I
n
d
on
e
s
ian
Jou
r
n
al
o
f
E
lec
t
r
ica
l
E
n
gin
e
e
r
in
g
a
n
d
Com
p
u
t
e
r
S
c
ience
Vo
l
.
25
,
N
o
.
2
,
F
e
b
r
ua
r
y
20
22
,
pp.
1167
~
1176
I
S
S
N:
2502
-
4752,
DO
I
:
10
.
11591/i
j
e
e
c
s
.
v
25
.i
2
.
pp
1167
-
1176
1167
Jou
r
n
al
h
o
m
e
page
:
ht
tp:
//
ij
e
e
c
s
.
iaes
c
or
e
.
c
om
S
of
t
c
om
p
u
t
in
g t
e
c
h
n
i
q
u
e
s f
or
e
ar
l
y
d
ia
b
e
t
e
s p
r
e
d
ic
t
io
n
S
ab
ah
Anwe
r
Abd
u
l
k
a
r
e
e
m
1
,
Hu
s
s
ien
Yos
s
if
R
ad
h
i
1
,
Yous
r
a
Ahm
e
d
F
ad
il
2
,
Hu
s
s
ain
F
al
ih
M
a
h
d
i
1
1
D
e
pa
r
tm
e
nt
of
C
o
mpu
te
r
E
ngi
n
e
e
r
in
g, C
o
ll
e
g
e
of
E
ngi
n
e
e
r
in
g,
U
ni
ve
r
s
it
y
of
D
i
y
a
la
, D
i
y
a
la
, I
r
a
q
2
C
o
ll
e
g
e
of
L
a
w
a
nd P
o
l
it
i
c
a
l
S
c
i
e
n
c
e
s
Un
iv
e
r
s
it
y
of
D
i
y
a
la
, D
i
y
a
la
, I
r
a
q
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
J
u
l
31
,
2021
R
e
vi
s
e
d
No
v
9
,
2021
A
c
c
e
pt
e
d
De
c
1
,
2021
D
i
ab
e
t
e
s
m
el
l
i
t
u
s
i
s
a
c
h
r
o
n
i
c
,
l
i
f
e
-
t
h
r
e
at
en
i
n
g
,
an
d
c
o
m
p
l
i
c
at
e
d
co
n
d
i
t
i
o
n
.
A
ro
u
n
d
1
.
5
m
i
l
l
i
o
n
d
e
at
h
s
d
u
e
t
o
d
i
ab
e
t
e
s
h
av
e
b
ee
n
d
o
c
u
men
t
ed
,
a
cc
o
r
d
i
n
g
t
o
a
W
o
r
l
d
H
e
al
t
h
O
rg
a
n
i
zat
i
o
n
(
W
H
O
)
e
s
t
i
m
at
i
o
n
i
n
2
0
1
9
.
I
n
t
h
e
w
o
r
l
d
o
f
me
d
i
c
i
n
e
,
p
r
e
d
i
c
t
i
n
g
d
i
ab
e
t
e
s
ri
s
k
i
s
a
d
i
ffi
cu
l
t
an
d
t
i
me
-
c
o
n
s
u
m
i
n
g
t
as
k
.
Man
y
p
as
t
s
t
u
d
i
e
s
h
av
e
b
een
c
o
n
d
u
c
t
ed
t
o
i
n
v
e
s
t
i
g
at
e
an
d
c
l
ari
f
y
d
i
ab
e
t
e
s
s
y
m
p
t
o
m
s
an
d
v
ari
ab
l
e
s
.
T
o
s
o
l
v
e
t
h
e
s
e
p
e
rs
i
s
t
i
n
g
i
s
s
u
e
s
,
h
o
w
ev
e
r,
m
o
r
e
c
ri
t
i
c
a
l
cl
i
n
i
c
a
l
c
ri
t
e
ri
a
mu
s
t
b
e
c
o
n
s
i
d
e
r
ed
.
