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se
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th
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b
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se
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y
Tab
Ne
t
a
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d
m
u
lt
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las
s
su
p
p
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rt
v
e
c
to
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m
a
c
h
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e
(S
VM
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c
re
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ted
o
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r
d
a
tas
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ts
fo
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e
x
p
e
ri
m
e
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g
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h
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s
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e
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so
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n
d
M
a
n
d
y
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re
g
io
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s
o
f
Ka
rn
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tak
a
,
In
d
ia.
Da
tas
e
ts
c
o
n
sist
o
f
1
6
fe
a
tu
re
s;
th
e
fe
a
tu
re
s
a
re
p
re
-
p
ro
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e
ss
e
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o
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n
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le
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g
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Ne
t,
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n
d
t
h
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lt
icla
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ise
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se
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s m
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ra
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ll
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d
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ywo
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d
s
:
C
o
r
o
n
ar
y
h
ea
r
t d
is
ea
s
e
Ma
ch
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lticlas
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VM
Su
p
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v
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ab
Net
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h
is i
s
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n
o
p
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a
c
c
e
ss
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rticle
u
n
d
e
r th
e
CC B
Y
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SA
li
c
e
n
se
.
C
o
r
r
e
s
p
o
nd
ing
A
uth
o
r
:
B
alak
r
is
h
n
a
Kem
p
eg
o
wd
a
Dep
ar
tm
en
t o
f
E
C
E
,
Ma
h
ar
aja
I
n
s
titu
te
o
f
T
ec
h
n
o
lo
g
y
My
s
o
r
e,
VT
U
Un
iv
er
s
ity
5
7
1
4
7
7
,
Kar
n
atak
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I
n
d
ia
E
m
ail:
b
alak
r
is
h
n
ak
_
ec
e@
m
itm
y
s
o
r
e.
in
1.
I
NT
RO
D
UCT
I
O
N
No
wad
ay
s
,
h
ea
r
t
d
is
ea
s
e
is
th
e
p
r
im
ar
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is
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s
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b
o
th
m
en
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d
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wh
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f
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n
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d
s
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ctu
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r
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d
ly
class
if
ied
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to
c
o
r
o
n
ar
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h
ea
r
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d
is
ea
s
e
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C
HD)
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h
ea
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f
ailu
r
e,
v
alv
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d
is
ea
s
e,
an
d
ar
r
h
y
th
m
ias
[
1
]
.
Ge
n
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ally
,
t
h
e
h
ea
r
t
is
th
e
s
ize
o
f
a
f
is
t,
an
d
it
b
ea
ts
7
2
tim
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er
m
in
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te
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o
r
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n
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y
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b
l
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n
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it.
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h
e
m
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te
b
lo
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d
v
ess
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n
s
titu
te
th
e
b
lo
o
d
an
d
o
x
y
g
en
r
eq
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ir
ed
f
o
r
th
e
f
u
n
ctio
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g
o
f
th
e
h
ea
r
t.
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h
e
n
a
r
r
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win
g
o
f
b
lo
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d
v
es
s
els
f
lo
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g
to
th
e
h
ea
r
t
lead
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t
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also
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led
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r
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n
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y
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r
ter
y
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is
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s
e
(
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AD)
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co
n
d
itio
n
i
n
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ich
th
e
h
ea
r
t
m
u
s
cle
d
o
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t
p
u
m
p
en
o
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g
h
b
lo
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d
t
o
th
e
h
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ab
n
o
r
m
al
h
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r
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n
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g
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lled
h
ea
r
t
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ailu
r
e
(
s
y
s
to
lic
an
d
d
iast
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lic)
.
T
h
e
f
o
u
r
ty
p
es
o
f
h
ea
r
t
v
alv
es
ar
e
m
itra
l,
t
r
icu
s
p
i
d
,
ao
r
tic,
a
n
d
p
u
lm
o
n
ar
y
,
wh
i
ch
lo
o
k
f
o
r
b
lo
o
d
to
f
lo
w
in
o
n
ly
o
n
e
d
i
r
ec
tio
n
p
r
o
p
er
ly
with
th
e
o
p
en
/clo
s
ed
v
alv
e
all
th
e
way
.
W
h
en
an
y
o
r
m
o
r
e
o
f
t
h
e
h
ea
r
t
v
alv
es
f
ail
to
f
u
n
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it
lead
s
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ea
s
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lled
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eg
u
r
g
it
atio
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ten
o
s
is
,
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d
a
r
tesi
a
.
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ally
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e
h
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r
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r
ate
r
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ain
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c
o
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tan
t
in
h
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ce
p
t
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n
d
itio
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s
s
u
ch
as
d
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r
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p
h
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ic
al
ac
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,
wh
e
n
it
m
ay
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q
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ic
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ly
,
a
n
d
wh
ile
s
leep
in
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wh
en
it
b
ea
ts
s
lo
wly
.
I
f
t
h
e
h
ea
r
t
b
ea
ts
to
o
q
u
ick
ly
o
r
s
lo
wly
d
u
r
in
g
a
p
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s
o
n
’
s
r
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g
u
lar
ac
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v
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,
it
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s
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an
ir
r
eg
u
lar
r
h
y
th
m
ca
l
led
ar
r
h
y
th
m
ias.
Pre
s
en
tly
,
th
e
d
iag
n
o
s
es
o
f
h
ea
r
t
d
is
ea
s
e
ar
e
d
o
n
e
u
s
in
g
a
n
ar
r
a
y
o
f
lab
o
r
ato
r
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test
s
an
d
im
ag
in
g
s
tu
d
ies
lik
e
elec
tr
o
ca
r
d
io
g
r
am
(
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C
G)
,
h
o
lter
m
o
n
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r
in
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,
ec
h
o
ca
r
d
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g
r
am
,
ex
er
cise
o
r
s
tr
ess
test
s
,
ca
r
d
iac
ca
th
eter
izatio
n
,
ca
r
d
iac
C
T
s
ca
n
an
d
ca
r
d
iac
m
ag
n
etic
r
eso
n
a
n
ce
im
a
g
in
g
(
MRI)
s
ca
n
[
2
]
.
All
th
ese
test
s
r
eq
u
ir
e
tim
e
f
o
r
th
e
p
h
y
s
ician
to
tr
ea
t
th
e
p
atien
t
b
y
k
n
o
win
g
th
e
test
r
esu
lts
.
T
h
e
p
atien
t
’
s
co
n
d
itio
n
is
cr
u
cia
l,
an
d
an
ac
cu
r
ate
d
iag
n
o
s
is
i
s
n
ec
ess
ar
y
to
s
av
e
Evaluation Warning : The document was created with Spire.PDF for Python.
