I
n
t
e
r
n
at
ion
al
Jou
r
n
a
l
of
I
n
f
o
r
m
at
ics
an
d
Com
m
u
n
icat
ion
T
e
c
h
n
ol
ogy
(
I
J
-
I
CT
)
Vo
l
.
1
4
,
N
o
.
1
,
A
pr
i
l
20
2
5
,
pp.
1
1
~
19
I
S
S
N:
2252
-
8776
,
DO
I
:
10
.
11591/i
ji
c
t
.
v
1
4
i
1
.
pp
11
-
19
11
Jou
r
n
al
h
o
m
e
page
:
ht
tp:
//
ij
ict
.
iaes
c
or
e
.
c
om
E
n
h
a
n
c
in
g
p
r
e
d
ic
t
iv
e
m
od
e
ll
i
n
g an
d
in
t
e
r
p
r
e
t
ab
il
ity
i
n
h
e
ar
t
f
ai
lu
r
e
p
r
e
d
ic
t
io
n
:
a
S
HAP
-
b
ase
d
an
al
y
si
s
Niaz
As
h
r
a
f
K
h
an
1
,
Md
.
F
e
r
d
ou
s
B
in
Haf
iz
2
,
M
d
.
Ak
t
ar
u
z
z
am
an
P
r
a
m
an
i
k
1
1
D
e
pa
r
tm
e
nt
of
C
o
mpu
te
r
S
c
ie
n
c
e
a
nd E
ngi
n
e
e
r
in
g,
U
ni
ve
r
s
it
y
of
L
ib
e
r
a
l
A
r
ts
B
a
ngl
a
d
e
s
h, D
ha
ka
, B
a
ngl
a
de
s
h
2
D
e
pa
r
tm
e
nt
of
C
o
mpu
te
r
S
c
ie
n
c
e
a
nd E
ngi
n
e
e
r
in
g,
S
o
u
th
e
a
s
t
U
ni
ve
r
s
it
y
, D
ha
ka
, B
a
ngl
a
de
s
h
Ar
t
ic
l
e
I
n
f
o
AB
S
T
RA
CT
A
r
ti
c
le
h
is
tor
y
:
R
e
c
e
i
ve
d
M
a
r
24,
2024
R
e
vi
s
e
d
A
ug
14,
2024
A
c
c
e
pt
e
d
S
e
p
22
,
2024
Pre
d
i
c
t
i
v
e
m
o
d
el
l
i
n
g
p
l
a
y
s
a
c
r
u
c
i
a
l
ro
l
e
i
n
h
e
a
l
t
h
c
ar
e
,
p
art
i
c
u
l
arl
y
i
n
fo
rec
as
t
i
n
g
mo
rt
al
i
t
y
d
u
e
t
o
h
e
art
fai
l
u
r
e
.
T
h
i
s
s
t
u
d
y
f
o
cu
s
e
s
o
n
e
n
h
an
ci
n
g
p
re
d
i
c
t
i
v
e
mo
d
el
l
i
n
g
an
d
i
n
t
e
rp
re
t
ab
i
l
i
t
y
i
n
h
e
art
fa
i
l
u
r
e
p
r
e
d
i
c
t
i
o
n
t
h
ro
u
g
h
ad
v
an
ced
b
o
o
s
t
i
n
g
al
g
o
r
i
t
h
m
s
,
e
n
s
em
b
l
e
me
t
h
o
d
s
,
a
n
d
SH
ap
l
ey
A
d
d
i
t
i
v
e
e
x
Pl
an
at
i
o
n
s
(
SH
A
P
)
a
n
al
y
s
i
s
.
L
ev
e
rag
i
n
g
a
d
at
as
e
t
o
f
p
at
i
en
t
s
d
i
a
g
n
o
s
ed
w
i
t
h
c
ard
i
o
v
as
cu
l
ar
d
i
s
e
as
e
s
(CV
D
)
,
w
e
em
p
l
o
y
ed
t
e
ch
n
i
q
u
e
s
s
u
ch
as
s
y
n
t
h
e
t
i
c
m
i
n
o
r
i
t
y
o
v
e
r
-
sa
m
p
l
i
n
g
t
ec
h
n
i
q
u
e
(SMO
T
E
)
an
d
b
o
o
t
s
t
rap
p
i
n
g
t
o
ad
d
r
e
s
s
c
l
as
s
i
m
b
al
an
ce
.
O
u
r
r
e
s
u
l
t
s
d
emo
n
s
t
rat
e
d
e
x
ce
p
t
i
o
n
a
l
p
red
i
c
t
i
v
e
p
e
rfo
r
m
an
ce,
w
i
t
h
t
h
e
g
rad
i
e
n
t
b
o
o
s
t
i
n
g
(
GB
o
o
s
t
)
mo
d
e
l
a
c
h
i
e
v
i
n
g
t
h
e
h
i
g
h
e
s
t
a
cc
u
ra
cy
o
f
9
1
.
3
9
%
.
E
n
s
em
b
l
e
t
ec
h
n
i
q
u
es
fu
rt
h
e
r
en
h
a
n
ce
d
p
e
r
fo
r
m
an
ce,
w
i
t
h
t
h
e
v
o
t
i
n
g
cl
as
s
i
fi
e
r
(V
C)
,
s
t
ack
i
n
g
c
l
as
s
i
fi
e
r
(SC)
,
a
n
d
Bl
en
d
i
n
g
a
ch
i
ev
i
n
g
accu
ra
c
i
e
s
o
f
9
1
.
0
0
%
.
SH
A
P
an
a
l
y
s
i
s
u
n
c
o
v
e
r
e
d
k
ey
fe
at
u
r
e
s
s
u
c
h
as
t
i
me
,
S
e
r
u
m
_
c
r
e
at
i
n
i
n
e
,
a
n
d
E
j
e
c
t
i
o
n
_
f
rac
t
i
o
n
,
s
i
g
n
i
f
i
c
an
t
l
y
i
m
p
ac
t
i
n
g
mo
rt
al
i
t
y
p
red
i
c
t
i
o
n
.
T
h
e
s
e
f
i
n
d
i
n
g
s
h
i
g
h
l
i
g
h
t
t
h
e
i
m
p
o
rt
an
ce
o
f
t
ran
s
p
are
n
t
an
d
i
n
t
e
rp
r
e
t
ab
l
e
m
a
ch
i
n
e
l
e
arn
i
n
g
mo
d
e
l
s
i
n
h
e
a
l
t
h
c
ar
e
d
eci
s
i
o
n
-
m
ak
i
n
g
p
ro
ce
s
s
e
s
,
fa
c
i
l
i
t
at
i
n
g
i
n
f
o
r
me
d
i
n
t
e
rv
en
t
i
o
n
s
an
d
p
e
rs
o
n
al
i
z
e
d
t
re
at
me
n
t
s
t
rat
e
g
i
e
s
fo
r
h
e
art
fai
l
u
r
e
p
at
i
en
t
s
.
K
e
y
w
o
r
d
s
:
C
VD
E
n
s
e
m
b
l
e
m
o
de
l
s
He
a
r
t
f
a
i
l
ur
e
I
n
t
e
r
pr
e
t
a
bi
li
t
y
S
HA
P
a
n
a
ly
s
i
s
Th
i
s
i
s
a
n
o
p
en
a
c
ces
s
a
r
t
i
c
l
e
u
n
d
e
r
t
h
e
CC
B
Y
-
SA
l
i
cen
s
e.
C
or
r
e
s
pon
din
g
A
u
th
or
:
N
i
a
z
A
s
h
r
a
f
Kha
n
De
pa
r
t
m
e
n
t
o
f
C
o
m
put
e
r
S
c
i
e
n
c
e
a
n
d
E
n
g
i
ne
e
r
i
n
g
,
Uni
v
e
r
s
i
t
y
o
f
L
i
be
r
a
l
A
r
t
s
B
a
n
g
l
a
de
s
h
Dh
a
ka
,
B
a
n
g
l
a
de
s
h
E
m
a
i
l
:
ni
a
z
.
a
s
h
r
a
f
@u
l
a
b
.
e
du.
b
d
1.
I
NT
RODU
C
T
I
ON
C
a
r
d
i
o
v
a
s
c
u
l
a
r
d
i
s
e
a
s
e
(
C
VD
)
s
t
a
n
ds
a
s
t
h
e
f
o
r
e
m
o
s
t
c
a
us
e
o
f
m
o
r
t
a
l
i
t
y
g
l
o
b
a
ll
y
,
pr
e
s
e
n
t
i
n
g
a
s
i
g
nif
i
c
a
n
t
c
h
a
l
l
e
n
g
e
i
n
publ
i
c
h
e
a
l
t
h
wo
r
l
dw
i
d
e
[
1]
.
I
n
t
h
e
r
e
a
l
m
o
f
c
a
r
d
i
o
va
s
c
u
l
a
r
r
e
s
e
a
r
c
h
,
m
a
c
hi
ne
l
e
a
r
ni
ng
a
l
go
r
i
t
hm
s
ha
v
e
e
m
e
r
ge
d
a
s
va
l
ua
bl
e
too
l
s
f
o
r
p
r
e
di
c
t
i
o
n
,
o
f
f
e
r
i
n
g
pr
o
m
i
s
i
ng
a
v
e
nue
s
f
o
r
un
de
r
s
t
a
n
d
i
ng
a
n
d
a
ddr
e
s
s
i
ng
C
VD
-
r
e
l
a
t
e
d
i
s
s
u
e
s
.
W
i
t
h
pr
o
j
e
c
t
i
o
ns
i
n
d
i
c
a
t
i
n
g
t
h
a
t
C
VD
w
i
ll
a
c
c
o
un
t
f
o
r
a
ppr
o
xi
m
a
t
e
l
y
23
m
il
li
o
n
de
a
t
h
s
by
2030
[
2]
,
t
h
e
ur
ge
n
c
y
to
de
v
e
l
o
p
e
f
f
e
c
t
i
v
e
pr
e
d
i
c
t
i
ve
m
o
de
l
s
h
a
s
i
n
t
e
n
s
if
i
e
d.
C
o
n
s
e
que
n
t
l
y
,
t
h
e
i
n
t
e
gr
a
t
i
o
n
o
f
a
r
ti
f
i
c
i
a
l
i
n
t
e
ll
i
g
e
n
c
e
(
A
I
)
a
n
d
e
x
t
e
ns
i
ve
da
t
a
s
e
t
s
i
n
C
VD
pr
e
d
i
c
t
i
o
n
m
o
de
l
s
i
s
i
n
c
r
e
a
s
i
ng
ly
p
r
e
v
a
l
e
n
t
[
3
]
,
[
4]
.
T
o
i
m
pr
o
v
e
m
o
r
t
a
l
i
t
y
f
o
r
e
c
a
s
t
i
n
g
i
n
C
V
D
c
a
s
e
s
,
pr
e
d
i
c
t
i
v
e
m
o
de
l
i
ng
i
s
c
r
uc
i
a
l
i
n
h
e
a
l
t
h
c
a
r
e
.
Ho
w
e
v
e
r
,
f
o
r
t
h
e
s
e
m
o
de
l
s
to
b
e
e
f
f
e
c
t
i
v
e
,
t
h
e
y
m
us
t
a
l
s
o
b
e
i
n
t
e
r
pr
e
t
a
bl
e
a
n
d
t
r
a
n
s
pa
r
e
n
t
,
f
o
s
t
e
r
i
n
g
t
r
us
t
a
m
o
ng
h
e
a
l
t
h
c
a
r
e
pr
o
f
e
s
s
i
o
n
a
l
s
a
n
d
p
a
t
i
e
n
t
s
.
T
hi
s
s
t
ud
y
a
i
m
s
t
o
e
nh
a
nc
e
h
e
a
r
t
f
a
il
ur
e
pr
e
d
i
c
t
i
o
n
by
e
m
p
l
o
yi
ng
a
dv
a
n
c
e
d
b
o
o
s
t
i
n
g
a
l
go
r
i
t
hm
s
,
e
n
s
e
m
b
l
e
m
e
t
ho
ds
,
a
n
d
S
Ha
p
l
e
y
Add
i
t
i
v
e
e
x
P
l
a
n
a
t
i
o
n
s
(
S
HA
P
)
a
n
a
ly
s
i
s
f
o
r
b
e
tt
e
r
i
n
t
e
r
pr
e
t
a
bi
li
t
y
.