A
c
o
m
p
arat
i
v
e
an
a
l
y
s
i
s
b
as
e
d
o
n
t
h
ree
s
o
ft
c
o
m
p
u
t
i
n
g
s
t
rat
e
g
i
e
s
f
o
r
d
i
ab
e
t
e
s
p
r
e
d
i
c
t
i
o
n
h
as
b
e
en
c
arri
ed
o
u
t
an
d
a
c
h
i
e
v
ed
i
n
t
h
i
s
w
o
r
k
.
A
mo
n
g
t
h
e
co
m
p
u
t
at
i
o
n
a
l
i
n
t
e
l
l
i
g
e
n
ce
me
t
h
o
d
s
u
s
e
d
i
n
t
h
i
s
s
t
u
d
y
ar
e
fu
zz
y
an
a
l
y
t
i
c
a
l
h
i
e
rar
ch
y
p
ro
ce
s
s
e
s
(FA
H
P),
s
u
p
p
o
rt
v
ec
t
o
r
m
a
c
h
i
n
e
(S
V
M),
a
n
d
art
i
f
i
c
i
a
l
n
eu
ral
n
e
t
w
o
rk
s
(A
N
N
s
).
T
h
e
t
e
ch
n
i
q
u
e
s
rev
e
al
p
ro
m
i
s
i
n
g
p
e
rfo
r
m
a
n
c
e
i
n
p
r
e
d
i
c
t
i
n
g
d
i
ab
e
t
e
s
rel
i
ab
l
y
an
d
e
ff
ec
t
i
v
el
y
i
n
t
e
r
m
s
o
f
s
ev
e
ral
cl
as
s
i
fi
c
at
i
o
n
e
v
al
u
at
i
o
n
me
t
ri
c
s
,
a
cc
o
r
d
i
n
g
t
o
e
x
p
e
ri
men
t
al
a
n
al
y
s
i
s
an
d
as
s
e
s
s
me
n
t
co
n
d
u
c
t
e
d
o
n
5
2
0
p
art
i
c
i
p
an
t
s
u
s
i
n
g
a
p
u
b
l
i
cl
y
av
a
i
l
ab
l
e
d
at
as
e
t
.
K
e
y
w
o
r
d
s
:
A
r
t
i
f
i
c
i
a
l
n
e
ur
a
l
n
e
t
wo
r
ks
D
i
a
b
e
t
e
s
M
u
l
t
i
c
r
i
t
e
r
i
a
de
c
i
s
i
o
n
m
a
k
i
ng
f
uz
z
y
a
n
a
ly
t
i
c
a
l
hi
e
r
a
r
c
hy
pr
o
c
e
s
s
e
s
S
uppor
t
v
e
c
to
r
m
a
c
hi
ne
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
o
r:
S
a
b
a
h
An
we
r
Ab
du
l
ka
r
e
e
m
De
pa
r
t
m
e
n
t
o
f
C
o
m
put
e
r
E
n
g
i
n
e
e
r
i
n
g,
C
o
l
l
e
ge
o
f
E
n
g
i
ne
e
r
i
n
g
,
Uni
ve
r
s
i
t
y
o
f
D
i
y
a
l
a
D
i
y
a
l
a
,
I
r
a
q
E
m
a
i
l
:
s
bh
_a
nwa
r
@
uo
d
i
y
a
l
a
.
e
du.
i
q
1.
I
NT
RODU
C
T
I
ON
On
e
o
f
t
h
e
m
o
s
t
pr
e
v
a
l
e
n
t
e
n
do
c
r
i
n
e
d
i
s
e
a
s
e
s
i
s
d
i
a
b
e
t
e
s
m
e
ll
i
t
us
.
T
h
a
t
r
e
qui
r
e
s
o
n
go
i
n
g
m
e
d
i
c
a
l
c
a
r
e
w
i
t
h
s
e
ve
r
a
l
s
t
r
a
t
e
gi
e
s
t
o
r
e
duc
e
t
h
e
e
x
t
e
r
n
a
l
r
i
s
k
o
f
g
ly
c
e
mi
c
c
o
n
t
r
o
l
.