I
n
d
o
n
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J
E
lec
E
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g
&
C
o
m
p
Sci
I
SS
N:
2502
-
4
7
5
2
A
h
yb
r
id
in
tellig
en
t
mo
d
el
fo
r
p
r
ed
ictio
n
o
f c
o
r
o
n
a
r
y
a
r
tery
… (
N
ived
ith
a
H
o
n
n
ema
d
u
R
u
d
r
esh
g
o
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d
a
)
157
th
e
h
u
m
an
life
with
in
th
at
s
tip
u
lated
p
er
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d
.
So
h
e
r
e,
we
n
ee
d
a
p
r
ed
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n
m
o
d
el
to
p
r
ed
i
ct
h
ea
r
t
d
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ea
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in
h
u
m
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m
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th
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p
h
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s
ician
to
tr
e
at
th
e
p
atien
t
an
d
e
n
f
o
r
ce
th
e
m
o
d
e
r
n
m
ac
h
in
e
lear
n
in
g
tech
n
o
lo
g
y
(
ML
)
.
ML
p
lay
s
a
cr
u
cial
r
o
le
i
n
d
e
tectin
g
an
d
p
r
ed
ictin
g
h
u
m
a
n
h
ea
r
t
d
is
ea
s
es
[
3
]
.
I
t
is
o
n
e
o
f
th
e
m
o
s
t
ch
allen
g
in
g
task
s
to
p
r
ed
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h
ea
r
t
d
is
ea
s
e
d
u
e
to
its
m
o
r
e
d
ep
en
d
en
t
1
6
p
ar
am
eter
s
s
u
ch
as
b
lo
o
d
p
r
ess
u
r
e,
h
y
p
er
lip
id
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b
o
d
y
m
ass
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s
ex
,
ag
e,
tr
ig
ly
ce
r
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HDL
,
L
DL
,
h
ea
r
t
r
ate,
cr
ea
tin
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e,
d
iag
n
o
s
is
,
f
am
ily
h
is
to
r
y
,
s
m
o
k
in
g
,
d
iab
etes,
c
h
o
lest
er
o
l,
an
d
g
lu
co
s
e.
Pre
d
i
ctin
g
th
e
f
u
tu
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r
is
k
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h
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p
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ea
r
t
d
is
ea
s
e
h
elp
s
p
atien
ts
tak
e
p
r
ec
au
tio
n
ar
y
m
ea
s
u
r
es,
an
d
p
h
y
s
ic
ian
s
o
p
t
f
o
r
s
p
ec
if
ic
tr
e
atm
en
t
b
y
m
ak
in
g
p
r
io
r
ity
d
ec
is
io
n
s
.
Pre
d
ictiv
e
m
o
d
els
ca
n
b
e
class
if
ied
in
to
two
ca
teg
o
r
ies:
class
if
ic
atio
n
m
o
d
els
an
d
r
eg
r
ess
io
n
m
o
d
els,
wh
ich
p
e
r
f
o
r
m
b
ased
o
n
s
tatis
tical
an
aly
s
is
an
d
d
ata
m
in
in
g
tech
n
iq
u
es
[
4
]
.
T
h
e
m
o
s
t
co
m
m
o
n
l
y
u
s
ed
p
r
e
d
ictiv
e
m
o
d
els
ar
e
d
ev
elo
p
e
d
f
r
o
m
r
eg
r
ess
io
n
,
d
ec
is
io
n
tr
ee
s
(
DT
)
,
a
n
d
n
e
u
r
al
n
etwo
r
k
s
.
Oth
er
class
if
ier
s
s
u
ch
as
clu
s
ter
in
g
,
tim
e
s
er
ies,
o
u
tlier
d
ete
ctio
n
,
en
s
em
b
le,
f
ac
to
r
an
al
y
s
is
,
N
aïv
e
B
ay
es,
an
d
s
u
p
p
o
r
t
v
ec
to
r
m
ac
h
in
e
(
SV
M)
ca
n
also
b
e
u
s
e
d
[
5
]
.
E
ac
h
class
if
ier
u
n
iq
u
ely
ap
p
r
o
ac
h
es
th
e
d
ata,
s
o
ch
o
o
s
in
g
s
u
itab
le
class
if
ier
s
f
o
r
th
e
m
o
d
el
is
ch
allen
g
in
g
f
o
r
th
e
r
esear
ch
er
s
.
I
n
th
is
r
esear
ch
,
we
aim
to
d
e
v
elo
p
a
h
u
m
an
C
AD
p
r
e
d
ictio
n
m
o
d
el,
co
n
s
id
er
in
g
1
6
p
ar
a
m
eter
s
th
at
d
ir
ec
tly
o
r
in
d
ir
ec
tl
y
ca
u
s
e
r
is
k
to
th
e
h
ea
r
t.
Hy
b
r
id
lear
n
in
g
alg
o
r
ith
m
s
,
wh
ich
in
teg
r
ate
m
u
ltip
le
wea
k
m
o
d
els
in
to
a
r
o
b
u
s
t
f
r
am
ewo
r
k
,
f
ac
ilit
ate
co
m
p
r
e
h
en
s
iv
e
ex
p
lo
r
atio
n
o
f
in
tr
icate
in
ter
co
n
n
ec
tio
n
s
am
o
n
g
d
iv
er
s
e
f
ea
tu
r
es
with
in
c
o
n
s
tr
ain
ed
f
ea
t
u
r
e
v
alu
es
an
d
s
am
p
le
s
izes.
T
h
is
ap
p
r
o
ac
h
en
h
a
n
ce
s
th
e
p
r
ed
ictiv
e
m
o
d
el
’
s
ass
ess
m
en
t
m
etr
ic
s
a
n
d
d
eliv
er
s
v
alu
ab
le
s
u
p
p
lem
en
tar
y
ass
is
tan
ce
f
o
r
C
AD
d
i
ag
n
o
s
is
[
6
]
.
I
t
h
elp
s
to
p
r
o
v
id
e
cu
s
to
m
ized
th
e
r
ap
y
f
o
r
p
atien
ts
with
s
u
f
f
icien
t
ti
m
e
f
o
r
th
e
p
h
y
s
ician
to
tak
e
a
p
p
r
o
p
r
iate
m
ea
s
u
r
es
b
y
en
h
a
n
cin
g
o
v
er
all
ca
r
d
i
ac
ca
r
e
an
d
o
p
tim
izin
g
r
e
s
o
u
r
ce
allo
ca
tio
n
[
7
]
.
T
h
e
d
ataset
u
s
ed
f
o
r
ex
p
er
im
en
tatio
n
was c
o
llected
in
an
d
ar
o
u
n
d
th
e
h
o
s
p
itals
lo
ca
ted
in
My
s
o
r
e
an
d
Ma
n
d
y
a
r
eg
io
n
s
,
Kar
n
atak
a,
I
n
d
ia.
