ac
cu
r
ac
y
.
Step
1
.
Data
p
r
ep
ar
atio
n
:
T
h
is
s
tag
e
en
co
m
p
ass
es
d
ata
co
llectio
n
,
clea
n
s
in
g
,
f
ea
tu
r
e
s
elec
tio
n
,
an
d
d
ata
s
p
litt
in
g
.
Data
wer
e
o
b
tain
ed
f
r
o
m
th
e
b
eh
av
io
r
al
r
is
k
f
ac
to
r
s
u
r
v
eillan
ce
s
y
s
tem
(
B
R
FS
S),
en
s
u
r
in
g
r
eliab
le
lar
g
e
-
s
ca
le
h
ea
lth
in
f
o
r
m
atio
n
.
C
lean
s
in
g
in
v
o
lv
ed
r
em
o
v
in
g
d
u
p
licates,
co
r
r
ec
tin
g
in
co
n
s
is
ten
cies,
f
ilter
in
g
o
u
tlier
s
,
an
d
m
an
ag
in
g
m
is
s
in
g
v
alu
es
to
en
h
an
ce
d
ata
in
teg
r
ity
.
Featu
r
e
s
elec
tio
n
ex
tr
ac
ted
s
alien
t
p
r
ed
icto
r
s
th
r
o
u
g
h
s
y
s
tem
atic
s
cr
ee
n
in
g
,
wh
ile
d
ata
s
p
litt
in
g
d
iv
id
ed
r
ec
o
r
d
s
in
to
tr
ain
in
g
an
d
test
in
g
s
ets
f
o
r
u
n
b
iased
ev
alu
atio
n
.
Evaluation Warning : The document was created with Spire.PDF for Python.
I
SS
N
:
2
0
8
8
-
8
7
0
8
I
n
t J E
lec
&
C
o
m
p
E
n
g
,
Vo
l.
1
6
,
No
.
2
,
Ap
r
il
20
2
6
:
9
1
4
-
923
920
Step
2
.
Mo
d
el
im
p
lem
en
tatio
n
:
E
ig
h
t
class
if
icatio
n
alg
o
r
ith
m
s
—
R
F,
SVM,
DT
,
L
R
,
NB
,
K
-
NN,
XGBo
o
s
tin
g
,
an
d
ANN
—
wer
e
ap
p
lied
.
E
ac
h
was tr
ain
ed
o
n
th
e
p
r
ep
ar
ed
d
ataset
an
d
v
alid
ated
to
co
m
p
ar
e
p
r
ed
ictiv
e
p
er
f
o
r
m
an
ce
.
Step
3
.
Per
f
o
r
m
an
ce
ass
ess
m
en
t:
Mo
d
el
ev
alu
atio
n
in
v
o
lv
ed
ca
lcu
latin
g
ac
cu
r
ac
y
,
s
en
s
itiv
ity
,
p
r
ec
is
io
n
,
F1
-
Sco
r
e,
an
d
co
n
f
u
s
io
n
m
atr
ix
m
etr
ics,
en
ab
lin
g
co
m
p
ar
ativ
e
an
aly
s
is
o
f
p
r
ed
ictiv
e
ca
p
ab
ilit
y
an
d
co
m
p
u
tatio
n
al
ef
f
icien
cy
to
id
en
tify
o
p
tim
al
tech
n
iq
u
es.
Fig
u
r
e
2
.
Ma
ch
i
n
e
lear
n
in
g
f
r
a
m
ewo
r
k
f
o
r
ca
r
d
io
v
ascu
lar
d
is
ea
s
e
r
is
k
an
aly
tics
an
d
p
r
e
d
ictio
n
4.
CO
NCLUS
I
O
N
T
h
is
s
y
s
tem
atic
liter
atu
r
e
r
ev
iew
s
y
n
th
esized
a
d
ec
ad
e
o
f
r
esear
ch
o
n
th
e
ap
p
licatio
n
o
f
m
ac
h
in
e
lear
n
in
g
f
o
r
ca
r
d
io
v
ascu
lar
d
is
ea
s
e
p
r
ed
ictio
n
.
T
h
e
ev
id
en
ce
d
em
o
n
s
tr
ates
th
at
p
r
ed
ictiv
e
p
er
f
o
r
m
an
ce
d
ep
en
d
s
n
o
t
o
n
ly
o
n
alg
o
r
ith
m
s
elec
tio
n
b
u
t
also
o
n
p
r
ep
r
o
ce
s
s
in
g
,
f
ea
tu
r
e
s
elec
tio
n
,
an
d
in
ter
p
r
etab
ilit
y
.