An
im
ba
l
a
n
c
e
o
c
c
ur
s
i
n
t
he
pe
r
s
o
n
’
s
n
ut
r
i
t
i
o
n
a
l
m
e
t
a
b
o
l
i
s
m
,
l
e
a
d
i
ng
t
o
m
a
ny
c
o
m
p
li
c
a
t
i
o
ns
a
n
d
l
o
n
g
-
t
e
r
m
e
f
f
e
c
t
s
,
i
nc
l
ud
i
n
g
t
h
e
he
a
r
t,
k
i
d
n
e
y
s
,
e
y
e
s
,
n
e
r
v
e
s
,
a
n
d
bl
o
o
d
v
e
s
s
e
l
s
.
T
o
d
i
a
g
n
o
s
e
s
ym
pt
o
m
a
t
i
c
d
i
a
b
e
t
e
s
by
do
c
to
r
s
,
t
h
e
pa
t
i
e
nt
s
h
o
ws
m
a
ny
s
i
g
n
s
a
n
d
s
ym
pt
o
m
s
r
e
s
u
l
t
i
ng
f
r
o
m
t
h
e
o
s
mot
i
c
s
e
pa
r
a
t
i
o
n
c
a
us
i
ng
hi
g
h
bl
o
o
d
s
uga
r
[
1]
.
T
h
e
d
i
s
e
a
s
e
s
o
f
d
i
a
b
e
t
e
s
c
o
m
p
l
i
c
a
t
i
o
ns
c
a
n
b
e
d
i
v
i
de
d
i
n
t
o
t
w
o
c
o
m
bi
na
t
i
o
n
s
a
c
c
o
r
d
i
n
g
to
t
h
e
i
r
da
m
a
ge
,
in
c
l
ud
i
ng
m
a
c
r
o
v
a
s
c
u
l
a
r
da
m
a
ge
(
t
h
e
a
r
t
e
r
i
e
s
)
a
n
d
mi
c
r
o
v
a
s
c
u
l
a
r
da
m
a
g
e
(
s
m
a
ll
bl
o
o
d
v
e
s
s
e
l
s
)
.
A
c
c
e
l
e
r
a
t
e
d
c
a
r
d
i
o
v
a
s
c
u
l
a
r
d
i
s
e
a
s
e
,
w
hi
c
h
pr
e
s
e
n
t
s
a
s
s
t
r
o
ke
s
a
n
d
o
t
h
e
r
c
a
t
a
s
t
r
o
phi
c
il
l
ne
s
s
e
s
,
i
s
t
he
m
o
s
t
de
va
s
t
a
t
i
n
g
m
a
c
r
o
v
a
s
c
u
l
a
r
c
o
n
s
e
que
nc
e
.
M
i
c
r
o
v
a
s
c
u
l
a
r
il
l
ne
s
s
e
s
s
uc
h
a
s
r
e
t
i
n
o
pa
t
hy
i
n
t
h
e
e
y
e
,
n
e
p
h
r
o
pa
t
h
y
i
n
t
h
e
k
i
d
n
e
y
,
a
n
d
n
e
ur
o
pa
t
hy
i
n
t
h
e
n
e
r
v
o
us
s
y
s
t
e
m
a
r
e
e
x
a
m
p
l
e
s
o
f
o
r
ga
n
-
s
pe
c
i
f
i
c
d
i
s
o
r
de
r
s
[
2]
,
[
3
]
.
A
c
c
o
r
d
i
n
g
t
o
t
h
e
W
o
r
l
d
He
a
l
t
h
Or
ga
ni
z
a
t
i
o
n
(
W
H
O)
,
di
a
b
e
t
e
s
a
f
f
e
c
t
s
422
m
il
l
i
o
n
p
e
o
pl
e
wo
r
l
dw
i
de
i
n
2018
(
W
HO
)
.