Data
s
ets we
r
e
co
m
p
u
te
d
an
d
a
n
aly
ze
d
o
n
Go
o
g
le
C
o
l
ab
u
s
in
g
Py
th
o
n
[
8
]
.
2.
L
I
T
E
R
AT
U
RE
SU
RVE
Y
Ma
n
y
em
in
en
t
r
esear
ch
e
r
s
in
ML
h
av
e
attem
p
ted
to
p
r
e
d
ict
h
u
m
an
h
ea
r
t
d
is
ea
s
es.
Hea
r
t
d
is
ea
s
es
ar
e
m
o
s
t
o
f
th
e
d
o
m
in
an
t
d
is
ea
s
es
lead
in
g
to
m
o
r
tality
in
d
ev
el
o
p
ed
n
atio
n
s
.
T
h
e
m
o
s
t
r
elev
an
t
wo
r
k
ca
r
r
ied
o
u
t
s
o
f
ar
in
th
e
p
r
ed
ictio
n
o
f
h
ea
r
t d
is
ea
s
es in
h
u
m
an
s
is
d
is
cu
s
s
ed
h
er
e.
Ud
d
in
et
a
l.
[9
]
d
e
v
elo
p
ed
a
m
o
d
el
to
d
iag
n
o
s
e
ca
r
d
io
v
asc
u
lar
d
is
ea
s
es
u
s
in
g
a
ML
ap
p
r
o
ac
h
.
Fo
r
th
e
ex
p
er
im
en
tatio
n
,
th
r
ee
d
ataset
ca
teg
o
r
ies
wer
e
co
llec
ted
f
r
o
m
th
e
h
ea
r
t
d
is
ea
s
e
d
ataset
R
ep
o
s
ito
r
y
Un
iv
er
s
ity
o
f
C
alif
o
r
n
ia
I
r
v
in
e
(
UC
I
)
co
n
tain
in
g
1
4
attr
ib
u
tes,
I
E
E
E
Data
Po
r
t
co
n
tain
in
g
1
2
attr
ib
u
tes,
an
d
Kag
g
le
co
n
tain
in
g
1
2
attr
ib
u
te
s
.
T
h
e
au
th
o
r
s
im
p
lem
en
te
d
ML
m
o
d
els
lik
e
SVM,
r
an
d
o
m
f
o
r
est
(
R
F)
,
m
u
ltil
ay
er
p
er
c
ep
tr
o
n
(
ML
P),
DT
,
ex
tr
e
m
e
g
r
ad
ien
t
b
o
o
s
t
(
XGBo
o
s
t)
,
g
r
ad
ien
t
b
o
o
s
tin
g
,
an
d
lig
h
t
g
r
ad
ien
t
b
o
o
s
tin
g
m
ac
h
in
e
class
if
ier
to
class
if
y
h
ea
r
t
d
is
ea
s
es.
B
ef
o
r
e
a
p
p
ly
in
g
th
e
d
atasets
t
o
p
r
o
ce
s
s
in
g
,
p
r
e
-
p
r
o
ce
s
s
in
g
s
tag
es
lik
e
clea
n
i
n
g
,
tr
a
n
s
f
o
r
m
atio
n
,
in
te
g
r
atio
n
,
an
d
r
e
d
u
ctio
n
wer
e
ca
r
r
ie
d
o
u
t.
T
h
e
m
o
d
el
’
s
p
er
f
o
r
m
an
ce
was
ev
alu
ate
d
th
r
o
u
g
h
p
r
ec
is
io
n
,
r
ec
all,
ac
cu
r
a
cy
r
ate,
r
ec
ei
v
er
o
p
er
atin
g
ch
a
r
ac
ter
is
tics
(
R
O
C
)
cu
r
v
e,
an
d
F1
-
s
co
r
e.
T
h
e
DT
m
o
d
el
o
u
tp
er
f
o
r
m
s
o
th
er
m
o
d
els
f
o
r
th
e
co
m
b
in
ed
d
atasets
o
f
UC
I
,
I
E
E
E
,
a
n
d
Kag
g
le.
A
li
et
a
l.
[
1
0
]
aim
ed
at
d
ev
el
o
p
in
g
s
u
p
er
v
is
ed
ML
alg
o
r
ith
m
s
f
o
r
th
e
p
r
ed
ictio
n
o
f
h
ea
r
t d
is
ea
s
es a
n
d
co
m
p
ar
ed
th
e
m
o
d
el
’
s
p
er
f
o
r
m
an
ce
f
o
r
th
e
h
ig
h
est
ac
cu
r
ac
y
.
Fo
r
e
x
p
er
im
en
tatio
n
p
u
r
p
o
s
es,
a
d
ataset
f
r
o
m
th
e
Kag
g
le
co
n
tain
ed
1
4
attr
ib
u
tes
o
f
1
,
0
2
5
p
atien
ts
,
in
clu
d
in
g
3
1
2
f
e
m
ales
an
d
7
1
3
m
al
es
with
an
d
with
o
u
t
d
is
ea
s
es.
T
o
class
if
y
h
ea
r
t
d
is
ea
s
es,
th
e
au
th
o
r
s
im
p
lem
e
n
te
d
s
ix
ML
alg
o
r
ith
m
s
lik
e
RF
, K
-
n
ea
r
est
n
eig
h
b
o
r
s
(
KNN)
,
ML
P,
DT
,
Ad
ab
o
o
s
tM1
,
an
d
lo
g
is
tic
r
eg
r
ess
io
n
(
L
R
)
class
if
ier
s
.
T
h
e
d
atasets
we
r
e
p
r
e
-
p
r
o
ce
s
s
ed
b
y
ap
p
ly
in
g
a
f
ilter
to
r
ep
lace
m
i
s
s
in
g
v
alu
es
k
n
o
wn
as
th
e
in
t
er
q
u
ar
tile
r
an
g
e
(
I
QR
)
to
g
et
b
etter
s
tatis
t
ical
an
d
an
aly
tical
r
esu
lts
.
T
h
e
class
if
icatio
n
alg
o
r
ith
m
s
wer
e
e
v
alu
ated
th
r
o
u
g
h
p
r
ec
is
io
n
,
r
ec
all,
F
-
m
ea
s
u
r
e
,
s
en
s
itiv
ity
,
s
p
ec
if
icity
,
an
d
k
ap
p
a
m
etr
ics.
T
h
e
RF
,
KNN,
an
d
DT
p
er
f
o
r
m
b
etter
th
an
o
th
er
class
if
icatio
n
alg
o
r
ith
m
s
.
Gao
et
a
l.