Ad
v
an
ce
d
m
o
d
els
s
u
ch
as
r
an
d
o
m
f
o
r
est,
s
u
p
p
o
r
t
v
ec
to
r
m
ac
h
in
es
,
an
d
XGBo
o
s
tin
g
co
n
s
is
ten
tly
o
u
tp
er
f
o
r
m
ed
tr
ad
itio
n
al
lin
ea
r
m
eth
o
d
s
,
wh
ile
d
ee
p
lear
n
in
g
ap
p
r
o
ac
h
es
s
h
o
wed
s
tr
o
n
g
p
r
ed
ictiv
e
p
o
ten
tial
in
h
an
d
lin
g
co
m
p
lex
an
d
lar
g
e
-
s
ca
le
d
atasets
.
Ho
wev
er
,
ch
allen
g
es
o
f
in
ter
p
r
etab
ilit
y
,
h
eter
o
g
en
eo
u
s
d
ata
q
u
ality
,
an
d
lim
ited
clin
ical
ad
o
p
tio
n
r
em
ain
s
ig
n
if
ican
t b
ar
r
ier
s
.
T
h
e
th
esis
o
f
th
is
p
ap
er
is
th
at
th
e
s
u
cc
ess
o
f
ML
in
C
VD
r
is
k
ass
ess
m
en
t
lies
in
th
e
co
m
b
in
ed
s
tr
en
g
th
o
f
alg
o
r
ith
m
ic
s
o
p
h
is
ticatio
n
,
d
ata
p
r
ep
r
o
ce
s
s
in
g
,
f
ea
tu
r
e
en
g
in
ee
r
in
g
,
an
d
ex
p
lain
ab
ilit
y
was
s
u
p
p
o
r
ted
b
y
th
e
r
ev
iewe
d
ev
id
en
ce
.
B
y
in
teg
r
atin
g
th
ese
f
ac
to
r
s
,
ML
-
b
ased
m
o
d
els
ca
n
m
o
v
e
clo
s
er
to
ac
h
iev
in
g
clin
ically
r
eliab
le
an
d
m
ea
n
in
g
f
u
l o
u
tco
m
es.
Fu
tu
r
e
r
esear
ch
s
h
o
u
ld
f
o
cu
s
o
n
th
e
d
ev
elo
p
m
en
t o
f
in
ter
p
r
etab
le
ML
f
r
am
ewo
r
k
s
,
th
e
in
teg
r
atio
n
o
f
m
u
ltimo
d
al
h
ea
lth
d
ata,
an
d
th
e
d
esig
n
o
f
p
r
iv
ac
y
-
p
r
eser
v
in
g
ap
p
r
o
ac
h
es
s
u
ch
as
f
ed
er
ated
lear
n
in
g
.
Ad
d
r
ess
in
g
th
ese
is
s
u
es
will
ac
ce
ler
ate
th
e
tr
an
s
itio
n
o
f
ML
-
b
ased
C
VD
p
r
ed
ictio
n
f
r
o
m
r
esear
ch
to
clin
ical
p
r
ac
tice,
u
ltima
tely
im
p
r
o
v
in
g
p
atien
t
o
u
tco
m
es
an
d
co
n
tr
ib
u
tin
g
to
m
o
r
e
p
er
s
o
n
alize
d
,
d
ata
-
d
r
iv
en
h
ea
lth
ca
r
e
s
y
s
tem
s
.
F
UNDING
I
NF
O
R
M
A
T
I
O
N
Au
th
o
r
s
s
tate
n
o
f
u
n
d
in
g
in
v
o
lv
ed
.
CO
NF
L
I
C
T
O
F
I
N
T
E
R
E
S
T
ST
A
T
E
M
E
NT
Au
th
o
r
s
s
tate
n
o
co
n
f
lict o
f
in
t
er
est.
Evaluation Warning : The document was created with Spire.PDF for Python.
I
n
t J E
lec
&
C
o
m
p
E
n
g
I
SS
N:
2088
-
8
7
0
8
Da
ta
a
n
a
lytics a
n
d
p
r
ed
ictio
n
o
f c
a
r
d
io
v
a
s
cu
la
r
d
is
ea
s
e
w
i
th
ma
ch
in
e
…
(
R
a
vip
a
S
o
n
th
a
n
a
)
921
DATA AV
AI
L
AB
I
L
I
T
Y
Data
av
ailab
ilit
y
is
n
o
t
ap
p
li
ca
b
le
to
th
is
p
ap
er
as
n
o
n
e
w
d
ata
wer
e
cr
ea
ted
o
r
an
aly
ze
d
in
th
is
s
tu
d
y
.
RE
F
E
R
E
NC
E
S
[
1
]
S
.
K
.
D
e
b
n
a
t
h
,
S
.