T
y
pe
1
a
n
d
t
y
pe
2
d
i
a
be
t
e
s
a
r
e
t
h
e
t
w
o
f
o
r
m
s
o
f
d
i
a
b
e
t
e
s
.
T
y
pe
1
d
i
a
b
e
t
e
s
m
e
l
li
t
us
i
s
a
n
a
uto
i
mm
u
n
e
d
i
s
e
a
s
e
t
h
a
t
c
a
n
s
t
r
i
k
e
a
ny
o
ne
a
t
a
ny
a
ge
,
b
ut
i
t
s
t
r
i
ke
s
c
hil
dr
e
n
a
n
d
a
do
l
e
s
c
e
n
t
s
m
o
r
e
f
r
e
que
n
t
l
y
.
T
h
e
im
m
u
n
e
s
y
s
t
e
m
i
nc
o
r
r
e
c
t
l
y
de
s
t
r
o
y
s
pa
nc
r
e
a
t
i
c
b
e
t
a
c
e
ll
s
,
r
e
s
u
l
t
i
ng
i
n
t
ot
a
l
i
ns
u
li
n
i
ns
u
f
f
i
c
i
e
n
c
y
,
a
s
m
a
l
l
a
m
o
un
t
o
f
i
ns
u
l
i
n
r
e
l
e
a
s
e
d
i
n
t
o
t
h
e
b
o
d
y
,
o
r
e
v
e
n
n
o
i
ns
u
li
n
r
e
l
e
a
s
e
d
i
n
t
o
t
h
e
b
o
d
y
.
M
e
ll
i
t
us
i
s
a
T
y
pe
2
d
i
a
b
e
t
e
s
c
o
m
p
l
i
c
a
t
i
o
n
t
h
a
t
a
r
i
s
e
s
whe
n
t
h
e
b
o
d
y
l
o
s
e
s
b
-
c
e
ll
i
ns
u
l
i
n
s
e
c
r
e
t
i
o
n
o
v
e
r
t
i
m
e
,
ge
n
e
r
a
t
e
s
i
n
s
u
f
f
i
c
i
e
n
t
i
ns
u
li
n,
o
r
s
t
a
y
s
i
ns
u
l
i
n
-
r
e
s
i
s
t
a
n
t
.
Ge
s
t
a
t
i
o
n
a
l
d
i
a
be
t
e
s
,
o
n
t
h
e
ot
h
e
r
ha
n
d,
i
s
c
a
us
e
d
by
h
o
r
m
o
na
l
c
h
a
n
ge
s
t
h
a
t
o
nl
y
o
c
c
ur
dur
i
n
g
pr
e
g
n
a
n
c
y
.
T
y
pe
1
d
i
a
b
e
t
e
s
,
T
y
pe
2
d
i
a
b
e
t
e
s
,
ge
s
t
a
t
i
o
n
a
l
d
i
a
b
e
t
e
s
,
a
n
d
ot
h
e
r
t
y
pe
s
o
f
Evaluation Warning : The document was created with Spire.PDF for Python.
I
S
S
N
:
2502
-
4752
I
n
do
n
e
s
i
a
n
J
E
l
e
c
E
n
g
&
C
o
m
p
S
c
i
,
Vo
l
.
25
,
N
o
.
2
,
F
e
b
r
ua
r
y
20
22
:
1167
-
1176
1168
d
i
a
b
e
t
e
s
c
a
us
e
d
by
va
r
i
o
us
f
a
c
t
o
r
s
a
r
e
c
l
a
s
s
if
i
e
d
by
s
o
m
e
r
e
s
e
a
r
c
h
e
r
s
a
n
d
t
h
e
Am
e
r
i
c
a
n
d
i
a
b
e
t
e
s
a
s
s
o
c
i
a
t
i
o
n
(
A
D
A
)
[
4]
.