[
1
1
]
wo
r
k
ed
o
n
en
h
an
cin
g
th
e
p
er
f
o
r
m
a
n
ce
o
f
th
e
h
ea
r
t
d
is
ea
s
e
p
r
ed
ictio
n
m
o
d
e
l
with
th
e
en
s
em
b
le
lear
n
in
g
m
eth
o
d
.
Fo
r
ex
p
e
r
im
en
tatio
n
p
u
r
p
o
s
e
s
,
1
,
0
2
5
d
atasets
wer
e
co
lle
cted
co
n
tain
in
g
1
3
f
ea
tu
r
es
to
class
if
y
f
o
r
h
ea
r
t
d
is
ea
s
e
an
d
n
o
n
-
h
ea
r
t
d
is
ea
s
e
f
r
o
m
th
e
C
lev
elan
d
h
ea
r
t
d
i
s
ea
s
e
d
ataset.
T
h
e
au
th
o
r
s
im
p
lem
e
n
ted
th
e
p
r
o
p
o
s
ed
m
o
d
el
u
s
in
g
f
o
u
r
ML
alg
o
r
ith
m
s
,
s
u
ch
as
KNN,
DT
,
RF
an
d
N
aïv
e
B
ay
es
an
d
two
en
s
em
b
le
al
g
o
r
ith
m
s
,
b
o
o
s
tin
g
an
d
b
a
g
g
in
g
,
to
class
if
y
h
ea
r
ts
as
h
ea
lth
y
o
r
u
n
h
e
alth
y
.
T
h
e
d
atasets
wer
e
p
r
e
-
p
r
o
ce
s
s
ed
to
d
elete
th
e
m
is
s
in
g
v
alu
es
an
d
ex
tr
ac
ted
f
ea
tu
r
es
u
s
in
g
lin
ea
r
d
is
cr
im
in
an
t
an
aly
s
is
(
L
DA)
an
d
p
r
in
cip
al
co
m
p
o
n
e
n
t a
n
aly
s
is
(
P
C
A)
.
Ou
t o
f
wh
i
ch
,
7
5
% o
f
th
e
d
atasets
wer
e
u
s
ed
f
o
r
tr
ain
in
g
th
e
m
o
d
el
u
s
in
g
n
in
e
-
f
o
ld
c
r
o
s
s
-
v
alid
atio
n
,
a
n
d
2
5
%
wer
e
u
s
ed
to
test
th
e
m
o
d
el
u
s
in
g
e
v
alu
atio
n
m
etr
ics,
n
am
ely
ac
cu
r
ac
y
,
r
ec
all,
F
-
s
co
r
e,
R
OC
,
a
r
ea
u
n
d
er
th
e
cu
r
v
e
(
AUC)
an
d
p
r
ec
is
io
n
.
T
h
e
two
en
s
em
b
le
alg
o
r
ith
m
s
p
er
f
o
r
m
b
etter
th
an
o
th
er
ML
alg
o
r
ith
m
s
f
o
r
th
e
P
C
A
f
ea
tu
r
e
ex
tr
ac
tio
n
m
et
h
o
d
.
C
h
an
g
et
a
l.
[
1
2
]
d
e
v
elo
p
e
d
an
ar
tific
ial
in
tellig
en
ce
m
o
d
el
to
d
etec
t
h
ea
r
t
d
is
ea
s
e
u
s
in
g
ML
alg
o
r
ith
m
s
.
Fo
r
ex
p
er
im
e
n
tatio
n
p
u
r
p
o
s
es,
th
e
d
atasets
w
er
e
co
llected
f
r
o
m
th
e
p
atien
t
’
s
m
ed
ical
h
is
to
r
y
Evaluation Warning : The document was created with Spire.PDF for Python.
I
SS
N
:
2502
-
4
7
5
2
I
n
d
o
n
esian
J
E
lec
E
n
g
&
C
o
m
p
Sci
,
Vo
l.
40
,
No
.
1
,
Octo
b
er
20
25
:
1
56
-
1
63
158
co
n
tain
in
g
f
i
v
e
p
ar
am
eter
s
f
o
r
th
e
p
r
ed
ictio
n
o
f
h
ea
r
t
d
is
ea
s
es.
T
h
e
au
th
o
r
s
im
p
lem
en
te
d
th
e
p
r
o
p
o
s
ed
m
o
d
el
u
s
in
g
ML
alg
o
r
ith
m
s
,
s
u
ch
as
th
e
K
-
n
eig
h
b
o
r
s
class
if
ier
,
DT
class
if
ier
,
SVM
,
RF
cla
s
s
if
ier
,
an
d
LR
to
p
r
ed
ict
an
d
class
if
y
h
ea
r
t
d
is
ea
s
es.
T
h
e
d
atasets
co
n
tain
in
g
1
4
f
ea
tu
r
es
f
r
o
m
th
e
1
0
0
p
e
r
s
o
n
s
wer
e
co
llected
to
class
if
y
f
o
r
h
ea
r
t
d
is
ea
s
e
an
d
n
o
n
-
h
ea
r
t
d
is
ea
s
e
b
ased
o
n
th
e
test
r
ep
o
r
ts
.
T
h
e
K
-
n
eig
h
b
o
r
s
class
if
ier
p
er
f
o
r
m
s
b
etter
co
m
p
a
r
ed
to
o
th
er
ML
a
lg
o
r
ith
m
s
.
Kar
th
ick
et
a
l.
[
1
3
]
im
p
lem
en
ted
a
ca
r
d
io
v
ascu
lar
d
is
ea
s
e
r
i
s
k
p
r
ed
ictio
n
u
s
in
g
ML
alg
o
r
ith
m
s
.
Fo
r
ex
p
er
im
en
tati
o
n
p
u
r
p
o
s
es,
th
e
d
atasets
wer
e
co
llec
ted
f
r
o
m
th
e
C
lev
elan
d
h
ea
r
t
d
is
ea
s
e
co
n
tain
in
g
1
3
s
elec
ted
f
ea
tu
r
es
f
r
o
m
t
h
e
3
0
3
d
ata
in
s
tan
ce
s
.
T
h
e
a
u
th
o
r
s
im
p
lem
en
ted
th
e
p
r
o
p
o
s
ed
m
o
d
el
u
s
in
g
s
ix
ML
alg
o
r
ith
m
s
,
s
u
ch
as
SVM,
LR
,
g
au
s
s
ian
Naïv
e
B
ay
es,
L
i
g
h
tGB
M,
XGBo
o
s
t
an
d
RF
,
to
p
r
ed
ict
th
e
h
ea
r
t
d
is
ea
s
e
r
is
k
.