M
a
l
i
k
,
G
.
K
a
u
r
,
S
.
B
a
g
c
h
i
,
A
.
M
.
S
o
o
mr
o
,
a
n
d
A
.
N
a
e
e
m,
“
P
r
e
d
i
c
t
i
o
n
a
c
c
u
r
a
c
y
i
mp
r
o
v
e
m
e
n
t
f
o
r
c
a
r
d
i
o
v
a
sc
u
l
a
r
d
i
s
e
a
se
s
u
si
n
g
ma
c
h
i
n
e
l
e
a
r
n
i
n
g
a
l
g
o
r
i
t
h
m,”
i
n
2
0
2
3
1
0
t
h
I
EEE
U
t
t
a
r
Pr
a
d
e
sh
S
e
c
t
i
o
n
I
n
t
e
r
n
a
t
i
o
n
a
l
C
o
n
f
e
r
e
n
c
e
o
n
E
l
e
c
t
ri
c
a
l
,
El
e
c
t
r
o
n
i
c
s
a
n
d
C
o
m
p
u
t
e
r
E
n
g
i
n
e
e
r
i
n
g
(
U
PC
O
N
)
,
2
0
2
3
,
p
p
.
1
0
3
2
–
1
0
3
7
,
d
o
i
:
1
0
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1
1
0
9
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P
C
O
N
5
9
1
9
7
.
2
0
2
3
.
1
0
4
3
4
5
5
9
.
[
2
]
I
.
D
a
w
a
r
a
n
d
S
.
W
a
d
h
w
a
n
,
“
P
r
e
d
i
c
t
i
n
g
c
a
r
d
i
o
v
a
s
c
u
l
a
r
d
i
s
e
a
s
e
u
si
n
g
ma
c
h
i
n
e
l
e
a
r
n
i
n
g
t
e
c
h
n
i
q
u
e
s
,
”
i
n
C
o
n
f
e
re
n
c
e
Pr
o
c
e
e
d
i
n
g
s
-
2
0
2
3
I
EE
E
S
i
l
c
h
a
r
S
u
b
se
c
t
i
o
n
C
o
n
f
e
r
e
n
c
e
,
S
I
L
C
O
N
2
0
2
3
,
2
0
2
3
,
p
p
.
1
–
6
,
d
o
i
:
1
0
.
1
1
0
9
/
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I
LC
O
N
5
9
1
3
3
.
2
0
2
3
.
1
0
4
0
5
3
1
8
.
[
3
]
J.
Jam
a
l
u
d
d
i
n
,
M
.
S
.
M
o
h
a
me
d
-
Y
a
ss
i
n
,
S
.
N
.
Ja
mi
l
,
M
.
A
.
M
o
h
a
m
e
d
K
a
me
l
,
a
n
d
M
.
Y
.
a
k
o
b
Y
u
so
f
,
“
F
r
e
q
u
e
n
c
y
a
n
d
p
r
e
d
i
c
t
o
r
s
o
f
i
n
a
p
p
r
o
p
r
i
a
t
e
m
e
d
i
c
a
t
i
o
n
d
o
sa
g
e
s
f
o
r
c
a
r
d
i
o
v
a
sc
u
l
a
r
d
i
se
a
se
p
r
e
v
e
n
t
i
o
n
i
n
c
h
r
o
n
i
c
k
i
d
n
e
y
d
i
sea
se
p
a
t
i
e
n
t
s:
A
r
e
t
r
o
sp
e
c
t
i
v
e
c
r
o
ss
-
sec
t
i
o
n
a
l
s
t
u
d
y
i
n
a
M
a
l
a
y
s
i
a
n
p
r
i
m
a
r
y
c
a
r
e
c
l
i
n
i
c
,
”
H
e
l
i
y
o
n
,
v
o
l
.
9
,
n
o
.
4
,
p
.
e
1
4
9
9
8
,
2
0
2
3
,
d
o
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:
1
0
.
1
0
1
6
/
j
.
h
e
l
i
y
o
n
.
2
0
2
3
.
e
1
4
9
9
8
.
[
4
]
B
.
V
H
o
w
a
r
d
a
n
d
M
.
F
.
M
a
g
e
e
,
“
D
i
a
b
e
t
e
s
a
n
d
c
a
r
d
i
o
v
a
s
c
u
l
a
r
d
i
se
a
s
e
,
”
C
u
rr
e
n
t
At
h
e
ro
s
c
l
e
ro
s
i
s
Re
p
o
rt
s
,
v
o
l
.
2
,
n
o
.
6
,
p
p
.
4
7
6
–
4
8
1
,
2
0
0
0
.
[
5
]
M
.
H
e
d
a
y
a
t
n
i
a
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