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O
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Hea
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ev
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ca
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s
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d
ea
th
if
n
o
t
[
1
]
.
Hea
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d
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f
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ca
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s
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ld
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o
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m
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t
im
p
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ata
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is
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ata
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d
d
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ase
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[
2
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.
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[
3
]
-
15]
On
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[
1
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-
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2
5
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th
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q
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:
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i)
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iii
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W
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th
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2.
RE
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Nu
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s
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in
th
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s
ec
tio
n
.
Ga
v
h
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e
et
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l
.
[
4
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,
Naïv
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B
ay
es,
d
ec
is
io
n
tr
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an
d
r
a
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d
o
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f
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est alg
o
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ith
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s
ar
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r
t d
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ataset.
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h
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f
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m
a
n
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e
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o
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ith
m
s
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ataset
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d
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ith
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ay
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5
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,
Gau
s
s
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Na
ïv
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ay
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alg
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ith
m
is
ap
p
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Un
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ik
ar
[
6
]
,
a
co
m
p
ar
ativ
e
an
aly
s
is
o
n
th
e
p
r
ed
ictiv
e
p
er
f
o
r
m
a
n
ce
o
f
m
ac
h
i
n
e
lear
n
i
n
g
alg
o
r
ith
m
s
,
s
u
ch
as
Gau
s
s
ian
Naïv
e
B
ay
es,
L
o
g
is
tic
r
eg
r
ess
io
n
,
r
a
n
d
o
m
f
o
r
est
an
d
KNN
is
co
n
d
u
cted
h
ea
r
t
d
is
ea
s
e
d
ataset.
T
h
e
co
m
p
ar
is
o
n
r
esu
lt
s
h
o
ws
th
at
l
o
g
is
tic
r
eg
r
ess
io
n
o
u
tp
er
f
o
r
m
e
d
th
e
o
t
h
er
alg
o
r
ith
m
s
with
b
etter
ac
cu
r
ac
y
o
n
p
r
ed
ictio
n
.
Pawlo
v
s
k
y
[
7
]
,
h
ea
r
t
d
is
ea
s
e
p
r
ed
ictio
n
m
o
d
el
is
p
r
o
p
o
s
ed
b
y
em
p
l
o
y
in
g
co
n
v
o
lu
tio
n
al
n
eu
r
al
n
etwo
r
k
(
C
NN)
.
T
h
e
ac
cu
r
ac
y
o
f
th
e
p
r
o
p
o
s
ed
h
ea
r
t
d
is
ea
s
e
p
r
ed
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n
m
o
d
el
is
ev
alu
ated
o
n
test
d
ataset
an
d
th
e
an
aly
s
is
o
f
th
e
r
esu
lt
s
h
o
w
s
th
at
th
e
C
NN
alg
o
r
ith
m
ac
h
i
ev
ed
a
p
r
ed
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n
ac
c
u
r
ac
y
o
f
6
5
%.
Zu
n
a
id
i
et
a
l.
[
8
]
,
KNN
is
ap
p
lied
to
h
ea
r
t
d
is
ea
s
e
o
b
s
er
v
atio
n
s
co
llect
ed
f
r
o
m
W
is
co
n
s
in
.
T
h
e
au
t
h
o
r
s
co
m
p
ar
ed
t
h
e
p
er
f
o
r
m
an
ce
o
f
lin
ea
r
an
d
n
o
n
-
lin
ea
r
s
u
p
p
o
r
t
v
ec
to
r
m
ac
h
i
n
e.
T
h
e
r
esu
lt
o
f
p
er
f
o
r
m
an
ce
an
aly
s
is
s
h
o
ws
th
at
th
e
KNN
h
as p
r
ed
ictiv
e
ac
cu
r
ac
y
o
f
8
4
.
8
% o
n
th
e
h
ea
r
t d
is
e
ase
class
if
icat
io
n
p
r
o
b
lem
.
J
o
th
i
et
a
l.
[
9
]
,
a
co
m
p
ar
ativ
e
s
tu
d
y
o
n
m
ac
h
in
e
lear
n
in
g
alg
o
r
ith
m
s
n
am
ely
,
d
ec
is
io
n
tr
e
e,
r
an
d
o
m
f
o
r
est
an
d
m
u
lti
-
lay
er
p
er
ce
p
ti
o
n
is
co
n
d
u
cted
o
n
t
h
e
W
is
co
n
s
in
h
ea
r
t
d
is
ea
s
e
d
ata
r
e
p
o
s
ito
r
y
.