Ou
t
o
f
th
e
av
ail
ab
le
d
atasets
,
8
0
%
wer
e
u
s
ed
f
o
r
tr
ain
in
g
th
e
m
o
d
el,
an
d
th
e
r
em
ain
in
g
2
0
%
wer
e
u
s
ed
f
o
r
test
in
g
t
h
e
m
o
d
el
an
d
ev
alu
atin
g
th
e
m
o
d
el
f
o
r
ac
cu
r
ac
y
u
s
in
g
C
h
i
-
Sq
u
ar
e
d
is
tr
ib
u
tio
n
.
T
h
e
RF
m
o
d
el
p
er
f
o
r
m
s
b
etter
co
m
p
a
r
ed
to
o
th
e
r
ML
m
o
d
els.
Do
p
p
ala
et
a
l.
[
1
4
]
d
ev
elo
p
e
d
a
h
y
b
r
i
d
ML
alg
o
r
ith
m
to
p
r
ed
ict
co
r
o
n
a
r
y
d
is
ea
s
es
u
s
in
g
th
e
f
ea
tu
r
e
s
elec
tio
n
m
eth
o
d
o
n
th
e
h
ea
r
t
d
ataset.
Fo
r
ex
p
er
im
e
n
tatio
n
p
u
r
p
o
s
es,
th
e
d
atasets
wer
e
co
llected
f
r
o
m
th
e
C
lev
elan
d
h
ea
r
t
d
is
ea
s
e
co
n
tain
in
g
1
4
s
elec
ted
f
ea
tu
r
e
s
f
r
o
m
th
e
3
0
3
d
ata
in
s
tan
ce
s
.
T
h
e
au
th
o
r
s
im
p
lem
en
ted
t
h
e
p
r
o
p
o
s
ed
al
g
o
r
ith
m
u
s
in
g
m
ac
h
in
e
alg
o
r
i
th
m
s
,
s
u
ch
as
Naïv
e
B
ay
es,
DT
,
LR
,
SVM,
RF
,
KNN
an
d
p
r
o
p
o
s
ed
g
en
etic
al
g
o
r
ith
m
(
GA)
with
r
ad
ial
b
asis
f
u
n
ctio
n
(
R
B
F)
to
p
r
ed
ict
C
HD
.
Fo
r
tr
ain
in
g
,
7
0
%
o
f
th
e
d
atasets
wer
e
u
s
ed
,
an
d
th
e
r
em
ain
in
g
3
0
%
wer
e
u
s
ed
f
o
r
test
in
g
th
e
m
o
d
el.
T
h
e
p
r
o
p
o
s
ed
m
o
d
el
GA
with
R
B
F p
er
f
o
r
m
s
b
etter
th
an
th
e
o
t
h
er
ML
m
o
d
els.
Fro
m
th
e
s
ev
er
al
s
tate
-
of
-
th
e
-
ar
t
wo
r
k
s
,
th
e
p
r
ed
ictio
n
o
f
co
r
o
n
a
r
y
a
r
ter
y
h
ea
r
t
d
is
ea
s
es
is
in
th
e
in
f
an
cy
o
f
th
e
r
ea
l
-
tim
e
im
p
le
m
en
tatio
n
o
f
th
e
m
o
d
el.
Her
e,
alg
o
r
ith
m
s
n
ee
d
to
b
e
im
p
r
o
v
ed
o
r
u
p
g
r
ad
ed
f
o
r
th
e
b
etter
m
en
t
o
f
t
h
e
ap
p
licatio
n
.
So
,
th
is
f
ield
attr
ac
ts
m
an
y
em
in
en
t
an
d
y
o
u
n
g
r
esear
ch
er
s
,
s
h
o
win
g
am
p
le
oppo
r
tu
n
ity
f
o
r
th
e
r
ea
l
-
tim
e
p
r
ed
ictio
n
o
f
C
HD
.
3.
M
E
T
H
O
D
E
x
p
lain
in
g
in
th
e
p
r
o
p
o
s
ed
m
eth
o
d
,
th
e
p
r
e
d
ictio
n
o
f
C
HD
is
d
o
n
e
th
r
o
u
g
h
two
s
ta
g
es
s
u
ch
as
f
ea
tu
r
e
ex
tr
ac
tio
n
a
n
d
class
if
icatio
n
,
as
s
h
o
wn
in
Fig
u
r
e
1
.
I
n
itially
,
th
e
d
atasets
co
llected
ar
o
u
n
d
th
e
My
s
o
r
e
an
d
Ma
n
d
y
a
r
e
g
io
n
s
wer
e
p
r
e
-
p
r
o
ce
s
s
ed
to
n
o
r
m
alize
,
en
co
d
e,
an
d
h
a
n
d
le
m
is
s
in
g
v
a
lu
es
to
ex
tr
a
ct
th
e
f
ea
tu
r
es
u
s
in
g
T
ab
Net
[
1
5
]
.
Du
r
in
g
t
h
e
s
ec
o
n
d
s
tag
e,
th
e
ex
tr
ac
ted
f
ea
t
u
r
es
ar
e
p
r
o
ce
s
s
ed
b
y
co
n
ca
ten
atin
g
f
ea
tu
r
es in
to
a
u
n
if
ied
f
ea
t
u
r
e
v
ec
to
r
to
tr
ai
n
th
e
m
u
lticlas
s
S
VM
m
o
d
el
[
1
6
].
Fig
u
r
e
1
.
Pro
p
o
s
ed
m
o
d
el
f
o
r
t
h
e
p
r
ed
ictio
n
o
f
C
HD
3
.
1
.
Da
t
a
s
et
Fo
r
th
e
e
x
p
er
im
e
n
tatio
n
p
u
r
p
o
s
e,
co
n
s
id
er
in
g
1
6
f
ea
t
u
r
es
c
au
s
in
g
C
ADs
d
ir
ec
tly
o
r
in
d
ir
ec
tly
,
s
u
ch
as
b
lo
o
d
p
r
ess
u
r
e,
h
y
p
e
r
lip
id
em
ia,
b
o
d
y
m
ass
,
s
ex
,
ag
e,
tr
ig
ly
ce
r
id
e,
HDL
,
L
DL
,
h
ea
r
t
r
ate,
cr
ea
tin
i
n
e,
d
iag
n
o
s
is
,
f
am
ily
h
is
to
r
y
,
s
m
o
k
in
g
,
d
iab
etes
,
ch
o
lest
er
o
l
,
an
d
g
lu
c
o
s
e
ar
e
co
llected
in
an
d
ar
o
u
n
d
t
h
e
h
o
s
p
itals
lo
ca
ted
i
n
My
s
o
r
e
a
n
d
Ma
n
d
y
a
r
eg
i
o
n
s
,
Kar
n
atak
a,
I
n
d
ia.