G
e
s
t
a
t
i
o
n
a
l
d
i
a
be
t
e
s
m
e
ll
i
t
us
(
GD
M
)
i
s
a
k
i
nd
o
f
d
i
a
be
t
e
s
t
h
a
t
o
nl
y
a
f
f
e
c
t
s
pr
e
gn
a
n
t
wo
m
e
n
a
n
d
us
ua
l
ly
de
v
e
l
o
ps
b
e
t
we
e
n
t
he
24t
h
a
n
d
28t
h
we
e
k
o
f
pr
e
g
n
a
n
c
y
(
e
x
c
e
pt
f
o
r
wo
m
e
n
w
h
o
a
l
r
e
a
d
y
ha
v
e
c
h
r
o
ni
c
d
i
a
b
e
t
e
s
)
.
W
h
e
n
bl
o
o
d
g
l
uc
o
s
e
l
e
v
e
l
s
r
i
s
e
o
v
e
r
no
r
m
a
l
dur
i
ng
pr
e
g
n
a
n
c
y
,
i
t
i
s
de
t
e
c
t
e
d.
Af
t
e
r
t
h
e
ba
by
is
b
o
r
n
,
t
h
e
m
a
j
o
r
i
t
y
o
f
m
o
t
h
e
r
s
w
il
l
n
o
t
ge
t
di
a
b
e
t
e
s
.
Af
t
e
r
c
hil
d
bi
r
t
h
,
h
o
we
v
e
r
,
s
o
m
e
wo
m
e
n
w
i
ll
c
o
n
t
i
n
ue
t
o
h
a
v
e
hi
g
h
bl
o
o
d
g
l
uc
o
s
e
l
e
v
e
l
s
[
5]
.
A
c
c
o
r
d
i
n
g
t
o
d
i
a
b
e
t
e
s
A
us
t
r
a
l
i
a
,
d
i
a
b
e
t
e
s
c
a
n
be
pr
e
s
e
n
t
f
o
r
up
to
s
e
ve
n
y
e
a
r
s
b
e
f
o
r
e
c
li
n
i
c
a
l
d
i
a
g
n
o
s
i
s
.
Dur
i
ng
t
hi
s
t
i
m
e
,
a
p
e
r
s
o
n
m
a
y
a
c
qu
i
r
e
po
t
e
n
t
i
a
l
ly
f
a
t
a
l
c
o
n
d
i
t
i
o
ns
s
u
c
h
a
s
bli
nd
n
e
s
s
f
r
o
m
e
ye
da
m
a
ge
,
f
o
o
t
ul
c
e
r
s
t
h
a
t
m
a
y
ne
e
d
a
m
put
a
t
i
o
n
o
f
t
h
e
a
f
f
e
c
t
e
d
l
im
bs
,
r
e
na
l
f
a
i
l
ur
e
,
a
n
d
h
e
a
r
t
a
tt
a
c
ks
[
6]
–
[
10]
.
F
i
a
r
ni
a
n
d
o
t
h
e
r
s
c
o
i
n
e
d
t
h
e
t
e
r
m
"
s
il
e
n
t
k
i
ll
e
r
"
t
o
de
s
c
r
i
b
e
i
t
f
o
r
t
h
e
s
e
r
e
a
s
o
ns
[
3]
.
W
i
t
h
r
e
gu
l
a
r
e
x
a
mi
na
t
i
o
ns
,
e
a
r
l
y
de
t
e
c
t
i
o
n
,
a
n
d
t
r
e
a
t
m
e
n
t
i
ni
t
iat
i
o
n
,
t
h
e
s
e
r
e
pe
r
c
us
s
i
o
n
s
c
a
n
b
e
pr
e
v
e
n
t
e
d,
m
a
na
ge
d,
o
r
e
v
e
n
e
li
mi
na
t
e
d
i
n
s
o
m
e
pe
r
s
o
ns
,
s
a
vi
ng
r
o
ughl
y
1
415
US
d
o
l
l
a
r
s
[
5]
.