T
e
c
hni
que
s
l
i
k
e
XG
B
o
o
s
t
(
XG
B
)
a
n
d
gr
a
d
i
e
n
t
b
oo
s
t
i
n
g
(
GB
o
o
s
t
)
,
a
l
o
n
g
w
i
t
h
e
n
s
e
m
b
l
e
m
e
t
h
o
ds
s
uc
h
a
s
v
o
t
i
n
g
a
n
d
bl
e
n
d
i
ng
,
a
r
e
ut
i
li
z
e
d
to
e
nh
a
nc
e
pr
e
d
i
c
t
i
v
e
a
c
c
ur
a
c
y
a
n
d
ga
i
n
de
e
pe
r
i
n
s
i
g
h
t
s
i
n
t
o
t
h
e
f
a
c
t
or
s
i
nf
l
ue
n
c
i
ng
he
a
r
t
f
a
il
ur
e
m
o
r
t
a
l
i
t
y
.
P
r
e
vi
o
us
s
t
ud
i
e
s
h
a
v
e
e
x
t
e
ns
i
ve
ly
a
pp
li
e
d
m
a
c
hin
e
l
e
a
r
ni
ng
t
e
c
hni
que
s
to
pr
e
di
c
t
s
ur
vi
va
l
i
n
h
e
a
r
t
f
a
il
ur
e
pa
t
i
e
n
t
s
,
f
o
c
us
i
n
g
o
n
i
d
e
n
t
i
f
yi
ng
c
r
i
t
i
c
a
l
r
i
s
k
f
a
c
t
or
s
.
T
h
e
h
e
a
l
t
h
c
a
r
e
s
e
c
t
o
r
h
a
s
s
e
e
n
n
o
t
a
bl
e
a
dv
a
n
c
e
m
e
n
t
s
in
Evaluation Warning : The document was created with Spire.PDF for Python.
I
S
S
N
:
2252
-
8776
I
n
t
J
I
n
f
&
C
o
m
m
u
n
T
e
c
hn
o
l
,
Vo
l
.
1
4
,
N
o.
1
,
A
pr
i
l
20
2
5
:
11
-
19
12
m
a
c
hi
ne
l
e
a
r
ni
ng
w
i
t
hi
n
c
a
r
d
i
o
l
o
g
y
,
u
n
de
r
s
c
o
r
i
n
g
t
h
e
i
nc
r
e
a
s
i
n
g
a
do
pt
i
o
n
a
n
d
e
f
f
e
c
t
i
v
e
ne
s
s
o
f
t
h
e
s
e
m
e
t
h
o
d
s
.
Al
o
t
a
i
bi
[
5]
,
m
a
c
hi
ne
l
e
a
r
ni
ng
t
e
c
hni
que
s
f
o
r
h
e
a
r
t
f
a
i
l
ur
e
pr
e
d
i
c
t
i
o
n
us
i
ng
da
t
a
f
r
o
m
t
h
e
c
l
e
v
e
l
a
n
d
c
l
i
n
i
c
f
o
un
d
a
t
i
o
n
we
r
e
e
x
p
l
o
r
e
d.
T
h
e
de
c
i
s
i
o
n
tr
e
e
a
l
go
r
i
t
hm
a
c
hi
e
v
e
d
t
h
e
hi
g
h
e
s
t
a
c
c
ur
a
c
y
a
t
93.
19%
,
f
o
l
l
o
we
d
by
s
uppo
r
t
v
e
c
to
r
m
a
c
hi
ne
(
S
VM
)
a
t
92.
30%
.
W
e
n
g
e
t
al.
[
6]
c
o
n
duc
t
e
d
t
e
s
t
s
o
n
f
o
ur
m
o
de
l
s
us
i
ng
c
l
i
n
i
c
a
l
da
t
a
f
r
o
m
o
v
e
r
300,
000
h
o
m
e
s
i
n
t
h
e
U
K
,
w
i
t
h
NN
de
m
o
ns
t
r
a
t
i
n
g
t
h
e
m
o
s
t
a
c
c
ur
a
t
e
p
r
e
di
c
t
i
o
n
s
f
o
r
C
VD
o
n
a
l
a
r
ge
r
da
t
a
s
e
t
.
Di
m
o
po
u
l
o
s
e
t
al.
[
7]
e
v
a
l
ua
t
e
d
k
-
n
e
a
r
e
s
t
n
e
i
g
hb
o
r
(
K
NN
)
,
r
a
n
do
m
f
o
r
e
s
t
(
RF
)
,
a
n
d
de
c
i
s
i
o
n
t
r
e
e
(
DT
)
m
o
de
l
s
us
i
ng
t
h
e
A
T
T
I
C
A
da
t
a
s
e
t,
s
h
o
wi
n
g
R
F
’
s
s
upe
r
i
o
r
i
t
y
i
n
c
o
m
bi
na
t
i
o
n
w
i
t
h
t
h
e
He
l
l
e
ni
c
S
C
OR
E
too
l
.
M
o
h
a
n
e
t
al.
[
8]
pr
o
p
o
s
e
d
a
hy
br
i
d
HR
F
L
M
s
t
r
a
t
e
gy
to
i
m
pr
o
v
e
pr
e
d
i
c
t
i
o
n
a
c
c
ur
a
c
y
i
n
I
oT
a
ppl
i
c
a
t
i
o
n
s
us
i
ng
m
a
c
hi
ne
l
e
a
r
ni
ng.
Ya
n
g
e
t
a
l.
[
9
]
e
m
p
l
o
y
e
d
l
o
g
i
s
t
i
c
r
e
gr
e
s
s
i
o
n
(
LR
)
to
a
s
s
e
s
s
c
a
r
d
i
o
v
a
s
c
u
l
a
r
r
i
s
k
f
a
c
to
r
s
i
n
e
a
s
t
e
r
n
C
hi
na
.
M
a
m
u
n
a
n
d
E
l
f
o
u
l
y
[
10]
pr
o
p
o
s
e
a
hy
b
r
i
d
1D
C
NN
us
i
ng
a
l
a
r
ge
o
nl
i
ne
da
t
a
s
e
t
a
n
d
f
e
a
t
ur
e
s
e
l
e
c
t
i
o
n
a
l
go
r
i
t
hm
s
,
a
c
hi
e
vi
ng
80.
1%
a
c
c
ur
a
c
y
f
o
r
n
o
n
-
c
o
r
o
n
a
r
y
h
e
a
r
t
di
s
e
a
s
e
(
n
o
-
C
HD
)
a
n
d
76.
9%
f
o
r
C
HD
.
M
uni
a
s
a
my
e
t
al.
[
11]
i
n
t
r
o
duc
e
s
a
de
e
p
c
o
n
v
o
l
ut
i
o
n
a
l
n
e
ur
a
l
n
e
t
wo
r
ks
(
C
NN
)
m
o
de
l
f
o
r
C
HD
c
l
a
s
s
i
f
i
c
a
t
i
o
n
w
i
t
h
f
e
a
t
ur
e
s
e
l
e
c
t
i
o
n
vi
a
L
A
S
S
O,
a
c
hi
e
vi
ng
99.
36%
a
c
c
ur
a
c
y
.
A
r
a
b
a
s
a
d
i
e
t
al.
[
12]
c
o
m
bi
ne
s
ge
n
e
t
i
c
a
l
go
r
i
t
hms
a
n
d
NN
s
t
o
e
n
h
a
n
c
e
d
i
a
g
n
o
s
t
i
c
a
c
c
ur
a
c
y
.
R
e
va
t
hi
e
t
al.
[
13]
p
r
e
s
e
n
t
s
t
h
e
OC
I
-
L
S
T
M
m
o
de
l
,
us
i
n
g
t
h
e
s
a
l
p
s
wa
r
m
a
l
go
r
i
t
hm
a
n
d
ge
n
e
t
i
c
a
l
go
r
i
t
hm
f
o
r
e
a
r
ly
d
i
a
g
n
o
s
i
s
o
f
C
VD
.
Ho
s
s
e
n
e
t
al.
[
14]
a
pp
l
i
e
s
s
up
e
r
vi
s
e
d
l
e
a
r
ni
ng,
pa
r
t
i
c
u
l
a
r
ly
L
R
,
to
pr
e
di
c
t
h
e
a
r
t
d
i
s
e
a
s
e
us
i
n
g
t
h
e
UC
I
C
l
e
v
e
l
a
n
d
da
t
a
b
a
s
e
.
B
h
a
r
t
i
e
t
al.
[
1
5]
e
x
p
l
o
r
e
s
m
a
c
hi
ne
l
e
a
r
ni
ng
a
n
d
de
e
p
l
e
a
r
ni
ng
a
lgo
r
i
t
hm
s
,
a
c
hi
e
vi
ng
hi
g
h
a
c
c
ur
a
c
y
a
n
d
i
n
t
e
gr
a
t
i
n
g
t
h
e
s
e
m
e
t
h
o
ds
wi
t
h
m
u
l
t
i
m
e
d
i
a
t
e
c
hn
o
l
o
g
y
.
Al
qa
h
t
a
ni
e
t
al.
[
16]
e
m
p
h
a
s
i
z
e
s
t
h
e
i
m
po
r
t
a
n
c
e
o
f
e
a
r
l
y
d
e
t
e
c
t
i
o
n
o
f
C
VD
s
y
m
pt
o
m
s
a
n
d
t
i
m
e
ly
i
n
t
e
r
v
e
n
t
i
o
n
.
P
h
a
s
i
na
m
e
t
al.
[
17]
i
n
t
r
o
duc
e
s
a
m
a
c
hi
ne
l
e
a
r
ni
ng
f
r
a
m
e
wo
r
k
f
o
r
pr
e
di
c
t
i
n
g
h
e
a
r
t
di
s
e
a
s
e
l
i
k
e
l
i
h
o
o
d,
wi
t
h
R
F
s
h
o
w
i
n
g
t
h
e
hi
g
h
e
s
t
a
c
c
ur
a
c
y
.
D
h
a
r
m
e
n
dr
a
a
n
d
S
a
r
a
v
a
na
n
[
18]
i
n
t
r
o
duc
e
s
a
c
a
t
e
g
o
r
i
z
a
t
i
o
n
a
l
go
r
i
t
hm
f
o
r
h
e
a
r
t
a
tt
a
c
k
pr
e
d
i
c
t
i
o
n
,
f
i
nd
i
ng
S
VM
s
upe
r
i
o
r
to
D
T
.
K
u
m
a
r
a
n
d
R
e
kh
a
[
19]
p
r
o
p
o
s
e
s
a
m
e
t
h
o
d
us
i
n
g
de
e
p
n
e
ur
a
l
n
e
t
wo
r
ks
f
o
r
C
VD
r
i
s
k
pr
e
d
i
c
t
i
o
n
o
n
we
l
l
-
d
e
f
i
ne
d
da
t
a
s
e
t
s
.
E
l
mi
na
a
m
e
t
al.
[
20]
hi
g
hli
g
h
t
s
t
h
e
e
f
f
e
c
t
i
v
e
n
e
s
s
o
f
m
a
c
hi
ne
l
e
a
r
ni
ng
,
pa
r
t
i
c
u
l
a
r
ly
LR
a
n
d
RF
,
i
n
i
m
pr
o
vi
ng
h
e
a
r
t
di
s
e
a
s
e
pr
e
d
i
c
t
i
o
n
a
c
c
ur
a
c
y
.
El
-
Ha
s
n
o
ny
e
t
al.
[
21
]
a
dv
o
c
a
t
e
f
o
r
M
L
-
b
a
s
e
d
s
y
s
t
e
m
s
i
n
he
a
r
t
di
s
e
a
s
e
pr
e
d
i
c
t
i
o
n
a
n
d
d
i
a
g
n
o
s
i
s
,
e
m
p
h
a
s
i
z
i
ng
a
c
t
i
v
e
l
e
a
r
ni
ng
w
i
t
h
us
e
r
-
e
x
pe
r
t
f
e
e
d
ba
c
k.
Gul
e
r
i
a
e
t
al.
[
22
]
e
x
p
l
o
r
e
s
e
n
s
e
m
bl
e
m
o
de
l
s
i
n
t
h
e
e
xp
l
a
i
na
bl
e
a
r
t
i
f
i
c
i
a
l
i
n
t
e
ll
i
g
e
n
c
e
(
XA
I
)
a
ppr
o
a
c
h
f
r
o
m
t
h
e
C
V
D
da
t
a
s
e
t
s
,
e
m
p
l
o
yi
ng
m
u
l
t
i
p
l
e
m
a
c
hi
ne
l
e
a
r
ni
ng
m
o
de
l
s
.