A
to
tal
o
f
2
8
2
d
atasets
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n
tain
in
g
all
1
6
f
ea
tu
r
es
wer
e
co
llected
,
wh
er
e
2
1
3
d
atasets
wer
e
f
r
o
m
h
o
s
p
itals
in
th
e
My
s
o
r
e
r
eg
io
n
,
Kar
n
atak
a,
I
n
d
ia
an
d
6
9
d
atasets
f
r
o
m
h
o
s
p
itals
in
t
h
e
Ma
n
d
y
a
r
eg
io
n
,
Kar
n
atak
a
,
I
n
d
ia.
T
h
e
c
o
r
r
elatio
n
am
o
n
g
th
e
1
6
f
ea
t
u
r
es
is
in
ter
p
r
eted
with
a
co
ef
f
icien
t
r
an
g
e
r
an
g
in
g
b
etwe
en
-
1
to
+1
,
wh
er
e
-
1
in
d
icate
s
th
e
s
tr
o
n
g
n
eg
ativ
e
co
r
r
elatio
n
a
n
d
+
1
in
d
icate
s
th
e
s
tr
o
n
g
p
o
s
itiv
e
c
o
r
r
elatio
n
.
Fig
u
r
e
2
s
h
o
ws
th
at
p
o
s
itiv
e
v
alu
e
am
o
n
g
th
e
f
ea
tu
r
es,
wh
ich
in
d
icate
s
th
at
th
e
f
ea
tu
r
es a
r
e
m
o
v
in
g
in
th
e
s
am
e
d
ir
ec
tio
n
.
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:
2502
-
4
7
5
2
A
h
yb
r
id
in
tellig
en
t
mo
d
el
fo
r
p
r
ed
ictio
n
o
f c
o
r
o
n
a
r
y
a
r
tery
… (
N
ived
ith
a
H
o
n
n
ema
d
u
R
u
d
r
esh
g
o
w
d
a
)
159
Fig
u
r
e
2
.
C
o
r
r
elatio
n
m
atr
ix
f
o
r
th
e
h
ea
r
t d
is
ea
s
e
f
ea
tu
r
es
3
.
2
.
P
re
-
pro
ce
s
s
ing
T
h
e
d
atasets
co
llected
ar
e
ta
b
u
lar
d
ata
p
r
e
p
r
o
ce
s
s
ed
to
ac
h
iev
e
th
e
m
o
d
el
’
s
o
p
tim
al
p
e
r
f
o
r
m
a
n
ce
.
B
ef
o
r
e
ap
p
ly
in
g
to
T
ab
Net,
th
e
tab
u
lar
d
ataset
is
p
r
ep
r
o
ce
s
s
ed
b
y
f
ea
tu
r
e
s
ca
lin
g
,
d
ata
clea
n
in
g
an
d
en
c
o
d
in
g
ca
teg
o
r
ical
f
ea
tu
r
es.
I
n
f
ea
tu
r
e
s
ca
lin
g
,
th
e
n
o
r
m
aliza
tio
n
m
e
th
o
d
is
ap
p
lied
to
s
ca
le
th
e
n
u
m
er
ical
f
ea
tu
r
es
f
r
o
m
0
to
1
f
r
o
m
th
e
d
if
f
e
r
en
t
r
an
g
es.
T
h
e
d
ata
clea
n
in
g
m
e
th
o
d
h
an
d
les
th
e
m
is
s
in
g
v
alu
es
b
y
ap
p
ly
i
n
g
t
h
e
im
p
u
tatio
n
a
n
d
d
eletio
n
[
1
7
]
.
T
h
e
en
c
o
d
in
g
ca
teg
o
r
ical
f
ea
t
u
r
es
m
eth
o
d
co
n
v
er
ts
th
e
ca
teg
o
r
ical
v
al
u
e
s
in
to
b
in
a
r
y
v
alu
es
b
y
o
n
e
-
h
o
t e
n
c
o
d
in
g
[
1
8
].
3
.
3
.
T
a
bNet
T
ab
Net
was
d
ev
elo
p
ed
b
y
Go
o
g
le
C
lo
u
d
AI
r
esear
ch
er
s
to
h
an
d
le
co
m
p
lex
tab
u
lar
d
ata
m
o
r
e
ef
f
ec
tiv
ely
co
m
p
ar
ed
t
o
g
r
a
d
ien
t
b
o
o
s
t
an
d
DT
m
o
d
els.
T
ab
Net
u
s
es
s
p
ar
s
e
atten
tio
n
an
d
a
f
ea
tu
r
e
tr
an
s
f
o
r
m
e
r
m
ec
h
an
is
m
,
wh
ich
h
elp
s
to
f
o
cu
s
o
n
th
e
m
o
s
t
r
elev
an
t
f
ea
tu
r
es
f
o
r
d
ec
is
io
n
b
y
im
p
r
o
v
i
n
g
in
te
r
p
r
etab
ilit
y
an
d
p
e
r
f
o
r
m
a
n
ce
[
1
9
]
.
T
h
e
f
ea
tu
r
e
s
elec
tio
n
u
s
in
g
T
ab
Net
is
s
h
o
wn
i
n
Fig
u
r
e
3
,
wh
ich
co
n
s
is
ts
o
f
m
u
lti
-
s
tep
s
eq
u
en
tial
o
p
er
atio
n
s
o
n
e
af
ter
t
h
e
o
th
er
.
I
n
t
h
e
in
itial
s
tep
,
all
th
e
p
r
e
-
p
r
o
ce
s
s
ed
d
atasets
wer
e
g
iv
en
to
th
e
m
o
d
el
to
p
ass
th
r
o
u
g
h
th
e
f
ea
tu
r
e
tr
an
s
f
o
r
m
er
[
2
0
]
.
T
h
e
f
ea
tu
r
e
tr
an
s
f
o
r
m
er
co
n
s
is
ts
o
f
a
g
ate
lin
ea
r
u
n
it
(
GL
U)
b
lo
ck
;
ea
c
h
GL
U
b
lo
c
k
co
n
s
is
ts
o
f
a
f
u
lly
co
n
n
ec
te
d
(
FC
)
lay
er
,
b
atch
n
o
r
m
aliza
tio
n
(
B
N)
,
an
d
GL
U
as
s
h
o
wn
in
(
1
)
.
T
o
g
et
s
tab
ilit
y
an
d
m
ain
tain
v
ar
ian
ce
,
a
n
o
r
m
aliza
tio
n
o
f
0
.
2
5
is
ap
p
lied
af
ter
e
v
er
y
b
lo
ck
.
(
)
=
(
)
∙
(
1
)
Fig
u
r
e
3
.
T
a
b
Net
p
r
ed
ictio
n
f
o
r
f
ea
tu
r
e
s
elec
tio
n
Evaluation Warning : The document was created with Spire.PDF for Python.