T
h
e
alg
o
r
ith
m
s
ar
e
ev
alu
ated
ag
ain
s
t
th
eir
ac
cu
r
ac
y
o
n
h
ea
r
t
d
is
ea
s
e
p
r
ed
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n
an
d
th
e
r
esu
lt
s
h
o
ws
th
at
m
u
lti
-
lay
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p
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ce
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tio
n
,
n
eu
r
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etwo
r
k
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b
etter
o
n
p
r
e
d
ictio
n
o
f
th
e
h
ea
r
t
d
is
ea
s
e.
J
ab
b
ar
et
a
l
.
[
1
0
]
,
s
u
p
p
o
r
t
v
ec
t
o
r
m
ac
h
in
e
is
ap
p
lied
to
th
e
h
ea
r
t
d
is
ea
s
e
d
ata
r
ep
o
s
ito
r
y
to
d
ev
elo
p
a
h
ea
r
t
d
is
ea
s
e
p
r
ed
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n
m
o
d
el.
T
h
e
au
t
h
o
r
s
ap
p
lied
f
ea
tu
r
e
s
elec
tio
n
to
im
p
r
o
v
e
th
e
p
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ed
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ctio
n
p
er
f
o
r
m
an
ce
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f
th
e
p
r
o
p
o
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ed
m
o
d
el
an
d
r
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lt
s
h
o
ws
th
at
th
e
m
o
d
el
h
as
ac
cu
r
ac
y
o
f
5
6
.
1
6
%.
Ass
eg
ie
et
a
l.
[
1
1
]
,
Naïv
e
B
ay
es
is
em
p
lo
y
ed
to
W
i
s
co
n
s
in
h
ea
r
t
d
is
ea
s
e
d
ata
r
ep
o
s
ito
r
y
to
p
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a
h
ea
r
t
d
is
ea
s
e.
T
h
e
m
a
x
im
u
m
p
r
ed
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n
ac
c
u
r
ac
y
ac
h
ie
v
ed
b
y
u
s
in
g
th
is
m
o
d
el
is
8
7
%.
A
p
r
ed
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n
ac
cu
r
ac
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o
f
8
7
%
is
ac
ce
p
ta
b
le
in
m
ac
h
in
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lear
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in
g
a
n
d
p
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s
y
s
tem
an
d
h
en
ce
,
Naïv
e
B
ay
es
m
o
d
el
is
b
etter
in
p
er
f
o
r
m
an
ce
an
d
p
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o
f
h
ea
r
t d
is
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s
e
.
3.
RE
S
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ARCH
M
E
T
H
O
D
I
n
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r
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r
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d
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r
ch
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th
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3
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p
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o
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am
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A
s
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tical
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eth
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r
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aliza
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Fig
u
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R
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11
ca
N
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12
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targ
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Fig
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2
.
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d
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class
3
.
2
.
F
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a
t
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rr
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t
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T
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h
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r
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F
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ated
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3
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s
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as
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e
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.
2
7
.
Similar
ity
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ig
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ly
co
r
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elate
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ef
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2
2
.
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n
a
d
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itio
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,
n
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m
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er
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f
m
ajo
r
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h
as
h
ig
h
co
r
r
elatio
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with
ag
e
with
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ef
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icien
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o
f
0
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2
5
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p
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ax
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m
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ea
r
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r
ate
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ie
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o
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0
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4
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n
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n
t
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n
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cise
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d
u
ce
d
an
g
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h
as n
eg
ativ
e
co
r
r
elatio
n
v
alu
e
with
ag
e
f
ea
tu
r
e.
Evaluation Warning : The document was created with Spire.PDF for Python.
I
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2
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4.
RE
SU
L
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AND
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SCUS
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tio
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ith
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ates
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4
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a
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ter
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et
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ith
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ith
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s
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wn
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Fig
u
r
e
5.
Evaluation Warning : The document was created with Spire.PDF for Python.
I
n
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f
&
C
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m
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N:
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6
Hea
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o
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ated
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ates
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=
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n
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u
r
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5
.
C
o
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f
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s
io
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m
atr
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x
o
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KNN
m
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el
Fig
u
r
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6
.
K
v
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s
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d
el
5.