I
n
c
o
n
t
r
a
s
t
to
p
r
i
o
r
s
t
udi
e
s
,
o
u
r
r
e
s
e
a
r
c
h
a
ddr
e
s
s
e
s
t
h
e
n
e
e
d
f
o
r
e
n
ha
n
c
e
d
pr
e
d
i
c
t
i
v
e
m
o
de
l
i
ng
a
n
d
i
n
t
e
r
pr
e
t
a
bi
li
t
y
i
n
h
e
a
r
t
f
a
il
ur
e
pr
e
d
i
c
t
i
o
n
.
a
n
d
c
o
n
t
r
i
b
ut
e
s
to
t
h
e
f
i
e
l
d
o
f
h
e
a
r
t
f
a
il
ur
e
pr
e
d
i
c
t
i
o
n
by
:
I
n
t
e
gr
a
t
i
o
n
o
f
a
d
v
a
n
c
e
d
b
o
o
s
t
i
n
g
a
l
go
r
i
t
hm
s
,
e
n
s
e
m
bl
e
m
e
t
h
o
ds
,
a
n
d
S
HA
P
a
n
a
ly
s
i
s
f
o
r
h
e
a
r
t
f
a
il
ur
e
pr
e
d
i
c
t
i
o
n
.
Ut
i
li
z
a
t
i
o
n
o
f
S
H
A
P
a
n
a
ly
s
i
s
t
o
p
r
o
vi
d
e
i
n
s
i
g
h
t
s
i
nto
m
o
r
t
a
l
i
t
y
pr
e
d
i
c
t
i
o
n
f
a
c
t
o
r
s
.
C
o
m
pr
e
h
e
ns
i
ve
a
ppr
o
a
c
h
c
o
m
bi
ni
ng
g
l
o
b
a
l
a
n
d
l
o
c
a
l
S
H
A
P
a
n
a
ly
s
i
s
f
o
r
a
h
o
l
i
s
t
i
c
u
n
de
r
s
t
a
n
d
in
g
o
f
m
o
de
l
pr
e
d
i
c
t
i
o
ns
.
A
c
hi
e
ve
m
e
n
t
o
f
s
upe
r
i
o
r
pr
e
di
c
t
i
v
e
pe
r
f
o
r
m
a
n
c
e
c
o
m
pa
r
e
d
to
p
r
e
vi
o
us
s
t
ud
i
e
s
.
T
h
e
t
e
c
hni
c
a
l
c
o
n
t
r
i
b
ut
i
o
ns
o
f
o
ur
s
t
udy
a
r
e
o
u
t
l
i
ne
d
i
n
de
t
a
i
l
i
n
t
he
s
u
b
s
e
que
n
t
c
h
a
pt
e
r
s
.
I
n
s
e
c
t
i
o
n
2
,
we
pr
o
vi
de
a
n
o
v
e
r
vi
e
w
o
f
t
h
e
da
t
a
s
e
t
us
e
d
i
n
o
ur
i
nv
e
s
t
i
ga
t
i
o
n
a
n
d
t
h
e
m
e
t
h
o
d
o
l
o
g
y
ut
il
i
z
e
d
in
de
v
e
l
o
p
i
ng
pr
e
d
i
c
t
i
v
e
m
o
de
l
s
.
F
ur
t
h
e
r
m
o
r
e
,
i
n
s
e
c
t
i
o
n
3
,
we
p
r
e
s
e
n
t
t
h
e
r
e
s
u
l
t
s
o
f
o
u
r
s
t
udy
,
i
n
c
l
ud
i
n
g
m
o
de
l
pe
r
f
o
r
m
a
n
c
e
m
e
t
r
i
c
s
a
n
d
i
ns
i
g
h
t
s
ga
i
n
e
d
f
r
o
m
S
HA
P
a
n
a
ly
s
i
s
.
F
i
na
l
ly
,
i
n
s
e
c
t
i
o
n
4
,
we
c
o
n
c
l
ude
o
ur
f
i
nd
i
ngs
a
n
d
d
i
s
c
u
s
s
t
h
e
i
r
im
p
l
i
c
a
t
i
o
n
s
f
o
r
h
e
a
l
t
hc
a
r
e
de
c
i
s
io
n
-
m
a
k
i
ng
pr
o
c
e
s
s
e
s
.
2.
RE
S
E
AR
CH
M
E
T
HO
D
I
n
t
hi
s
s
t
ud
y
,
we
a
n
a
ly
z
e
d
a
c
o
m
pr
e
h
e
ns
i
ve
da
t
a
s
e
t
c
o
m
pr
i
s
i
ng
299
pa
t
i
e
n
t
s
d
i
a
g
n
o
s
e
d
w
i
t
h
C
VD
.
T
hi
s
da
t
a
s
e
t
i
n
c
l
ude
d
e
s
s
e
n
t
i
a
l
a
t
tr
i
b
ut
e
s
s
uc
h
a
s
a
n
a
e
m
i
a
s
t
a
t
us
,
hi
g
h
bl
o
o
d
pr
e
s
s
ur
e
,
c
r
e
a
t
i
ni
ne
ph
o
s
ph
o
k
i
na
s
e
l
e
v
e
l
s
,
a
n
d
d
i
a
b
e
t
e
s
s
t
a
t
us
,
a
m
o
n
g
ot
h
e
r
s
,
tot
a
l
i
ng
13
f
e
a
t
ur
e
s
.
T
h
e
s
e
f
e
a
t
ur
e
s
we
r
e
m
e
t
i
c
u
l
o
us
ly
c
h
o
s
e
n
ba
s
e
d
o
n
t
h
e
i
r
c
li
n
i
c
a
l
r
e
l
e
va
n
c
e
a
n
d
po
t
e
n
t
i
a
l
im
pa
c
t
o
n
pr
e
d
i
c
t
i
n
g
m
o
r
t
a
l
i
t
y
due
to
h
e
a
r
t
f
a
il
ur
e
.
A
dd
i
t
i
o
n
a
ll
y
,
a
l
l
299
pa
t
i
e
n
t
s
h
a
v
e
pr
e
vi
o
us
e
x
pe
r
i
e
n
c
e
s
w
i
t
h
he
a
r
t
f
a
il
ur
e
s
c
a
t
e
go
r
i
z
e
d
i
n
t
o
c
l
a
s
s
e
s
I
I
I
or
I
V
o
f
t
h
e
Ne
w
Y
o
r
k
He
a
r
t
A
s
s
o
c
i
a
t
i
o
n
(
NY
HA
)
c
l
a
s
s
i
f
i
c
a
t
i
o
n
s
y
s
t
e
m
f
o
r
h
e
a
r
t
f
a
il
ur
e
s
t
a
ge
s
.
T
o
t
a
c
kl
e
t
h
e
i
nh
e
r
e
n
t
c
l
a
s
s
i
m
ba
l
a
nc
e
i
n
t
h
e
da
t
a
s
e
t
,
we
u
t
i
l
i
z
e
d
t
w
o
t
e
c
h
ni
que
s
:
s
y
n
t
h
e
t
i
c
mi
n
o
r
i
t
y
o
v
e
r
-
s
a
m
p
li
ng
t
e
c
h
ni
que
(
S
M
OT
E
)
a
n
d
b
oot
s
tr
a
p
p
i
n
g.
S
M
OT
E
wa
s
e
m
p
l
o
y
e
d
to
ge
n
e
r
a
t
e
s
y
n
t
h
e
t
i
c
s
a
m
p
l
e
s
f
o
r
t
h
e
m
i
n
o
r
i
t
y
c
l
a
s
s
(
de
a
t
h
e
v
e
n
t
)
,
a
i
mi
ng
to
b
a
l
a
n
c
e
t
h
e
c
l
a
s
s
d
i
s
t
r
i
b
ut
i
o
n
a
n
d
m
i
t
i
ga
t
e
p
ot
e
n
t
i
a
l
mo
de
l
bi
a
s
to
wa
r
ds
t
h
e
m
a
j
o
r
i
t
y
c
l
a
s
s
.
F
ur
t
h
e
r
m
o
r
e
,
b
o
ot
s
tr
a
pp
i
n
g
wa
s
im
p
l
e
m
e
n
t
e
d
to
a
ug
m
e
n
t
t
h
e
da
t
a
s
e
t
s
i
z
e
.
F
i
gur
e
1
de
p
i
c
t
s
t
h
e
c
l
a
s
s
d
i
s
t
r
i
b
ut
i
o
n
w
i
t
hi
n
t
h
e
da
t
a
s
e
t.
M
o
r
e
o
v
e
r
,
we
b
a
l
a
n
c
e
d
t
h
e
d
i
s
t
r
i
b
ut
i
o
n
o
f
th
e
t
a
r
ge
t
l
a
b
e
l
(
de
a
t
h
e
v
e
n
t
)
to
a
ppr
o
xi
m
a
t
e
l
y
400
i
ns
t
a
n
c
e
s
f
o
r
e
a
c
h
da
t
a
s
e
t.
M
o
r
e
o
v
e
r
,
e
x
p
l
o
r
a
to
r
y
da
t
a
a
n
a
l
y
s
i
s
wa
s
c
o
n
duc
t
e
d
to
ga
i
n
i
ns
i
g
h
t
s
i
n
t
o
t
h
e
da
t
a
s
e
t
’
s
c
ha
r
a
c
t
e
r
i
s
t
i
c
s
,
i
n
c
l
ud
i
ng
t
h
e
di
s
t
r
i
b
ut
i
o
n
o
f
n
u
m
e
r
i
c
a
l
a
n
d
c
a
t
e
g
o
r
i
c
a
l
f
e
a
t
ur
e
s
.
F
i
gur
e
s
2
il
l
us
t
r
a
t
e
t
h
e
c
or
r
e
l
a
t
i
o
n
m
a
t
r
i
x
w
hi
c
h
s
h
o
ws
t
h
e
r
e
l
a
t
i
o
n
s
hi
p
s
b
e
t
we
e
n
t
wo
v
a
r
i
a
bl
e
s
o
f
t
h
e
da
t
a
s
e
t.
Evaluation Warning : The document was created with Spire.PDF for Python.
I
n
t
J
I
n
f
&
C
o
m
m
u
n
T
e
c
hn
o
l
I
S
S
N:
2252
-
8776
E
nhanc
ing
pr
e
dictive
mode
ll
ing
and
int
e
r
pr
e
tabi
li
ty
in
he
ar
t
f
ail
ur
e
…
(
N
iaz
A
s
hr
af
K
han
)
13
F
i
gur
e
1.
Di
s
t
r
i
b
ut
i
o
n
o
f
t
a
r
ge
t
l
a
b
e
l
F
i
gur
e
2.
C
o
r
r
e
l
a
t
i
o
n
m
a
t
r
i
x
2.
1.
Dat
a
p
r
e
p
r
o
c
e
s
s
in
g
an
d
f
e
at
u
r
e
e
n
gin
e
e
r
in
g
Our
m
e
t
h
o
do
l
o
g
y
b
e
ga
n
w
i
t
h
m
e
t
i
c
u
l
o
u
s
da
t
a
pr
e
pr
o
c
e
s
s
i
n
g
t
o
e
n
s
ur
e
t
h
e
da
t
a
s
e
t
’
s
s
u
i
t
a
bil
i
t
y
f
o
r
pr
e
d
i
c
t
i
v
e
m
o
de
l
li
ng.
G
i
ve
n
t
h
e
pr
e
v
a
l
e
n
c
e
o
f
c
l
a
s
s
i
m
ba
l
a
nc
e
i
nhe
r
e
n
t
i
n
h
e
a
l
t
h
c
a
r
e
da
t
a
s
e
t
s
,
we
e
m
p
l
o
y
e
d
S
M
OT
E
a
n
d
b
o
ot
s
tr
a
pp
i
n
g.
S
M
OT
E
e
f
f
e
c
t
i
v
e
ly
r
e
s
o
l
v
e
d
t
h
e
c
l
a
s
s
d
i
s
pa
r
i
t
y
i
s
s
u
e
by
g
e
n
e
r
a
t
i
n
g
a
r
t
i
f
i
c
i
a
l
s
a
m
p
l
e
s
f
o
r
t
h
e
un
de
r
r
e
pr
e
s
e
n
t
e
d
c
l
a
s
s
.
(
de
a
t
h
e
ve
n
t
)
,
t
h
e
r
e
by
b
a
l
a
nc
i
ng
t
h
e
d
i
s
t
r
i
b
ut
i
o
n
o
f
t
h
e
t
a
r
ge
t
l
a
b
e
l
.
C
o
n
c
ur
r
e
n
t
l
y
,
b
o
ot
s
tr
a
ppi
n
g
a
ug
m
e
n
t
e
d
t
h
e
da
t
a
s
e
t
’
s
s
i
z
e
t
h
r
o
ugh
r
e
s
a
m
p
li
ng
w
i
t
h
r
e
p
l
a
c
e
m
e
n
t
f
r
o
m
t
h
e
o
r
i
g
i
na
l
da
t
a
,
e
n
ha
n
c
i
ng
t
h
e
m
o
de
l
’
s
r
o
b
us
t
n
e
s
s
a
ga
i
ns
t
o
v
e
r
f
i
t
t
i
n
g.