I
SS
N
:
2502
-
4
7
5
2
I
n
d
o
n
esian
J
E
lec
E
n
g
&
C
o
m
p
Sci
,
Vo
l.
40
,
No
.
1
,
Octo
b
er
20
25
:
1
56
-
1
63
160
An
atten
tiv
e
tr
an
s
f
o
r
m
er
co
n
s
is
ts
o
f
a
FC
,
BN
,
Prio
r
Scale
s
,
an
d
Sp
ar
s
em
ax
lay
er
,
wh
ic
h
r
ec
eiv
es
d
ata
to
Prio
r
Scales
af
ter
p
ass
in
g
th
e
FC
an
d
B
N
lay
er
.
T
h
e
ag
g
r
e
g
atio
n
o
f
th
e
f
ea
tu
r
e
u
s
ed
till
th
e
p
r
o
ce
s
s
s
tep
is
tak
en
b
ef
o
r
e
Pri
o
r
S
ca
les
m
ak
es
th
e
cu
r
r
en
t
d
ec
is
io
n
.
Sp
ar
s
e
s
elec
tio
n
o
f
f
e
atu
r
es
is
d
o
n
e
b
y
n
o
r
m
al
izin
g
t
h
e
co
ef
f
icien
t v
a
lu
es [
2
1
]
,
w
h
ich
is
s
im
ilar
to
So
f
tMa
x
o
p
e
r
atio
n
as sh
o
wn
in
(
2
)
.
∑
(
)
=
1
=
1
∀
∈
(
2
)
T
h
e
o
u
t
p
u
t
f
r
o
m
th
e
atten
tiv
e
tr
an
s
f
o
r
m
e
r
s
tep
is
th
en
s
u
p
er
v
is
ed
b
y
th
e
atten
tio
n
m
a
s
k
,
wh
ich
h
elp
s
to
id
en
tify
th
e
s
elec
ted
f
ea
tu
r
es.
T
h
e
m
ask
q
u
an
tifie
s
th
e
im
p
o
r
tan
ce
o
f
a
g
g
r
e
g
ate
f
ea
tu
r
es
an
d
an
aly
ze
s
ea
ch
s
tep
,
allo
win
g
ag
g
r
e
g
ate
d
ec
is
io
n
s
as
g
iv
en
in
(
3
)
[
2
2
]
.
As
s
p
ec
if
ied
in
Fig
u
r
e
3
,
all
th
e
1
6
s
elec
ted
f
ea
tu
r
es
wer
e
p
r
esen
ted
to
f
ea
tu
r
e
s
elec
tio
n
,
wh
ich
u
n
d
er
wen
t
f
ea
tu
r
e
tr
an
s
f
o
r
m
e
r
an
d
at
ten
tiv
e
tr
an
s
f
o
r
m
er
p
r
o
ce
s
s
in
g
b
ef
o
r
e
ag
g
r
eg
atin
g
th
e
d
ec
is
io
n
o
f
f
ea
tu
r
es
.
[
]
=
∑
(
,
[
]
)
=
1
(
3
)
T
h
e
f
ea
tu
r
e
s
elec
tio
n
p
r
o
ce
s
s
was
co
m
p
leted
b
y
ap
p
ly
i
n
g
th
e
T
ab
Net
m
o
d
el;
n
e
x
t,
we
n
ee
d
to
g
o
f
o
r
th
e
p
r
ed
ictio
n
o
f
C
HD
.
Her
e,
t
h
e
m
o
d
el
n
ee
d
s
to
b
e
tr
ain
ed
an
d
test
ed
f
o
r
b
etter
p
e
r
f
o
r
m
an
ce
an
d
e
v
alu
ated
t
h
e
m
o
d
el
p
er
f
o
r
m
a
n
ce
u
s
in
g
ev
al
u
atio
n
m
etr
ics.
3
.
4
.
M
ulticla
s
s
SVM
C
o
m
b
in
ed
f
ea
t
u
r
es
ar
e
f
ed
to
th
e
SVM
alg
o
r
ith
m
f
o
r
d
e
tectin
g
C
HD
[
2
3
]
.
T
h
e
SVM
m
o
d
el
is
tr
ain
ed
to
class
if
y
th
e
d
is
ea
s
e,
as
s
h
o
wn
in
F
ig
u
r
e
4
.
I
n
th
e
tr
ain
in
g
p
h
ase,
th
e
m
o
d
el
is
tr
ain
ed
f
o
r
ea
ch
class
co
n
s
id
er
in
g
th
e
t
h
r
ee
class
es.
C
las
s
es
s
u
ch
as
C
las
s
0
:
No
d
is
ea
s
e,
C
lass
1
:
Mild
d
is
ea
s
e
an
d
C
lass
2
:
Sev
er
e
d
is
ea
s
e
b
ased
o
n
th
e
ex
tr
ac
te
d
f
ea
tu
r
e
v
alu
es.
E
ac
h
o
f
th
ese
class
e
s
wi
ll
b
e
tr
ain
ed
to
b
e
d
is
tin
g
u
is
h
ed
f
r
o
m
o
th
er
s
,
as
s
h
o
wn
in
Fig
u
r
e
4
.
Du
r
in
g
t
h
e
p
r
ed
ictio
n
p
h
a
s
e,
b
ased
o
n
th
e
p
r
o
b
ab
ilit
y
s
co
r
e
it
s
h
o
ws
th
e
lik
elih
o
o
d
o
f
in
s
t
an
ce
s
b
elo
n
g
in
g
to
a
class
with
r
esp
ec
tiv
e
class
es.
Fin
ally
,
th
e
m
ax
im
u
m
s
co
r
e
is
s
elec
ted
as
th
e
in
s
tan
ce
f
o
r
t
h
e
p
r
e
d
ictio
n
o
f
th
e
class
[
2
4
]
,
[
2
5
]
.
Fig
u
r
e
4
.
SVM
alg
o
r
ith
m
f
o
r
t
h
e
p
r
ed
ictio
n
o
f
C
HD
4.
RE
SU
L
T
S AN
D
D
I
SCU
SS
I
O
N
I
n
th
is
s
ec
tio
n
,
we
p
r
esen
t
th
e
s
im
u
latio
n
r
esu
lts
o
n
a
Go
o
g
le
C
o
lab
u
s
in
g
Py
th
o
n
f
o
r
t
h
e
d
atasets
co
llected
in
an
d
ar
o
u
n
d
th
e
h
o
s
p
itals
in
My
s
o
r
e
an
d
Ma
n
d
y
a
r
eg
io
n
s
,
Kar
n
atak
a,
I
n
d
ia.