CO
NCLU
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O
N
I
n
th
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ased
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el
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h
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t
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e
p
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ed
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ataset
o
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tain
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r
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Kag
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le
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ata
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ito
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.
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m
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el
s
o
lv
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iased
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icatio
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ith
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et.
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aly
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o
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ith
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.
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ith
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lts
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n
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m
ajo
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ity
class
y
ield
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et
ter
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er
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o
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m
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ce
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n
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ed
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o
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ajo
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s
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ity
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.
ACK
NO
WL
E
DG
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M
E
NT
S
T
h
e
au
th
o
r
wo
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l
d
lik
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to
th
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k
I
n
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ar
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Un
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ity
f
o
r
p
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v
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g
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in
ter
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et
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lab
o
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f
ac
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ates
f
o
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co
n
d
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ctin
g
th
is
wo
r
k
.
RE
F
E
R
E
NC
E
S
[1
]
A.
M
.
Yu
so
f,
N
.
A.
M
.
G
h
a
n
i,
K.
A.
M
.
G
h
a
n
i,
a
n
d
K.
I.
M
.
G
h
a
n
i,
“
A
p
re
d
ictiv
e
m
o
d
e
l
f
o
r
p
re
d
ict
io
n
o
f
h
e
a
rt
su
r
g
e
ry
p
ro
c
e
d
u
re
,
”
I
n
d
o
n
e
sia
n
J
o
u
rn
a
l
o
f
El
e
c
trica
l
En
g
in
e
e
rin
g
a
n
d
Co
m
p
u
ter
S
c
ien
c
e
,
v
o
l
.
1
5
,
n
o
.
3
,
p
p
.
1
6
1
5
–
1
6
2
0
,
S
e
p
.
2
0
1
9
,
d
o
i:
1
0
.
1
1
5
9
1
/i
jee
c
s.v
1
5
.
i
3
.
p
p
1
6
1
5
-
1
6
2
0
.
[2
]
T.
S
u
re
sh
,
T
.
A.
As
se
g
ie,
S
.
Ra
jk
u
m
a
r,
a
n
d
N.
K.
Ku
m
a
r,
“
A h
y
b
ri
d
a
p
p
ro
a
c
h
to
m
e
d
ica
l
d
e
c
isio
n
-
m
a
k
in
g
:
d
iag
n
o
sis
o
f
h
e
a
rt
d
ise
a
se
with
m
a
c
h
in
e
-
l
e
a
rn
in
g
m
o
d
e
l,
”
In
ter
n
a
ti
o
n
a
l
J
o
u
rn
a
l
o
f
El
e
c
trica
l
a
n
d
Co
m
p
u
ter
En
g
i
n
e
e
rin
g
(IJ
ECE
)
,
v
o
l.
1
2
,
n
o
.
2
,
p
p
.
1
8
3
1
–
1
8
3
8
,
A
p
r.
2
0
2
2
,
d
o
i:
1
0
.
1
1
5
9
1
/i
j
e
c
e
.
v
1
2
i
2
.
p
p
1
8
3
1
-
1
8
3
8
.
[3
]
A.
A.
Hu
ss
e
in
,
“
Im
p
ro
v
e
th
e
p
e
r
fo
rm
a
n
c
e
o
f
K
-
m
e
a
n
s
b
y
u
si
n
g
g
e
n
e
ti
c
a
lg
o
r
it
h
m
fo
r
c
las
sifica
ti
o
n
h
e
a
rt
a
tt
a
c
k
,
”
In
ter
n
a
t
io
n
a
l
J
o
u
rn
a
l
o
f
El
e
c
trica
l
a
n
d
C
o
mp
u
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.
[2
0
]
T.
A.
As
se
g
ie,
S
.
J.
S
u
s
h
m
a
,
B.
G
.
Bh
a
v
y
a
,
a
n
d
S
.
P
a
d
m
a
sh
re
e
,
“
Co
rre
latio
n
An
a
ly
sis
fo
r
De
term
in
i
n
g
Eff
e
c
ti
v
e
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ta
in
M
a
c
h
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e
Lea
rn
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g
:
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tec
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rt
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re
,
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mp
u
ter
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.
3
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A
p
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2
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5.
[2
1
]
T.
R.
S
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M
a
ry
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.