2
.
2.
M
od
e
l
e
val
u
at
ion
an
d
s
e
l
e
c
t
ion
F
o
l
l
o
w
i
ng
da
t
a
pr
e
p
r
o
c
e
s
s
i
ng,
we
pr
o
c
e
e
de
d
w
i
th
m
o
de
l
e
v
a
l
u
a
t
i
o
n
a
n
d
s
e
l
e
c
t
i
o
n
t
o
i
de
n
t
i
f
y
t
he
m
o
s
t
s
u
i
t
a
bl
e
a
l
go
r
i
t
hm
s
f
o
r
pr
e
di
c
t
i
n
g
de
a
t
h
e
ve
n
t
s
i
n
h
e
a
r
t
di
s
e
a
s
e
pa
t
i
e
n
t
s
.
L
e
v
e
r
a
g
i
ng
a
d
i
v
e
r
s
e
s
e
t
o
f
m
a
c
hi
ne
l
e
a
r
ni
n
g
m
o
de
l
s
,
i
n
c
l
ud
i
ng
GB
,
RF
,
a
n
d
XGB
,
we
c
o
n
duc
t
e
d
r
i
go
r
o
us
t
r
a
i
ni
ng
a
nd
t
e
s
t
i
n
g
pr
o
c
e
dur
e
s
o
n
t
h
e
da
t
a
s
e
t.
A
c
o
n
c
i
s
e
e
x
p
l
a
n
a
t
i
o
n
o
f
e
a
c
h
m
o
de
l
i
s
g
i
ve
n
b
e
l
o
w.
Evaluation Warning : The document was created with Spire.PDF for Python.
I
S
S
N
:
2252
-
8776
I
n
t
J
I
n
f
&
C
o
m
m
u
n
T
e
c
hn
o
l
,
Vo
l
.
1
4
,
N
o.
1
,
A
pr
i
l
20
2
5
:
11
-
19
14
2.
2.
1.
XG
B
oos
t
XG
B
i
s
a
s
o
phi
s
t
i
c
a
t
e
d
i
m
p
l
e
m
e
n
t
a
t
i
o
n
o
f
gr
a
d
i
e
n
t
e
n
h
a
nc
e
m
e
n
t
a
l
go
r
i
t
hm
s
de
s
i
g
n
e
d
f
o
r
e
f
f
i
c
i
e
nc
y
.
It
’
s
kn
o
wn
f
o
r
i
t
s
s
pe
e
d
a
n
d
a
c
c
ur
a
c
y
in
h
a
n
d
li
ng
l
a
r
ge
da
t
a
s
e
t
s
.
T
h
e
o
b
j
e
c
t
i
v
e
f
u
n
c
t
i
o
n
s
’
c
r
uc
i
a
l
c
h
a
r
a
c
t
e
r
i
s
t
i
c
li
e
s
i
n
t
h
e
i
r
c
o
m
po
s
i
t
i
o
n
o
f
t
w
o
e
l
e
men
t
s
:
t
h
e
t
r
a
i
ni
ng
l
o
s
s
a
n
d
t
h
e
r
e
gu
l
a
r
i
z
a
t
i
o
n
t
e
r
m
.
O(
θ
)
=
L
(
θ
)
+
Ω(
θ
)
(
1)
I
n
(
1)
,
L
i
s
t
h
e
tr
a
i
ni
n
g
l
o
s
s
f
u
n
c
t
i
o
n
a
n
d
Ω
i
s
t
h
e
r
e
gu
l
a
r
i
z
a
t
i
o
n
t
e
r
m
.
2.
2.
2.
Rand
om
f
or
e
s
t
RF
i
s
a
n
e
ns
e
m
b
l
e
l
e
a
r
ni
n
g
m
e
t
h
o
d
t
h
a
t
c
o
n
s
t
r
uc
t
s
m
u
l
t
i
p
l
e
de
c
i
s
i
o
n
t
r
e
e
s
dur
i
n
g
t
r
a
i
ni
ng
a
n
d
o
u
t
pu
t
s
t
h
e
m
o
de
o
f
t
h
e
c
l
a
s
s
e
s
a
s
t
h
e
pr
e
d
i
c
t
i
o
n
o
f
i
n
d
i
v
i
dua
l
t
r
e
e
s
.
T
h
e
s
e
l
e
c
t
i
o
n
o
f
t
h
e
r
oot
n
o
de
i
s
b
a
s
e
d
o
n
us
i
n
g
t
h
e
G
i
n
i
I
n
de
x
,
w
hi
c
h
v
a
r
i
e
s
b
e
t
we
e
n
0
(
i
n
d
i
c
a
t
i
n
g
pe
r
f
e
c
t
pur
i
t
y
)
a
n
d
1
(
r
e
f
l
e
c
t
i
ng
hi
g
h
e
r
i
ne
qua
li
t
y
)
.
T
h
e
G
i
ni
I
n
de
x
c
a
l
c
u
l
a
t
i
o
n
c
a
n
b
e
e
x
pr
e
s
s
e
d
a
s
(
2)
.
Gi
ni
I
n
de
x
=
1
-
[
(
P
)
2
+
(
N
)
2
]
(
2)
He
r
e
i
n
(
2)
,
P
is
t
h
e
pr
o
b
a
bil
i
t
y
o
f
a
po
s
i
t
i
v
e
c
l
a
s
s
,
a
n
d
N
is
t
h
e
pr
o
b
a
bi
li
t
y
o
f
a
ne
ga
t
i
v
e
c
l
a
s
s
.
2.
2.
3.
G
B
oos
t
GB
oo
s
t
i
s
a
n
a
dva
n
c
e
d
e
ns
e
m
b
l
e
l
e
a
r
ni
ng
t
e
c
hni
que
t
h
a
t
b
u
i
l
d
s
a
s
t
r
o
n
g
pr
e
di
c
t
i
v
e
m
o
de
l
by
s
e
que
n
t
i
a
ll
y
a
dd
i
n
g
w
e
a
k
l
e
a
r
n
e
r
s
,
t
y
p
i
c
a
ll
y
de
c
i
s
io
n
tr
e
e
s
.
I
n
t
h
i
s
m
e
t
h
o
d,
e
a
c
h
s
u
bs
e
que
n
t
m
o
de
l
i
s
t
r
a
i
n
e
d
to
c
o
r
r
e
c
t
t
h
e
e
r
r
o
r
s
o
f
t
h
e
pr
e
vi
o
us
o
ne
s
,
m
ini
mi
z
i
ng
a
s
pe
c
i
f
i
e
d
l
o
s
s
f
u
nc
t
i
o
n
.
T
hi
s
pr
o
c
e
s
s
r
e
s
u
l
t
s
i
n
a
r
o
b
us
t
m
o
de
l
t
h
a
t
c
a
n
e
f
f
e
c
t
i
v
e
ly
h
a
n
d
l
e
c
o
m
p
l
e
x
pa
tt
e
r
n
s
i
n
t
h
e
da
t
a
,
i
m
pr
o
vi
n
g
t
h
e
a
c
c
ur
a
c
y
o
f
pr
e
d
i
c
t
i
o
n
s
.
2.
2.
4.
Vot
i
n
g
an
d
s
t
ac
k
in
g
c
l
as
s
i
f
ie
r
VC
i
s
a
n
e
n
s
e
m
b
l
e
t
e
c
hni
que
t
h
a
t
c
o
m
bi
ne
s
pr
e
d
i
c
t
i
o
ns
f
r
o
m
m
u
l
t
i
p
l
e
i
n
d
i
vi
du
a
l
c
l
a
s
s
i
f
i
e
r
s
to
de
t
e
r
m
i
ne
t
h
e
f
i
na
l
c
l
a
s
s
l
a
b
e
l
t
h
r
o
ugh
a
m
a
j
o
r
i
t
y
vot
i
n
g
m
e
c
h
a
ni
s
m
.
T
hi
s
a
ppr
o
a
c
h
l
e
v
e
r
a
ge
s
t
h
e
s
t
r
e
n
gt
h
s
o
f
d
i
f
f
e
r
e
n
t
a
l
go
r
i
t
hm
s
t
o
a
c
hi
e
v
e
b
e
t
t
e
r
o
v
e
r
a
l
l
p
e
r
f
o
r
m
a
n
c
e
.
On
t
h
e
ot
h
e
r
h
a
n
d,
S
C
u
t
i
li
z
e
s
a
m
e
t
a
-
l
e
a
r
n
e
r
to
c
o
m
bi
ne
pr
e
d
i
c
t
i
o
ns
f
r
o
m
m
u
l
t
i
p
l
e
b
a
s
e
c
l
a
s
s
i
f
i
e
r
s
.
2.
3.
S
HAP
an
al
ys
is
f
o
r
in
t
e
r
p
r
e
t
ab
il
it
y
T
o
e
nh
a
n
c
e
t
h
e
i
n
t
e
r
pr
e
t
a
bi
li
t
y
o
f
o
ur
m
o
de
l
s
a
n
d
ga
i
n
i
ns
i
g
h
t
s
i
n
t
o
f
e
a
t
ur
e
i
m
po
r
t
a
n
c
e
,
we
e
m
p
l
o
y
e
d
S
H
A
P
a
n
a
ly
s
i
s
.
S
HA
P
pr
o
vi
de
s
a
unif
i
e
d
f
r
a
m
e
wo
r
k
f
o
r
e
x
p
l
a
i
n
i
ng
t
h
e
o
u
t
pu
t
o
f
m
a
c
hi
ne
l
e
a
r
ni
ng
m
o
de
l
s
by
a
s
s
i
g
ni
ng
e
a
c
h
f
e
a
t
ur
e
a
n
i
mpo
r
t
a
n
c
e
v
a
l
u
e
f
o
r
a
pa
r
t
i
c
u
l
a
r
pr
e
d
i
c
t
i
o
n
.
T
hi
s
tec
hni
que
f
a
c
il
i
t
a
t
e
s
t
h
e
de
t
e
c
t
i
o
n
o
f
m
o
de
l
bi
a
s
e
s
a
n
d
a
i
d
s
i
n
u
n
de
r
s
t
a
n
d
i
ng
t
h
e
r
e
a
s
o
ni
ng
b
e
hi
nd
i
n
d
i
vi
dua
l
pr
e
d
i
c
t
i
o
n
s
,
m
a
k
i
ng
i
t
a
v
a
l
ua
bl
e
too
l
f
o
r
v
a
li
da
t
i
ng
a
n
d
r
e
f
i
ni
ng
o
ur
m
o
de
l
s
.
=
1
∑
|
|
!
(
−
|
|
−
1
)
!
!
⊆
{
1
,
…
,
}
∖
{
}
[
(
∪
{
}
)
−
(
)
]
(
3)
He
r
e
,
i
n
(
3)
,
ϕ
i
r
e
pr
e
s
e
n
t
s
t
h
e
S
h
a
p
l
e
y
v
a
l
ue
f
o
r
f
e
a
t
ur
e
i.
N
i
s
t
h
e
n
u
m
be
r
o
f
po
s
s
i
bl
e
pe
r
m
ut
a
t
i
o
n
s
o
f
f
e
a
t
ur
e
s
.
M
i
s
t
h
e
tot
a
l
n
u
m
be
r
o
f
f
e
a
t
ur
e
s
.
S
i
s
t
h
e
s
ubs
e
t
o
f
f
e
a
t
ur
e
s
e
x
c
l
ud
i
n
g
f
e
a
t
ur
e
i
.
f
(
S
∪
{
i
}
)
i
s
t
h
e
m
o
de
l
’
s
o
u
t
pu
t
wh
e
n
i
nc
l
ud
i
n
g
f
e
a
t
ur
e
i
i
n
s
ub
s
e
t
S
.
f
(
S)
i
s
t
h
e
m
o
de
l
’
s
o
ut
pu
t
wi
t
h
o
ut
i
n
c
l
ud
i
ng
f
e
a
t
ur
e
i
i
n
s
u
b
s
e
t
S
.
3.
RE
S
UL
T
S
AN
D
DI
S
CU
S
S
I
ON
Our
a
s
s
e
s
s
m
e
n
t
i
n
c
l
ude
d
t
h
e
e
x
a
mi
na
t
i
o
n
o
f
v
a
r
i
o
us
e
v
a
l
ua
t
i
o
n
m
e
t
r
i
c
s
.
He
r
e
,
GB
oo
s
t
s
too
d
o
u
t
a
s
t
h
e
to
p
pe
r
f
o
r
m
e
r
,
a
c
hi
e
vi
ng
a
n
i
m
pr
e
s
s
i
ve
a
c
c
ur
a
c
y
o
f
91.