A
r
t
i
f
i
c
i
a
l
i
n
t
e
ll
i
ge
n
c
e
t
e
c
hn
o
l
o
g
i
e
s
a
r
e
r
o
u
t
i
ne
ly
e
m
p
l
o
y
e
d
to
de
t
e
c
t
a
n
d
d
i
a
g
n
o
s
e
d
i
s
e
a
s
e
s
a
uto
m
a
t
i
c
a
ll
y
.
T
h
e
a
ut
h
o
r
s
i
n
[
11]
r
e
c
o
m
m
e
n
de
d
t
h
e
us
e
o
f
a
s
u
b
s
e
t
e
v
a
l
ua
t
o
r
(
C
S
E
)
a
s
a
m
e
t
h
o
d
f
o
r
i
de
n
t
i
f
y
i
ng
t
h
e
m
o
s
t
i
m
po
r
t
a
n
t
r
i
s
k
v
a
r
i
a
bl
e
s
f
o
r
di
a
b
e
t
e
s
pr
e
v
a
l
e
n
c
e
i
n
t
h
e
b
o
d
y
.
B
a
s
e
d
o
n
t
h
e
P
i
ma
I
n
d
i
a
n
D
i
a
b
e
t
e
s
da
t
a
s
e
t
,
t
h
e
a
ut
h
o
r
s
c
o
m
bi
ne
d
C
S
E
a
n
d
de
c
i
s
i
o
n
t
r
e
e
(
DT
)
to
c
r
e
a
t
e
a
c
l
a
s
s
i
f
i
e
r
s
u
bs
e
t
e
v
a
l
ua
t
o
r
de
c
i
s
i
o
n
t
r
e
e
(
C
S
E
-
DT
)
(
P
I
DD
)
.
M
or
e
o
v
e
r
,
S
h
u
ja
e
t
al
.
[
12]
de
v
e
l
o
pe
d
a
t
wo
-
s
t
a
ge
a
ppr
o
a
c
h
f
o
r
d
i
a
b
e
t
i
c
pr
e
d
i
c
t
i
o
n
b
a
s
e
d
o
n
da
t
a
m
i
ni
ng
c
a
t
e
go
r
i
z
a
t
i
o
n
t
e
c
h
ni
que
s
:
F
o
r
da
t
a
pr
e
pr
o
c
e
s
s
i
n
g,
t
h
e
i
ni
t
i
a
l
s
t
a
ge
i
s
s
y
n
t
he
t
i
c
m
i
n
o
r
i
t
y
o
v
e
r
s
a
m
p
li
ng
t
e
c
hni
que
(
S
M
OT
E
)
.
F
i
v
e
m
a
c
hi
ne
l
e
a
r
ni
ng
c
l
a
s
s
i
f
i
e
r
s
a
r
e
u
s
e
d
i
n
t
h
e
s
e
c
o
n
d
s
t
a
ge
:
s
i
m
p
l
e
l
o
g
i
s
t
i
c
,
de
c
i
s
i
o
n
t
r
e
e
,
b
a
ggi
ng,
a
r
t
i
f
i
c
i
a
l
n
e
ur
a
l
n
e
t
wor
ks
(
A
NN
)
,
a
n
d
s
uppor
t
v
e
c
to
r
m
a
c
hi
ne
(
S
VM
)
.
S
wa
pna
e
t
al
.
[
13]
a
m
e
t
h
o
d
f
o
r
d
i
s
t
i
n
gu
i
s
hi
ng
n
o
r
m
a
l
he
a
r
t
r
a
t
e
v
a
r
i
a
bil
i
t
y
(
HR
V
)
s
i
g
n
a
l
s
a
n
d
d
i
a
b
e
t
e
s
wa
s
pr
o
po
s
e
d
u
t
i
li
z
i
ng
de
e
p
l
e
a
r
ni
n
g
a
r
c
hi
t
e
c
t
ur
e
s
.
T
o
c
o
n
s
t
r
uc
t
a
n
i
n
t
e
gr
a
t
e
d
s
y
s
t
e
m
,
t
h
e
a
ut
h
o
r
s
m
e
r
ge
d
a
c
o
n
v
o
l
ut
i
o
n
a
l
n
e
ur
a
l
n
e
t
wo
r
k
(
C
NN
)
wi
t
h
l
o
n
g
s
h
o
r
t
-
t
e
r
m
m
e
m
o
r
y
(
L
S
T
M
)
.