As
s
p
ec
if
ied
in
th
e
d
ataset
s
ec
tio
n
,
h
er
e
we
h
av
e
co
llected
2
8
2
d
ataset
s
,
o
f
wh
i
ch
1
3
0
d
atasets
b
elo
n
g
t
o
class
0
,
8
6
to
class
1
,
an
d
t
h
e
r
e
m
ain
in
g
6
6
to
class
2
.
Ap
p
r
o
x
im
ately
5
0
%
o
f
t
h
e
av
ailab
le
d
atasets
in
ea
ch
class
wer
e
u
s
ed
to
tr
ain
th
e
m
o
d
el,
a
n
d
t
h
e
r
e
m
ain
in
g
was
u
s
ed
t
o
test
th
e
m
o
d
el
p
er
f
o
r
m
an
ce
.
So
m
e
d
atasets
wer
e
also
r
a
n
d
o
m
l
y
g
iv
en
,
wh
ich
a
r
e
n
o
t
c
o
n
s
id
er
ed
in
th
e
d
ataset
p
ar
t.
I
n
th
e
f
ir
s
t
p
h
ase,
th
e
f
ea
tu
r
es
ar
e
ag
g
r
eg
ated
u
s
in
g
th
e
T
ab
Net
m
o
d
el,
wh
ic
h
co
n
s
id
e
r
s
th
e
1
6
s
elec
ted
f
ea
tu
r
e
v
al
u
es
ca
u
s
in
g
C
ADs
d
ir
ec
tly
o
r
in
d
ir
ec
tly
,
s
u
ch
as
b
lo
o
d
p
r
ess
u
r
e,
h
y
p
e
r
lip
id
e
m
ia,
b
o
d
y
m
ass
,
s
ex
,
a
g
e,
tr
ig
ly
ce
r
id
e,
HDL
,
L
DL
,
h
e
ar
t
r
ate,
cr
ea
tin
i
n
e,
d
iag
n
o
s
is
,
f
am
ily
h
is
to
r
y
,
s
m
o
k
in
g
,
d
ia
b
etes,
ch
o
lest
er
o
l,
an
d
g
lu
co
s
e.
Du
r
in
g
th
e
s
ec
o
n
d
p
h
ase,
ex
tr
ac
te
d
f
ea
tu
r
es
wer
e
f
e
d
to
t
h
e
m
u
lt
iclass
SVM
m
o
d
el
f
o
r
th
e
p
r
ed
ictio
n
o
f
th
e
class
es
th
at
b
elo
n
g
b
ased
o
n
th
e
h
ig
h
est s
co
r
e.
T
h
e
m
o
d
el
’
s
p
er
f
o
r
m
an
ce
is
e
v
alu
ated
u
s
in
g
e
v
alu
atio
n
m
et
r
ics
s
u
ch
as
p
r
ec
is
io
n
,
r
ec
all
,
F1
-
s
co
r
e,
an
d
ac
c
u
r
ac
y
,
an
d
th
e
r
esu
lts
ar
e
i
n
d
icate
d
i
n
T
a
b
le
1
.
C
lass
0
m
ea
n
s
th
e
p
r
ed
ictio
n
o
f
n
o
C
AD
d
is
ea
s
es
s
h
o
ws
a
r
esu
lt
o
f
p
r
ec
is
io
n
o
f
9
3
.
3
8
%,
r
ec
all
o
f
9
0
.
7
6
%,
F1
-
s
co
r
e
9
2
.
0
5
%,
an
d
ac
c
u
r
a
cy
o
f
9
0
%.
C
lass
1
in
d
icate
s
th
e
p
r
ed
ictio
n
o
f
m
ild
C
AD
d
is
ea
s
es,
with
9
1
.
8
6
%
p
r
ec
is
io
n
,
r
ec
all
at
8
9
.
5
3
%,
F1
-
s
co
r
e
at
9
0
.
6
8
%,
an
d
ac
cu
r
ac
y
at
8
8
.
3
7
%.
C
lass
2
in
d
icate
s
th
e
p
r
ed
ictio
n
o
f
s
ev
er
e
C
AD
d
is
ea
s
es,
wh
ich
s
h
o
ws
a
p
r
ec
is
io
n
o
f
9
0
.
9
0
%,
r
ec
all
o
f
8
7
.
2
0
%,
F1
-
s
co
r
e
8
9
.
0
1
%,
a
n
d
ac
c
u
r
ac
y
o
f
8
7
.
8
7
%.
Fig
u
r
e
5
s
h
o
ws
th
e
g
r
a
p
h
ical
r
ep
r
esen
tatio
n
o
f
th
e
r
esu
lt c
la
s
s
v
er
s
u
s
ev
alu
atio
n
m
etr
ics.
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:
2502
-
4
7
5
2
A
h
yb
r
id
in
tellig
en
t
mo
d
el
fo
r
p
r
ed
ictio
n
o
f c
o
r
o
n
a
r
y
a
r
tery
… (
N
ived
ith
a
H
o
n
n
ema
d
u
R
u
d
r
esh
g
o
w
d
a
)
161
T
ab
le
1
.
Per
f
o
r
m
an
ce
o
f
m
o
d
e
l
C
l
a
s
s
Pr
e
c
i
s
i
o
n
R
e
c
a
l
l
F1
-
sc
o
r
e
A
c
c
u
r
a
c
y
0
9
3
.
3
8
9
0
.
7
6
9
2
.
0
5
90
1
9
1
.
8
6
8
9
.
5
3
9
0
.
6
8
8
8
.
3
7
2
9
0
.
9
0
8
7
.
2
0
8
9
.
0
1
8
7
.
8
7
Fig
u
r
e
5
.
Gr
a
p
h
ical
r
ep
r
esen
tatio
n
o
f
p
er
f
o
r
m
an
ce
o
f
th
e
m
o
d
el
T
h
e
p
r
o
p
o
s
ed
m
o
d
el
p
e
r
f
o
r
m
an
ce
is
co
m
p
ar
e
d
with
th
e
p
r
ev
io
u
s
r
elate
d
wo
r
k
ca
r
r
i
ed
o
u
t
b
y
r
esear
ch
er
s
an
d
is
s
h
o
wn
in
T
ab
le
2
.
Pre
d
ictio
n
s
o
f
C
AD
d
i
s
ea
s
e
-
r
elate
d
wo
r
k
s
ca
r
r
ied
o
u
t
b
y
th
e
r
esear
c
h
er
s
wer
e
v
er
y
f
ew
in
n
u
m
b
er
,
an
d
ev
en
t
h
e
d
atasets
co
n
s
id
er
ed
wer
e
r
ep
o
s
ito
r
y
d
atasets
.
I
n
o
u
r
p
r
o
p
o
s
ed
h
y
b
r
id
m
o
d
el,
we
h
av
e
wo
r
k
e
d
b
y
c
r
ea
tin
g
o
u
r
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