39%
.
T
h
e
pr
e
c
i
s
i
o
n
a
n
d
r
e
c
a
ll
s
c
o
r
e
s
f
o
r
b
ot
h
c
l
a
s
s
e
s
f
o
r
GB
,
a
l
o
n
g
w
i
t
h
t
h
e
ot
h
e
r
t
w
o
m
o
de
l
s
c
a
n
b
e
f
o
un
d
i
n
t
h
e
a
c
c
o
m
pa
nyi
ng
c
o
nf
u
s
i
o
n
m
a
t
r
i
x
i
n
F
i
gur
e
3
.
F
i
gur
e
3
(
a
)
c
o
n
t
a
i
ns
t
h
e
c
o
nf
us
i
o
n
m
a
t
r
i
x
o
f
GB
o
o
s
t
whi
l
e
F
i
gur
e
3
(
b
)
c
o
n
t
a
i
ns
t
h
e
c
o
nf
us
i
o
n
m
a
t
r
i
x
o
f
R
F
a
n
d
F
i
gur
e
3
(
c
)
c
o
n
t
a
i
n
s
t
h
e
c
o
nf
us
i
o
n
m
a
t
r
i
x
o
f
XG
B
.
W
e
f
ur
t
h
e
r
i
nve
s
t
i
ga
t
e
d
e
n
s
e
m
bl
e
l
e
a
r
ni
ng
t
e
c
h
ni
que
s
,
i
n
c
l
ud
i
ng
t
h
e
VC
a
n
d
SC
,
to
e
n
h
a
nc
e
pe
r
f
o
r
m
a
n
c
e
.
T
h
e
VC
,
whi
c
h
a
m
a
lga
m
a
t
e
d
pr
e
d
i
c
t
i
o
n
s
f
r
o
m
X
GB
a
n
d
RF
m
o
de
l
s
,
a
t
t
a
i
n
e
d
a
n
a
c
c
ur
a
c
y
o
f
91%
,
w
hi
l
e
t
he
S
C
yi
e
l
de
d
a
n
a
c
c
ur
a
c
y
o
f
Evaluation Warning : The document was created with Spire.PDF for Python.
I
n
t
J
I
n
f
&
C
o
m
m
u
n
T
e
c
hn
o
l
I
S
S
N:
2252
-
8776
E
nhanc
ing
pr
e
dictive
mode
ll
ing
and
int
e
r
pr
e
tabi
li
ty
in
he
ar
t
f
ail
ur
e
…
(
N
iaz
A
s
hr
af
K
han
)
15
92%
.
A
dd
i
t
i
o
na
l
ly
,
c
o
m
bi
ni
ng
pr
e
d
i
c
t
i
o
n
s
f
r
o
m
X
GB
a
n
d
RF
r
e
s
u
l
t
e
d
i
n
a
r
o
b
us
t
a
c
c
ur
a
c
y
o
f
91%
.
T
a
bl
e
1
pr
e
s
e
n
t
s
t
h
e
pe
r
f
o
r
m
a
n
c
e
s
u
mm
a
r
y
o
f
i
n
d
i
v
i
dua
l
m
o
de
l
s
(
GB
o
o
s
t
,
RF
,
a
n
d
XG
B
oo
s
t)
i
n
t
e
r
m
s
o
f
a
c
c
ur
a
c
y
,
pr
e
c
i
s
i
o
n
,
a
n
d
r
e
c
a
ll
,
w
hil
e
T
a
bl
e
2
s
u
m
m
a
r
i
z
e
s
th
e
pe
r
f
o
r
m
a
n
c
e
m
e
t
r
i
c
s
o
f
VC
,
SC
,
a
n
d
BC
i
n
pr
e
d
i
c
t
i
n
g
c
r
i
t
i
c
a
l
h
e
a
l
t
h
o
ut
c
o
m
e
s
f
o
r
C
VD
pa
t
i
e
n
t
s
,
c
o
nf
u
s
i
o
n
m
a
t
r
i
x
f
o
r
t
h
e
VC
a
n
d
B
C
i
s
pr
o
vi
de
d
i
n
F
i
g
ur
e
4.
F
i
gur
e
4
(
a
)
c
o
n
t
a
i
n
s
t
h
e
c
o
nf
u
s
i
o
n
m
a
t
r
i
x
o
f
V
C
whil
e
F
i
gur
e
4
(
b
)
c
o
n
t
a
i
n
s
t
h
e
c
o
nf
us
i
o
n
m
a
t
r
i
x
o
f
B
C
.
W
e
a
l
s
o
c
o
m
pa
r
e
d
w
i
t
h
pr
e
vi
o
us
wo
r
k,
wh
e
r
e
o
ur
m
o
de
l
pr
o
vi
de
s
s
upe
r
i
o
r
pe
r
f
o
r
m
a
n
c
e
,
a
s
s
h
o
wn
in
T
a
bl
e
3.
T
a
bl
e
1.
P
e
r
f
o
r
m
a
nc
e
s
u
m
m
a
r
y
o
f
t
h
e
e
n
s
e
m
b
l
e
t
e
c
hni
que
s
M
o
de
l
A
c
c
u
r
a
c
y
(
%
)
C
la
s
s
0
C
la
s
s
1
P
r
e
c
is
i
o
n
R
e
c
a
ll
P
r
e
c
is
i
o
n
R
e
c
a
ll
GB
91.39
0.90
0.93
0.92
0.90
RF
90.33
0.88
0.92
0.92
0.87
X
G
B
88.93
0.90
0.88
0.88
0.90
T
a
bl
e
2
.
P
e
r
f
o
r
m
a
nc
e
s
u
m
m
a
r
y
m
e
t
r
i
c
s
o
f
v
o
t
i
n
g,
s
t
a
c
k
i
n
g
a
n
d
bl
e
d
i
ng
c
l
a
s
s
if
i
e
r
s
M
o
de
l
A
c
c
u
r
a
c
y
(
%
)
C
la
s
s
0
C
la
s
s
1
P
r
e
c
is
i
o
n
R
e
c
a
ll
P
r
e
c
is
i
o
n
R
e
c
a
ll
VC
91
0.87
0.95
0.95
0.86
SC
92
0.90
0.94
0.94
0.89
BC
91
0.87
0.95
0.95
0.86
(
a
)
(
b
)
(
c
)
F
i
gur
e
3.
C
o
nf
u
s
i
o
n
m
a
t
r
i
x
o
f
;
(
a
)
GB
oo
s
t
,
(
b
)
r
a
n
do
m
f
o
r
e
s
t
c
l
a
s
s
i
f
i
e
r
,
a
n
d
(
c
)
XG
B
oo
s
t
Evaluation Warning : The document was created with Spire.PDF for Python.
I
S
S
N
:
2252
-
8776
I
n
t
J
I
n
f
&
C
o
m
m
u
n
T
e
c
hn
o
l
,
Vo
l
.
1
4
,
N
o.
1
,
A
pr
i
l
20
2
5
:
11
-
19
16
T
a
bl
e
3
.
C
o
m
pa
r
a
t
i
ve
a
n
a
ly
s
i
s
w
i
t
h
pr
e
vi
o
us
wo
r
k
s
P
a
pe
r
M
o
de
l
A
c
c
u
r
a
c
y
[
23
]
LR
83.3%
[
24
]
D
T
+
M
R
M
R
+
R
F
E
80.00%
[
25
]
B
R
F
+
C
H
2
76.25%
O
ur
w
o
r
k
GB
91.39%
(
a
)
(
b
)
F
i
gur
e
4
.
C
o
nf
u
s
i
o
n
m
a
t
r
i
x
o
f
(
a
)
v
ot
i
n
g
c
l
a
s
s
i
f
i
e
r
a
n
d
(
b
)
s
t
a
c
k
i
n
g
c
l
a
s
s
i
f
i
e
r
F
i
gur
e
5
c
o
r
r
e
s
p
o
n
ds
t
o
t
h
e
s
u
mm
a
r
y
p
l
o
t
f
o
r
t
h
e
F
i
gur
e
5(
a
)
XG
B
oo
s
t
a
n
d
F
i
gur
e
5(
b
)
GB
o
o
s
t
m
o
de
l
r
e
s
pe
c
t
i
ve
l
y
.
F
i
gur
e
6
a
l
s
o
pr
o
vi
de
s
t
h
e
S
H
A
P
a
n
a
ly
s
i
s
f
o
r
R
F
a
s
we
ll
.
T
h
e
s
e
p
l
o
t
s
vi
s
ua
li
z
e
t
h
e
g
l
o
b
a
l
f
e
a
t
ur
e
i
m
po
r
t
a
n
c
e
,
s
h
o
w
i
n
g
t
h
e
i
m
pa
c
t
o
f
e
a
c
h
f
e
a
t
ur
e
o
n
m
o
de
l
pr
e
d
i
c
t
i
o
ns
a
c
r
o
s
s
t
h
e
da
t
a
s
e
t.
A
n
a
ly
z
i
n
g
t
h
e
S
HA
P
c
h
a
r
t
pr
o
vi
de
s
t
h
r
e
e
ke
y
i
ns
i
g
h
t
s
:
f
i
r
s
t
l
y
,
f
e
a
t
ur
e
s
a
t
t
h
e
to
p
e
x
e
r
t
t
h
e
m
o
s
t
s
i
g
ni
f
i
c
a
n
t
i
m
pa
c
t
o
n
pr
e
d
i
c
t
i
o
n
s
,
w
hi
l
e
t
h
o
s
e
a
t
t
h
e
b
o
tt
o
m
h
a
v
e
l
e
s
s
e
r
i
nf
l
ue
nc
e
.
S
e
c
o
n
d
l
y
,
t
h
e
po
s
i
t
i
o
n
o
f
e
a
c
h
f
e
a
t
ur
e
b
a
r
r
e
f
l
e
c
t
s
i
t
s
r
a
n
ge
o
f
i
m
pa
c
t
o
n
pr
e
di
c
t
i
o
n
s
.
L
a
s
t
l
y
,
e
a
c
h
dot
r
e
pr
e
s
e
n
t
s
a
da
t
a
p
o
i
n
t
,
wi
t
h
t
h
e
de
n
s
i
t
y
a
r
o
un
d
a
r
e
g
i
o
n
i
n
d
i
c
a
t
i
n
g
t
h
e
c
o
n
t
r
a
c
t
i
o
n
o
f
f
e
a
t
ur
e
v
a
l
ue
s
.
Am
o
n
g
t
h
e
a
n
a
ly
z
e
d
f
e
a
t
ur
e
s
,
T
i
m
e
e
m
e
r
ge
s
a
s
th
e
m
o
s
t
c
r
uc
i
a
l
,
f
o
l
l
o
we
d
by
S
e
r
u
m
_c
r
e
a
t
i
ni
ne
,
w
i
t
h
hi
g
h
_
bl
o
o
d_pr
e
s
s
ur
e
e
xhi
bi
t
i
ng
t
h
e
l
e
a
s
t
i
nf
l
ue
nc
e
.
Not
a
bl
y
,
f
e
a
t
ur
e
s
l
i
ke
t
i
m
e
,
s
e
r
u
m
_c
r
e
a
t
i
ni
ne
,
e
j
e
c
t
i
o
n
_
f
r
a
c
t
i
o
n
,
p
l
a
t
e
l
e
t
s
,
a
n
d
a
ge
de
m
o
n
s
t
r
a
t
e
a
r
i
g
h
t
-
t
a
i
l
e
d
d
i
s
t
r
i
b
ut
i
o
n
,
i
n
d
i
c
a
t
i
n
g
hi
g
h
e
r
v
a
l
ue
s
s
i
g
ni
f
i
c
a
n
tl
y
im
pa
c
t
pr
e
d
i
c
t
i
o
n
s
.
C
e
r
t
a
i
n
f
e
a
t
ur
e
s
,
i
n
c
l
ud
in
g
t
i
m
e
,
s
e
r
u
m
_c
r
e
a
t
i
ni
ne
,
e
j
e
c
t
i
o
n
_
f
r
a
c
t
i
o
n
,
a
n
d
d
i
a
be
t
e
s
,
e
xhi
b
i
t
hi
g
h
de
ns
i
t
y
w
i
t
hi
n
s
p
e
c
i
f
i
c
v
a
l
ue
r
a
n
ge
s
,
s
ugge
s
t
i
n
g
c
o
n
c
e
n
t
r
a
t
e
d
da
t
a
p
o
i
n
t
s
.