M
u
l
t
i
p
l
e
c
r
i
t
e
r
i
a
de
c
i
s
i
o
n
m
a
k
i
ng
(
M
C
DM
)
,
a
s
u
b
-
f
i
e
l
d
a
n
d
ke
y
b
r
a
n
c
h
o
f
o
pe
r
a
t
i
o
n
s
r
e
s
e
a
r
c
h
(
OR
)
h
a
v
e
b
e
e
n
w
i
de
ly
e
m
p
l
o
y
e
d
i
n
m
a
ny
s
c
i
e
n
t
i
f
i
c
do
m
a
i
ns
f
o
r
pr
o
ble
m
-
s
o
l
vi
ng
a
n
d
de
c
i
s
i
o
n
-
m
a
k
i
ng
i
n
a
dd
i
t
i
o
n
t
o
a
r
t
i
f
i
c
i
a
l
i
n
t
e
ll
i
ge
n
c
e
a
l
go
r
i
t
hm
s
[
14]
,
[
15]
.
T
h
e
M
C
D
A
o
r
M
C
DM
pr
o
bl
e
m
n
o
r
m
a
ll
y
c
o
m
pr
i
s
e
s
f
o
ur
ph
a
s
e
s
:
f
o
r
m
u
l
a
t
i
o
n
o
f
o
pt
i
o
n
s
,
c
r
i
t
e
r
i
o
n
s
e
l
e
c
t
i
o
n
,
c
r
i
t
e
r
i
o
n
we
i
g
h
t
i
n
g,
a
n
d
de
c
i
s
i
o
n
m
a
k
i
ng
[
16]
.
B
y
m
e
r
g
i
ng
M
C
DM
a
ppr
o
a
c
h
e
s
w
i
t
h
a
r
t
i
f
i
c
i
a
l
i
n
t
e
ll
i
g
e
n
c
e
t
e
c
hni
que
s
,
pa
r
t
i
c
u
l
a
r
ly
s
o
f
t
c
o
m
put
i
n
g
t
e
c
hn
o
l
o
g
i
e
s
,
hy
b
r
id
m
e
t
h
o
do
l
o
g
i
e
s
c
a
n
b
e
c
o
ns
t
r
uc
t
e
d
.
T
h
e
f
uz
z
y
a
na
ly
t
i
c
h
i
e
r
a
r
c
hy
pr
o
c
e
s
s
(
F
AH
P
)
i
s
a
m
u
l
t
i
-
c
r
i
t
e
r
i
o
n
de
c
i
s
i
o
n
-
m
a
k
i
ng
t
e
c
hni
que
a
n
a
ly
t
i
c
hi
e
r
a
r
c
hy
pr
o
c
e
s
s
(
A
HP)
t
h
a
t
i
n
c
o
r
por
a
t
e
s
f
uz
z
y
t
h
e
o
r
i
e
s
(
a
b
r
a
nc
h
o
f
s
o
f
t
c
o
m
put
i
n
g)
[
17]
.
I
n
t
h
e
li
t
e
r
a
t
ur
e
,
t
h
e
r
e
h
a
ve
b
e
e
n
m
a
n
y
m
e
t
ho
ds
de
v
e
l
o
pe
d
f
o
r
d
i
a
b
e
t
e
s
pr
e
d
i
c
t
i
o
n
.
l
o
g
i
s
t
i
c
r
e
gr
e
s
s
i
o
n
i
s
o
n
e
o
f
t
h
e
c
l
a
s
s
if
i
c
a
t
i
o
n
t
e
c
hni
que
s
us
e
d
t
o
p
r
e
di
c
t
di
a
b
e
t
e
s
[
18]
.