S
e
b
a
stia
n
,
“
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re
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rt
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lme
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t
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P
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ti
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n
ts
wit
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in
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n
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m
b
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f
F
e
a
t
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re
s
u
si
n
g
Da
ta
M
in
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n
g
Tec
h
n
i
q
u
e
s,”
In
ter
n
a
ti
o
n
a
l
J
o
u
rn
a
l
o
f
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e
c
trica
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n
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m
p
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n
g
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(IJ
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)
,
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[2
2
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T.
A.
As
se
g
ie,
“
An
o
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m
ize
d
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re
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b
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b
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d
b
re
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tec
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n
,
”
J
o
u
rn
a
l
o
f
Ro
b
o
t
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a
n
d
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o
n
tr
o
l
(J
RC)
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1
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o
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1
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2
3
6
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.
[2
3
]
Y.
K.
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in
g
h
,
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S
in
h
a
,
a
n
d
S
.
K.
S
in
g
h
,
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He
a
rt
Dise
a
se
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re
d
ictio
n
S
y
ste
m
Us
in
g
Ra
n
d
o
m
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o
re
st
,
”
in
Co
mm
u
n
ica
ti
o
n
s
in
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m
p
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ter
a
n
d
In
fo
rm
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t
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c
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n
c
e
,
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p
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r
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g
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p
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re
,
2
0
1
7
,
p
p
.
6
1
3
–
6
2
3
.
[2
4
]
M
.
P
a
l
a
n
d
S
.
P
a
rij
a
,
“
P
re
d
icti
o
n
o
f
He
a
rt
Dise
a
s
e
s
u
sin
g
Ra
n
d
o
m
F
o
re
st,”
J
o
u
rn
a
l
o
f
P
h
y
sic
s:
Co
n
fer
e
n
c
e
S
e
rie
s
,
v
o
l.
1
8
1
7
,
n
o
.
1
,
p
.
1
2
0
0
9
,
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0
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o
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7
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1
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0
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.
[2
5
]
L.
Ali
e
t
a
l.
,
“
A
F
e
a
tu
re
-
Driv
e
n
De
c
isio
n
S
u
p
p
o
rt
S
y
ste
m
fo
r
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a
rt
F
a
il
u
re
P
re
d
ict
io
n
Ba
se
d
o
n
χ
2
S
tatisti
c
a
l
M
o
d
e
l
a
n
d
G
a
u
ss
ian
Na
iv
e
Ba
y
e
s
,
”
Co
m
p
u
t
a
ti
o
n
a
l
a
n
d
M
a
th
e
ma
ti
c
a
l
M
e
t
h
o
d
s
in
M
e
d
ici
n
e
,
v
o
l.
2
0
1
9
,
p
p
.
1
–
8
,
No
v
.
2
0
1
9
,
d
o
i:
1
0
.
1
1
5
5
/2
0
1
9
/
6
3
1
4
3
2
8
.
B
I
O
G
RAP
H
Y
O
F
AUTHO
R
Tse
h
a
y
Ad
m
a
ss
u
As
se
g
ie
re
c
e
iv
e
d
h
is
f
irst
d
e
g
re
e
in
Co
m
p
u
ter
S
c
ien
c
e
fr
o
m
Dill
a
Un
iv
e
rsity
,
Et
h
i
o
p
ia,
in
2
0
1
3
.
He
h
a
s
a
lso
M
a
ste
r
d
e
g
re
e
in
Co
m
p
u
ter
S
c
ien
c
e
fro
m
An
d
h
r
a
Un
iv
e
rsity
,
In
d
ia,
in
2
0
1
6
.
He
is
c
u
rre
n
tl
y
Co
m
p
u
ter
S
c
ien
c
e
re
se
a
r
c
h
e
r
wo
rk
in
g
a
s
Lec
tu
re
d
in
De
p
a
rtme
n
t
o
f
C
o
m
p
u
ter
S
c
ien
c
e
,
In
ji
b
a
ra
Un
i
v
e
rsity
,
Et
h
i
o
p
ia.
His
m
a
in
re
se
a
rc
h
in
tere
sts
fo
c
u
s
o
n
M
a
c
h
in
e
lea
rn
in
g
,
M
e
d
ica
l
d
a
ta
An
a
ly
sis
a
n
d
Da
ta
M
in
i
n
g
.
He
c
a
n
b
e
c
o
n
tac
ted
a
t
e
m
a
il
:
tse
h
a
y
a
d
m
a
ss
u
2
0
0
6
@
g
m
a
il
.
c
o
m
.
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