L
o
c
a
l
S
H
A
P
a
n
a
ly
s
i
s
,
a
s
de
p
i
c
t
e
d
i
n
F
i
gur
e
7
,
un
de
r
s
c
o
r
e
s
t
h
e
im
po
r
t
a
n
c
e
o
f
e
x
p
l
o
r
i
n
g
i
n
d
i
v
i
dua
l
c
a
s
e
s
to
g
r
a
s
p
d
i
v
e
r
ge
n
c
e
s
f
r
o
m
b
r
o
a
de
r
tr
e
n
ds
i
d
e
n
t
i
f
i
e
d
t
h
r
o
ug
h
g
l
o
ba
l
im
po
r
t
a
n
c
e
a
n
a
ly
s
i
s
.
L
o
c
a
l
S
H
A
P
a
n
a
ly
s
i
s
o
n
t
wo
r
a
n
do
m
da
t
a
po
i
n
t
s
wi
t
h
t
h
r
e
e
f
e
a
t
ur
e
s
pr
o
vi
d
in
g
m
o
s
t
im
pa
c
t
i
s
s
h
o
wn
i
n
F
i
gur
e
7
(
a
)
wi
t
h
F
i
gur
e
7
(
b
)
s
ho
wi
n
g
w
i
t
h
t
wo
f
e
a
t
ur
e
s
pr
o
vi
d
i
ng
t
h
e
m
o
s
t
i
m
pa
c
t.
(
a
)
(
b
)
F
i
gur
e
5.
S
HA
P
a
n
a
ly
s
i
s
o
n
(
a
)
XG
B
oo
s
t
a
n
d
(
b
)
GB
oo
s
t
a
l
go
r
i
t
hm
Evaluation Warning : The document was created with Spire.PDF for Python.
I
n
t
J
I
n
f
&
C
o
m
m
u
n
T
e
c
hn
o
l
I
S
S
N:
2252
-
8776
E
nhanc
ing
pr
e
dictive
mode
ll
ing
and
int
e
r
pr
e
tabi
li
ty
in
he
ar
t
f
ail
ur
e
…
(
N
iaz
A
s
hr
af
K
han
)
17
F
i
gur
e
6
.
S
HA
P
a
n
a
ly
s
i
s
o
n
r
a
n
do
m
f
o
r
e
s
t
c
l
a
s
s
if
ier
(
a
)
(
b
)
F
i
gur
e
7
.
L
o
c
a
l
S
HA
P
a
n
a
ly
s
i
s
o
n
t
wo
r
a
n
do
m
da
t
a
po
i
n
t
s
(
a
)
wi
t
h
t
h
r
e
e
f
e
a
t
ur
e
s
pr
o
vi
d
i
n
g
m
o
s
t
i
m
p
a
c
t
a
n
d
(
b
)
w
i
t
h
t
w
o
f
e
a
t
ur
e
s
pr
o
vi
d
i
ng
m
o
s
t
i
m
pa
c
t
4.
CONC
L
USI
ON
T
o
c
o
n
c
l
ude
,
t
hi
s
s
t
ud
y
s
h
o
wc
a
s
e
d
t
h
e
e
f
f
e
c
t
i
v
e
n
e
s
s
o
f
a
d
v
a
n
c
e
d
b
o
o
s
t
i
n
g
a
l
go
r
i
t
hm
s
,
e
n
s
e
m
b
le
m
e
t
h
o
ds
,
a
n
d
S
H
A
P
a
n
a
ly
s
i
s
i
n
im
pr
o
vi
n
g
pr
e
d
i
c
t
i
v
e
m
o
de
ll
i
ng
a
n
d
i
n
t
e
r
pr
e
t
a
bi
li
t
y
f
o
r
h
e
a
r
t
f
a
i
l
ur
e
pr
e
d
i
c
t
i
o
n
.
R
e
s
u
l
t
s
de
m
o
n
s
t
r
a
t
e
d
hi
g
h
pr
e
d
i
c
t
i
v
e
p
e
r
f
o
r
m
a
n
c
e
,
w
i
t
h
t
h
e
GB
o
o
s
t
m
o
de
l
a
c
hi
e
vi
ng
a
n
a
c
c
ur
a
c
y
o
f
91.
39%
.
E
n
s
e
m
b
l
e
t
e
c
hni
que
s
f
ur
t
h
e
r
b
o
l
s
t
e
r
e
d
pe
r
f
o
r
m
a
n
c
e
,
w
i
t
h
a
c
c
ur
a
c
i
e
s
r
e
a
c
hi
ng
91.
00%
.
F
ur
t
h
e
r
m
o
r
e
,
we
c
o
n
duc
t
e
d
b
ot
h
l
o
c
a
l
a
n
d
g
l
o
b
a
l
S
HA
P
a
n
a
ly
s
e
s
to
ga
i
n
i
ns
i
g
h
t
s
i
n
t
o
f
e
a
t
ur
e
i
m
po
r
tan
c
e
a
n
d
i
nd
i
v
i
dua
l
pr
e
d
i
c
t
i
o
n
s
.
T
h
e
gl
o
b
a
l
S
H
A
P
a
na
l
y
s
is
pr
o
vi
de
d
a
c
o
m
pr
e
he
n
s
i
ve
un
de
r
s
t
a
n
d
i
n
g
o
f
t
h
e
o
v
e
r
a
l
l
im
pa
c
t
o
f
f
e
a
t
ur
e
s
o
n
m
o
de
l
pr
e
d
i
c
t
i
o
ns
,
hi
g
hli
gh
t
i
n
g
ke
y
f
a
c
t
o
r
s
s
uc
h
a
s
t
i
m
e
,
S
e
r
um
_c
r
e
a
t
i
ni
ne
,
a
n
d
e
j
e
c
t
i
o
n
_
f
r
a
c
t
i
o
n
.
On
t
h
e
ot
h
e
r
h
a
n
d,
l
o
c
a
l
S
HA
P
a
na
l
y
s
i
s
un
d
e
r
s
c
o
r
e
d
t
h
e
i
m
po
r
t
a
n
c
e
o
f
e
x
p
l
o
r
i
ng
i
nd
i
v
i
dua
l
c
a
s
e
s
t
o
g
r
a
s
p
de
vi
a
t
i
o
n
s
f
r
o
m
b
r
o
a
de
r
t
r
e
n
ds
i
d
e
n
t
i
f
i
e
d
t
h
r
o
ugh
g
l
o
b
a
l
S
H
A
P
a
n
a
ly
s
is
.
T
h
e
s
e
f
i
nd
i
n
g
s
u
n
de
r
s
c
o
r
e
t
h
e
s
i
g
ni
f
i
c
a
n
c
e
o
f
t
r
a
n
s
pa
r
e
nt
a
n
d
i
n
t
e
r
pr
e
t
a
bl
e
m
a
c
hi
ne
l
e
a
r
ni
ng
m
o
de
l
s
i
n
h
e
a
l
t
h
c
a
r
e
de
c
i
s
i
o
n
-
m
a
k
i
ng
pr
o
c
e
s
s
e
s
.
B
y
i
n
t
e
gr
a
t
i
n
g
S
HA
P
a
n
a
l
y
s
i
s
,
we
n
o
t
o
nl
y
a
c
hi
e
v
e
d
e
xc
e
pt
i
o
n
a
l
p
r
e
d
i
c
t
i
v
e
pe
r
f
o
r
m
a
n
c
e
b
ut
a
l
s
o
ga
i
ne
d
v
a
l
ua
bl
e
i
ns
i
g
h
t
s
i
n
to
m
o
r
t
a
l
i
t
y
pr
e
d
i
c
t
i
o
n
f
a
c
t
o
r
s
.
S
uc
h
i
ns
i
g
h
t
s
a
r
e
pi
v
o
t
a
l
f
o
r
i
n
f
o
r
m
e
d
de
c
i
s
i
o
n
-
m
a
k
i
n
g
i
n
c
li
n
i
c
a
l
s
e
t
t
i
n
gs
,
f
a
c
il
i
t
a
t
i
n
g
pe
r
s
o
n
a
li
z
e
d
t
r
e
a
t
m
e
n
t
s
t
r
a
t
e
gi
e
s
a
n
d
i
n
t
e
r
v
e
n
t
i
o
ns
f
o
r
C
VD
pa
t
i
e
n
t
s
.
F
ut
ur
e
r
e
s
e
a
r
c
h
c
o
u
l
d
e
x
p
l
o
r
e
a
dd
i
t
i
o
na
l
f
e
a
t
ur
e
s
a
n
d
l
a
r
ge
r
da
t
a
s
e
t
s
to
e
n
ha
n
c
e
m
o
de
l
pe
r
f
o
r
m
a
n
c
e
f
ur
t
h
e
r
.
I
n
v
e
s
t
i
ga
t
i
n
g
t
e
m
po
r
a
l
d
y
na
m
i
c
s
a
n
d
i
n
t
e
gr
a
t
i
n
g
pa
t
i
e
n
t
-
s
pe
c
i
f
i
c
da
t
a
m
a
y
im
pr
o
v
e
pr
e
d
i
c
t
i
v
e
a
c
c
ur
a
c
y
a
n
d
pe
r
s
o
n
a
l
i
z
e
d
t
r
e
a
t
m
e
n
t
s
t
r
a
t
e
gi
e
s
.
P
r
o
s
pe
c
t
i
v
e
s
t
ud
i
e
s
v
a
li
da
t
i
n
g
m
o
de
l
pe
r
f
o
r
m
a
n
c
e
i
n
c
li
n
i
c
a
l
s
e
t
t
i
n
g
s
a
r
e
e
s
s
e
n
t
i
a
l
.
C
o
n
t
i
n
uo
us
r
e
f
i
n
e
m
e
n
t
o
f
pr
e
di
c
t
i
v
e
m
o
de
l
s
a
n
d
i
n
t
e
r
pr
e
t
a
bi
li
t
y
t
e
c
hni
que
s
r
e
m
a
i
ns
c
r
uc
i
a
l
f
o
r
a
dv
a
nc
i
ng
h
e
a
r
t
f
a
il
ur
e
m
a
n
a
ge
m
e
n
t
a
n
d
pa
t
i
e
n
t
c
a
r
e
.
Evaluation Warning : The document was created with Spire.PDF for Python.
I
S
S
N
:
2252
-
8776
I
n
t
J
I
n
f
&
C
o
m
m
u
n
T
e
c
hn
o
l
,
Vo
l
.
1
4
,
N
o.
1
,
A
pr
i
l
20
2
5
:
11
-
19
18
RE
F
E
R
E
NC
E
S
[
1]
S
.
A
r
y
a
l,
A
.
A
li
ma
da
di
,
I
.
M
a
na
ndha
r
,
B
.
J
o
e
,
a
nd
X
.
C
he
ng,
“
M
a
c
hi
ne
l
e
a
r
ni
ng
s
tr
a
te
g
y
f
or
gut
mi
c
r
o
bi
ome
-
ba
s
e
d
di
a
gn
os
ti
c
s
c
r
e
e
ni
ng
of
c
a
r
d
i
ov
a
s
c
ul
a
r
di
s
e
a
s
e
,”
H
y
pe
r
te
ns
io
n
,
vol
.
76,
n
o
.
5,
pp.
1555
–
1562,
N
ov
.
20
20,
do
i:
10.1161/
H
Y
P
E
R
T
E
N
S
I
O
N
A
H
A
.120.15885.
[
2]
M
.
J
uho
la
,
H
.
J
o
ut
s
i
j
o
ki
,
K
.
P
e
nt
ti
n
e
n,
a
nd
K
.
A
a
lt
o
-
S
e
tä
lä
,
“
D
e
t
e
c
ti
o
n
of
g
e
n
e
ti
c
c
a
r
di
a
c
di
s
e
a
s
e
s
b
y
C
a
2+
tr
a
ns
ie
nt
pr
of
i
le
s
us
in
g
ma
c
hi
n
e
l
e
a
r
ni
ng
m
e
th
o
ds
,”
Sc
ie
nt
if
ic
R
e
por
t
s
,
v
o
l.
8,
n
o
.
1,
p.
9355,
J
un.
2018,
do
i:
10.1038/s
41598
-
018
-
27695
-
5.
[
3]
B
.
H
.
M
.
v
a
n
de
r
V
e
ld
e
n,
H
.
J
.
K
ui
j
f
,
K
.
G
.
A
.
G
il
hui
js
,
a
nd
M
.
A
.
V
ie
r
g
e
ve
r
,
“
E
x
pl
a
in
a
bl
e
a
r
ti
f
ic
ia
l
in
t
e
ll
ig
e
n
c
e
(
X
A
I
)
in
de
e
p
le
a
r
ni
ng
-
ba
s
e
d
m
e
di
c
a
l
im
a
ge
a
na
l
y
s
is
,”
M
e
di
c
al
I
m
age
A
nal
y
s
is
,
v
ol
.
79,
p.
102470,
J
ul
.
2022,
do
i:
10.1016/j
.
me
di
a
.2022.102470.
[
4]
J
.
C
.
W
o
l
f
e
,
L
.
A
.
M
ik
he
e
v
a
,
H
.
H
a
gr
a
s
,
a
nd
N
.
R
.
Z
a
be
t,
“
A
n
e
x
pl
a
in
a
bl
e
a
r
ti
f
i
c
ia
l
in
te
ll
ig
e
nc
e
a
ppr
o
a
c
h
f
or
de
c
o
di
ng
th
e
e
nha
nc
e
r
hi
s
to
n
e
m
o
di
f
i
c
a
ti
o
ns
c
o
de
a
nd i
d
e
nt
i
f
i
c
a
ti
o
n
of
n
ove
l
e
nha
n
c
e
r
s
i
n D
r
o
s
o
phi
la
,”
G
e
nom
e
B
io
lo
gy
,
vol
. 22, n
o
.
1, p. 3
08,
D
e
c
. 2021, d
oi
:
10
.1186/s
13059
-
021
-
02532
-
7.
[
5]
F
.
S
.
A
l
o
ta
ib
i,
“
I
mpl
e
m
e
nt
a
ti
o
n
of
ma
c
hi
n
e
le
a
r
ni
ng
m
o
d
e
l
t
o
p
r
e
di
c
t
h
e
a
r
t
f
a
i
lu
r
e
di
s
e
a
s
e
,”
I
nt
e
r
nat
io
nal
J
our
nal
o
f
A
dv
anc
e
d
C
om
put
e
r
Sc
ie
n
c
e
and
A
ppl
ic
at
io
ns
,
vo
l.
10,
n
o
.
6,
pp.
261
–
268,
2019,
do
i:
10.14569/i
ja
c
s
a
.2019.0
100637.
[
6]
S
.
F
.
W
e
ng,
J
.
R
e
ps
,
J
.
K
a
i,
J
.
M
.
G
a
r
ib
a
ld
i,
a
nd
N
.
Q
ur
e
s
hi
,
“
C
a
n
m
a
c
hi
ne
-
l
e
a
r
ni
ng
i
mpr
ove
c
a
r
d
i
ov
a
s
c
ul
a
r
r
is
k
pr
e
di
c
ti
o
n
us
in
g
r
o
ut
in
e
c
li
ni
c
a
l
da
ta
?
,”
P
L
oS
O
N
E
,
vo
l.
12,
n
o
.
4,
p.
e
0174944,
A
pr
.
2
017,
do
i:
10.1371/j
o
u
r
na
l.
p
o
ne
.0174944.
[
7]
A
.
C
.
D
im
o
po
ul
o
s
e
t
al
.
,
“
M
a
c
hi
ne
le
a
r
ni
ng
m
e
th
o
d
o
l
o
gi
e
s
v
e
r
s
us
c
a
r
di
ov
a
s
c
ul
a
r
r
is
k
s
c
o
r
e
s
,
in
pr
e
di
c
ti
ng
di
s
e
a
s
e
r
is
k,”
B
M
C
M
e
di
c
al
R
e
s
e
ar
c
h M
e
th
odol
ogy
, v
o
l.
18, n
o
. 1, p. 179, D
e
c
. 2018, d
o
i:
10.1186/s
12874
-
018
-
0644
-
1.
[
8]
S
.
M
o
ha
n,
C
.
T
h
ir
uma
la
i,
a
nd
G
.
S
r
i
v
a
s
ta
v
a
,
“
E
f
f
e
c
t
i
v
e
he
a
r
t
di
s
e
a
s
e
p
r
e
di
c
ti
o
n
us
in
g
h
y
br
id
ma
c
hi
n
e
l
e
a
r
ni
ng
te
c
hni
qu
e
s
,”
I
E
E
E
A
c
c
e
s
s
, v
ol
. 7, pp. 81542
–
81554, 2019, d
o
i:
10.1109/AC
C
E
S
S
.2019.2923707.
[
9]
L
.
Y
a
ng
e
t
al
.
,
“
S
tu
d
y
of
c
a
r
di
ov
a
s
c
ul
a
r
di
s
e
a
s
e
p
r
e
d
ic
t
i
o
n
m
o
d
e
l
ba
s
e
d
o
n
r
a
n
d
o
m
f
or
e
s
t
in
e
a
s
te
r
n
C
hi
na
,”
Sc
ie
nt
if
ic
R
e
por
ts
,
vo
l.
10, n
o
. 1, p. 5245, M
a
r
. 2020, d
o
i:
10.1038/s
41598
-
020
-
62
133
-
5.
[
10]
M
.
M
.
R
.
K
.
M
a
mun
a
nd
T
.
E
l
f
o
ul
y
,
“
D
e
t
e
c
ti
o
n
o
f
c
a
r
di
ov
a
s
c
u
la
r
di
s
e
a
s
e
f
r
o
m
c
li
n
ic
a
l
pa
r
a
m
e
t
e
r
s
us
in
g
a
o
n
e
-
di
m
e
ns
i
o
na
l
c
o
n
vo
lu
ti
o
n
a
l
n
e
ur
a
l
n
e
tw
o
r
k,”
B
io
e
ngi
ne
e
r
in
g
,
v
o
l.
10,
n
o
.
7,
p.
796,
J
ul
.
20
23,
do
i:
10.3390/bi
oe
ngi
n
e
e
r
in
g10070796.
[
11]
A
.
M
uni
a
s
a
my
,
A
.
B
e
gum,
A
.
S
a
ba
ha
th
,
H
.
Y
a
qub,
a
nd
G
.
K
a
r
una
ka
r
a
n,
“
C
o
r
o
na
r
y
h
e
a
r
t
di
s
e
a
s
e
c
la
s
s
i
f
ic
a
ti
o
n
us
in
g
de
e
p
le
a
r
ni
ng
a
ppr
o
a
c
h
w
it
h
f
e
a
tu
r
e
s
e
l
e
c
ti
o
n
f
or
im
p
r
ov
e
d
a
c
c
ur
a
c
y
,”
T
e
c
hnol
ogy
and
H
e
al
th
C
ar
e
,
vo
l.
32,
n
o
.
3,
pp.
1991
–
2
007,
2024, do
i:
10.3233/
T
H
C
-
231807.
[
12]
Z
.
A
r
a
ba
s
a
di
,
R
.
A
li
z
a
de
hs
a
ni
,
M
.
R
o
s
ha
nz
a
mi
r
,
H
.
M
oo
s
a
e
i,
a
nd
A
.
A
.
Y
a
r
if
a
r
d,
“
C
o
mput
e
r
a
id
e
d
de
c
is
i
o
n
ma
ki
ng
f
or
he
a
r
t
di
s
e
a
s
e
de
t
e
c
ti
o
n
us
in
g
h
y
br
id
n
e
ur
a
l
n
e
tw
o
r
k
-
G
e
n
e
ti
c
a
lg
o
r
it
hm,”
C
om
put
e
r
M
e
th
ods
and
P
r
ogr
am
s
in
B
io
m
e
di
c
in
e
,
v
o
l.
141,
pp. 19
–
26, Apr
. 2017, do
i:
10.1016
/j
.c
mpb.2017.01.004.
[
13]
T
.
K
.
R
e
v
a
th
i,
S
.
B
a
la
s
ubr
a
ma
ni
a
m,
V
.
S
ur
e
s
hkuma
r
,
a
nd
S
.
D
ha
na
s
e
ka
r
a
n,
“
A
n
im
pr
ove
d
l
o
ng
s
hor
t
-
t
e
r
m
me
m
o
r
y
a
lg
o
r
it
h
m
f
o
r
c
a
r
di
ov
a
s
c
ul
a
r
di
s
e
a
s
e
pr
e
di
c
ti
o
n,”
D
ia
gnos
ti
c
s
,
v
ol
.
14,
no
.
3,
p.
239,
J
a
n.
2
024,
do
i:
10.3390/di
a
gn
o
s
ti
c
s
14030239.
[
14]
M
.
D
.
A
.
H
o
s
s
e
n
e
t
al
.
,
“
S
upe
r
v
is
e
d
ma
c
hi
n
e
l
e
a
r
ni
ng
-
ba
s
e
d
c
a
r
di
ov
a
s
c
ul
a
r
di
s
e
a
s
e
a
na
l
y
s
is
a
nd
pr
e
di
c
ti
o
n,”
M
at
he
m
at
ic
al
P
r
obl
e
m
s
i
n E
ngi
ne
e
r
in
g
, vo
l.
2021, pp. 1
–
10, D
e
c
. 2021, d
o
i:
10.1155/2021/
1792201.
[
15]
R
.
B
ha
r
ti
,
A
.
K
ha
mpa
r
ia
,
M
.
S
ha
ba
z
,
G
.
D
hi
ma
n,
S
.
P
a
nd
e
,
a
nd
P
.
S
in
gh,
“
P
r
e
di
c
ti
o
n
of
he
a
r
t
di
s
e
a
s
e
us
in
g
a
c
o
mbi
na
ti
o
n
of
ma
c
hi
n
e
l
e
a
r
ni
ng
a
nd
de
e
p
l
e
a
r
ni
ng,”
C
om
put
at
io
nal
I
n
te
ll
ig
e
nc
e
and
N
e
ur
os
c
ie
nc
e
,
v
o
l.
2021,
no
.
1,
J
a
n.
2021,
do
i:
10.1155/2021/
8387680.
[
16]
A
.
A
lq
a
ht
a
ni
,
S
.
A
ls
uba
i,
M
.
S
ha
,
L
.
V
il
c
e
k
ov
a
,
a
nd
T
.
J
a
ve
d,
“
C
a
r
di
ov
a
s
c
ul
a
r
di
s
e
a
s
e
d
e
te
c
ti
o
n
us
in
g
e
ns
e
mb
le
l
e
a
r
ni
ng,”
C
om
put
at
io
nal
I
nt
e
ll
ig
e
nc
e
and N
e
ur
o
s
c
ie
nc
e
, v
o
l.
2022, pp. 1
–
9, A
ug. 2022, do
i:
10.1155/2022/
5267498.
[
17]
K
.
P
ha
s
in
a
m,
T
.
M
o
nda
l,
D
.
N
ov
a
li
e
ndr
y
,
C
.
H
.
Y
a
ng,
C
.
D
ut
ta
,
a
nd
M
.
S
ha
ba
z
,
“
A
na
l
y
z
in
g
th
e
pe
r
f
o
r
ma
n
c
e
of
ma
c
hi
ne
le
a
r
ni
ng
t
e
c
hni
qu
e
s
in
di
s
e
a
s
e
pr
e
di
c
ti
o
n,”
J
our
nal
of
F
ood
Q
ual
it
y
,
vo
l.
2022,
pp.
1
–
9,
M
a
r
.
2022,
do
i:
10.1155/2022/
7529472.
[
18]
D
.
D
ha
r
me
ndr
a
a
nd
M
.
S
.
S
a
r
a
v
a
na
n,
“
P
r
e
di
c
ti
o
n
of
h
e
a
r
t
f
a
il
ur
e
us
in
g
s
uppo
r
t
v
e
c
t
or
ma
c
h
in
e
c
o
mpa
r
e
d
w
it
h
de
c
is
i
o
n
t
r
e
e
a
lg
o
r
it
h
m
f
o
r
b
e
tt
e
r
a
c
c
ur
a
c
y
,”
in
I
nt
e
r
nat
io
nal
C
onf
e
r
e
nc
e
on
Sus
ta
in
abl
e
C
om
put
in
g
and
D
at
a
C
om
m
uni
c
at
io
n
Sy
s
t
e
m
s
,
I
C
SC
D
S 2022
-
P
r
oc
e
e
di
ngs
, A
pr
. 2022, pp. 1535
–
1540, do
i:
1
0.1109/I
C
S
C
D
S
53736.2022.9760989.
[
19]
A
.
S
.
K
uma
r
a
nd
R
.
R
e
kha
,
“
A
de
ns
e
n
e
tw
o
r
k
a
ppr
o
a
c
h
w
it
h
ga
us
s
ia
n
o
pt
im
iz
e
r
f
o
r
c
a
r
di
ov
a
s
c
ul
a
r
di
s
e
a
s
e
pr
e
di
c
ti
o
n,”
N
e
w
G
e
ne
r
at
io
n C
om
put
in
g
, v
o
l.
41, n
o
. 4, pp. 859
–
878, N
ov
.
2023, do
i:
10.1007/s
00354
-
023
-
00234
-
1.
[
20]
D
. A
.
E
lm
in
a
a
m, M
. R
a
dw
a
n, N
. M
.
A
bd
e
lr
a
hma
n,
H
. W
.
K
a
m
a
l,
A
. K
.
A
.
E
le
w
a
, a
nd
A
. M
.
M
o
ha
m
e
d, “
M
L
H
e
a
r
tDi
s
P
r
e
di
c
ti
o
n:
he
a
r
t
di
s
e
a
s
e
pr
e
di
c
ti
o
n
us
in
g
ma
c
hi
ne
l
e
a
r
ni
ng,”
J
our
nal
o
f
C
om
put
in
g
and
C
om
m
uni
c
at
io
n
,
v
ol
.
2,
no
.
1,
pp.
50
–
65,
J
a
n.
2
023,
do
i:
10.21608/j
oc
c
.2023.282098.
[
21]
I
.
M
.
E
l
-
H
a
s
no
n
y
,
O
.
M
.
E
lz
e
k
i,
A
.
A
ls
he
hr
i,
a
nd
H
.
S
a
le
m,
“
M
ul
ti
-
la
be
l
a
c
ti
ve
l
e
a
r
ni
ng
-
ba
s
e
d
ma
c
hi
n
e
l
e
a
r
ni
ng
m
o
de
l
f
or
he
a
r
t
di
s
e
a
s
e
pr
e
di
c
ti
o
n,”
Se
ns
or
s
, v
o
l.
22, n
o
. 3, p. 1184, F
e
b. 2022,
do
i:
10.3390/s
22031184.
[
22]
P
.
G
u
le
r
ia
,
P
.
N
.
S
r
in
i
v
a
s
u,
S
.
A
hme
d,
N
.
A
lm
us
a
ll
a
m,
a
nd
F
.
K
.
A
la
r
f
a
j,
“
X
A
I
f
r
a
me
w
o
r
k
f
o
r
c
a
r
di
ov
a
s
c
ul
a
r
di
s
e
a
s
e
pr
e
di
c
ti
o
n
us
in
g
c
la
s
s
if
i
c
a
ti
o
n
t
e
c
hn
iq
ue
s
,”
E
le
c
tr
oni
c
s
(
Sw
it
z
e
r
la
nd)
,
vo
l.
11,
no
.
24,
p.
4086,
D
e
c
.
2
022,
do
i:
10.3390/
e
l
e
c
tr
o
ni
c
s
11244086.
[
23]
D
.
C
hi
c
c
o
a
nd
G
.
J
ur
ma
n,
“
M
a
c
hi
ne
l
e
a
r
ni
ng
c
a
n
pr
e
di
c
t
s
ur
v
i
v
a
l
of
pa
ti
e
n
ts
w
it
h
he
a
r
t
f
a
il
ur
e
f
r
om
s
e
r
um
c
r
e
a
ti
ni
n
e
a
nd
e
j
e
c
ti
o
n
f
r
a
c
t
i
o
n
a
l
o
n
e
,”
B
M
C
M
e
di
c
al
I
nf
or
m
at
ic
s
and
D
e
c
is
io
n
M
ak
in
g
,
vo
l.
20,
n
o
.
1,
p.
16,
2
020,
do
i:
10.1186/s
12911
-
020
-
1023
-
5.
[
24]
M
.
A
-
M
.
H
a
s
a
n,
J
.
S
hi
n,
U
.
D
a
s
,
a
nd
A
.
Y
.
S
r
i
z
o
n,
“
I
de
n
ti
f
y
in
g
pr
o
gn
o
s
ti
c
f
e
a
tu
r
e
s
f
or
pr
e
di
c
ti
ng
h
e
a
r
t
f
a
il
ur
e
b
y
u
s
in
g
ma
c
hi
ne
l
e
a
r
ni
ng
a
lg
o
r
it
hm,”
in
A
C
M
I
nt
e
r
nat
io
nal
C
on
f
e
r
e
nc
e
P
r
oc
e
e
di
ng
Se
r
ie
s
,
M
a
r
.
2021,
pp.
4
0
–
46,
do
i:
10.1145/3460238.3
4602
45.
[
25]
A
.
M
.
Q
a
dr
i,
M
.
S
.
A
.
H
a
s
hmi
,
A
.
R
a
z
a
,
S
.
A
.
J
.
Z
a
id
i
,
a
nd
A
.
ur
R
e
hma
n,
“
H
e
a
r
t
f
a
il
ur
e
s
ur
vi
v
a
l
pr
e
di
c
ti
o
n
us
in
g
nove
l
tr
a
ns
f
e
r
le
a
r
ni
ng
ba
s
e
d
pr
o
ba
bi
li
s
ti
c
f
e
a
tu
r
e
s
,”
P
e
e
r
J
C
om
put
e
r
S
c
ie
nc
e
,
v
o
l.
10,
pp.
1
–
30,
M
a
r
.
2
024,
do
i:
10.7717/p
e
e
r
j
-
c
s
.
1894.
Evaluation Warning : The document was created with Spire.PDF for Python.
I
n
t
J
I
n
f
&
C
o
m
m
u
n
T
e
c
hn
o
l
I
S
S
N:
2252
-
8776
E
nhanc
ing
pr
e
dictive
mode
ll
ing
and
int
e
r
pr
e
tabi
li
ty
in
he
ar
t
f
ail
ur
e
…
(
N
iaz
A
s
hr
af
K
han
)
19
B
I
OG
RA
P
HI
E
S
OF
AU
T
HO
RS
N
i
a
z
A
s
h
ra
f
K
h
a
n
i
s
a
d
ed
i
c
at
ed
a
c
a
d
em
i
c
p
ro
f
e
s
s
i
o
n
al
w
i
t
h
o
v
e
r
5
y
e
ars
o
f
e
x
p
e
ri
en
ce
i
n
t
h
e
f
i
e
l
d
o
f
C
o
m
p
u
t
e
r
S
c
i
en
ce
a
n
d
E
n
g
i
n
ee
ri
n
g
.
C
u
rr
e
n
t
l
y
s
e
rv
i
n
g
as
a
L
ec
t
u
r
e
r
at
t
h
e
D
e
p
art
me
n
t
o
f
C
o
m
p
u
t
e
r
S
ci
e
n
ce
a
n
d
E
n
g
i
n
ee
r
i
n
g
at
t
h
e
U
n
i
v
e
rs
i
t
y
o
f
L
i
b
e
ral
A
rt
s
Ban
g
l
a
d
e
s
h
,
N
i
az
h
o
l
d
s
a
Mas
t
e
r
’
s
d
e
g
r
ee
i
n
Co
m
p
u
t
e
r
Sc
i
en
ce
an
d
E
n
g
i
n
ee
ri
n
g
fr
o
m
N
o
rt
h
So
u
t
h
U
n
i
v
e
rs
i
t
y
,
D
h
a
k
a,
Ba
n
g
l
ad
e
s
h
,
al
o
n
g
w
i
t
h
a
b
ac
h
el
o
r
’
s
d
e
g
r
ee
fro
m
t
h
e
s
ame
i
n
s
t
i
t
u
t
i
o
n
.
h
i
s
r
e
s
e
ar
ch
i
n
t
e
r
e
s
t
s
l
i
e
i
n
t
h
e
i
n
t
e
rs
ec
t
i
o
n
o
f
s
o
u
n
d
s
i
g
n
al
p
ro
ce
s
s
i
n
g
,
N
L
P,
m
a
c
h
i
n
e
l
e
ar
n
i
n
g
,
an
d
d
ee
p
l
e
arn
i
n
g
.
H
e
c
an
b
e
co
n
t
a
c
t
ed
at
em
a
i
l
:
n
i
az.
as
h
raf
@
u
l
ab
.
ed
u
.
b
d
.
Md
.
F
erdo
u
s
Bi
n
Ha
f
i
z
i
s
c
u
rr
en
t
l
y
w
o
rk
i
n
g
an
A
s
s
i
s
t
an
t
Pro
fe
s
s
o
r
i
n
t
h
e
D
e
p
art
men
t
o
f
C
o
m
p
u
t
e
r
S
ci
e
n
ce
a
n
d
E
n
g
i
n
ee
ri
n
g
at
S
o
u
t
h
e
as
t
U
n
i
v
e
rs
i
t
y
.
H
e
co
m
p
l
e
t
e
d
h
i
s
BSc
.
i
n
Co
m
p
u
t
e
r
S
c
i
en
ce
an
d
In
fo
r
m
at
i
o
n
T
ech
n
o
l
o
g
y
fro
m
I
s
l
a
mi
c
U
n
i
v
e
rs
i
t
y
o
f
T
e
ch
n
o
l
o
g
y
(
I
U
T
),
Ban
g
l
a
d
e
s
h
,
a
n
d
o
b
t
ai
n
ed
a
Mas
t
e
r
o
f
I
n
f
o
r
m
at
i
o
n
T
ech
n
o
l
o
g
y
fro
m
Ch
arl
e
s
St
u
rt
U
n
i
v
e
rs
i
t
y
,
A
u
s
t
ral
i
a.
W
i
t
h
al
mo
s
t
6
y
ears
o
f
e
x
p
e
ri
en
ce
,
h
e
h
as
p
rev
i
o
u
s
l
y
w
o
rk
ed
at
t
h
e
U
n
i
v
e
rs
i
t
y
o
f
L
i
b
e
ral
A
rt
s
Ban
g
l
a
d
e
s
h
(U
L
A
B)
,
E
as
t
D
el
t
a
U
n
i
v
e
rs
i
t
y
,
an
d
N
o
rt
h
e
rn
U
n
i
v
e
rs
i
t
y
Ban
g
l
a
d
e
s
h
as
a
fu
l
l
-
t
i
me
l
ec
t
u
r
er
.
H
i
s
r
e
s
e
ar
ch
i
n
t
e
r
e
s
t
s
i
n
c
l
u
d
e
d
at
a
s
c
i
e
n
ce,
m
a
ch
i
n
e
l
e
arn
i
n
g
,
a
n
d
cy
b
e
r
s
ec
u
r
i
t
y
.
H
e
c
an
b
e
c
o
n
t
ac
t
e
d
at
fe
rd
o
u
s
.
b
i
n
h
af
i
z@
s
e
u
.
ed
u
.
b
d
.
M
d.
A
k
ta
ruzz
a
m
a
n
P
ra
m
a
n
i
k
i
s
cu
rr
e
n
t
l
y
w
o
rk
i
n
g
as
a
L
ec
t
u
r
e
r
at
t
h
e
D
e
p
art
men
t
o
f
Co
m
p
u
t
e
r
S
ci
e
n
ce
a
n
d
E
n
g
i
n
ee
r
i
n
g
at
t
h
e
U
n
i
v
e
rs
i
t
y
o
f
L
i
b
e
ra
l
A
rt
s
Ban
g
l
a
d
e
s
h
(U
L
A
B).
H
e
h
as
co
m
p
l
e
t
ed
h
i
s
Bac
h
el
o
rs
an
d
Mas
t
e
rs
fro
m
J
ah
a
n
g
i
rn
a
g
ar
U
n
i
v
e
rs
i
t
y
(J
U
).
B
e
f
o
re
j
o
i
n
i
n
g
U
L
A
B
,
h
e
w
o
rk
ed
as
a
So
ft
w
ar
e
E
n
g
i
n
ee
r
at
Fro
n
t
u
r
e
T
e
ch
n
o
l
o
g
i
e
s
L
t
d
.
fo
r
8
mo
n
t
h
s
.
Pr
e
v
i
o
u
s
l
y
h
e
w
o
r
k
ed
as
a
n
A
s
s
i
s
t
an
t
Pro
g
ra
mme
r
at
Ban
g
l
a
d
e
s
h
H
o
u
s
e
B
u
i
l
d
i
n
g
Fi
n
an
ce
C
o
rp
o
rat
i
o
n
(BH
B
FC)
fo
r
1
y
e
ar
an
d
3
mo
n
t
h
s
.
H
e
al
s
o
w
o
rk
ed
as
a
f
u
l
l
-
t
i
me
L
ec
t
u
r
e
r
f
o
r
1
y
e
ar
an
d
as
a
co
n
t
ra
c
t
u
al
L
ec
t
u
re
r
f
o
r
1
y
ear
a
n
d
3
mo
n
t
h
s
at
t
h
e
D
e
p
art
me
n
t
o
f
C
o
m
p
u
t
e
r
S
ci
e
n
ce
an
d
E
n
g
i
n
ee
ri
n
g
,
D
aff
o
d
i
l
I
n
t
e
rn
at
i
o
n
al
U
n
i
v
e
rs
i
t
y
.
H
e
c
an
b
e
co
n
t
a
c
t
ed
at
em
a
i
l
:
a
k
t
aru
zza
m
an
.
p
ra
m
an
i
k
@
u
l
ab
.
e
d
u
.
b
d
.
Evaluation Warning : The document was created with Spire.PDF for Python.