T
h
e
a
ut
h
o
r
s
e
m
p
l
o
ye
d
s
e
v
e
n
f
a
c
t
o
r
s
a
s
a
t
tr
i
b
ut
e
s
i
n
t
he
i
r
da
t
a
a
n
a
ly
s
i
s
,
r
e
s
u
l
t
i
n
g
i
n
a
pr
e
d
i
c
t
i
o
n
pr
o
b
a
bil
i
t
y
o
f
78.
5565.
An
o
t
h
e
r
wo
r
k,
M
o
r
ga
n
e
t
al
.
[
19]
us
e
d
t
h
e
w
o
r
l
d
h
e
a
l
t
h
s
ur
v
e
y
p
l
u
s
(
W
HS+)
,
whi
c
h
wa
s
pe
r
f
o
r
m
e
d
w
i
t
h
W
HO
s
uppo
r
t
a
c
r
o
s
s
f
i
ve
Gu
l
f
c
o
o
pe
r
a
t
i
o
n
c
o
un
c
il
(
G
C
C
)
n
a
t
i
o
n
s
i
n
2008
a
n
d
2009,
i
nc
l
ud
i
ng
t
h
e
U
A
E
,
K
uw
a
i
t
,
S
a
ud
i
A
r
a
bi
a
,
a
n
d
O
m
a
n
.
T
h
e
s
a
m
p
l
e
s
i
z
e
s
f
o
r
t
h
e
a
ut
h
o
r
s
we
r
e
UA
E
(
n
=
2569)
,
K
uwa
i
t
(
n
=
3828)
,
S
a
udi
A
r
a
bi
a
(
n
=
8629)
,
a
n
d
O
m
a
n
(
n=
4717)
.
A
c
c
o
r
d
i
n
g
t
o
t
h
e
f
i
nd
i
n
g
s
,
O
m
a
n
h
a
s
t
h
e
l
o
we
s
t
s
t
a
n
da
r
d
i
z
e
d
pr
e
v
a
l
e
n
c
e
o
f
d
i
a
b
e
t
e
s
a
t
8.
5
%
(
7
.
4
%
–
9.
8
%
)
,
f
o
l
l
o
we
d
by
S
a
ud
i
A
r
a
bi
a
a
t
10.
5
%
(
9.
6
%
–
11.
4
%
)
,
t
h
e
Uni
t
e
d
A
r
a
b
E
m
i
r
a
t
e
s
a
t
13.
2
%
(
11
.
4
%
–
15.
2
%
)
,
a
n
d
K
uwa
i
t
a
t
15.
3
%
(
1
3.
9
%
–
16.
8
%
)
.
S
i
n
g
h
e
t
al
.
[
20]
T
h
e
P
i
m
a
I
n
d
i
a
ns
d
i
a
b
e
t
e
s
da
t
a
s
e
t
wa
s
a
l
s
o
us
e
d
t
o
de
v
e
l
o
p
m
a
c
hi
ne
l
e
a
r
ni
n
g
a
ppr
o
a
c
h
e
s
f
o
r
d
i
a
be
t
e
s
d
i
a
g
n
o
s
i
s
,
i
nc
l
ud
i
ng
li
k
e
l
i
h
o
o
d
-
b
a
s
e
d
n
a
v
i
e
b
a
y
e
s
(
NB
)
,
de
c
i
s
i
o
n
t
r
e
e
-
b
a
s
e
d
r
a
n
d
o
m
f
o
r
e
s
t
(
R
F
)
,
a
n
d
m
u
l
t
i
-
l
a
y
e
r
e
d
f
u
n
c
t
i
o
n
-
b
a
s
e
d
r
a
n
do
m
f
o
r
e
s
t
(
R
F
)
(
M
L
P
)
.
T
h
e
y
de
m
o
ns
t
r
a
t
e
d
t
h
a
t
da
t
a
pr
e
-
pr
o
c
e
s
s
i
n
g
c
a
n
i
m
pr
o
v
e
t
h
e
pe
r
f
o
r
m
a
n
c
e
o
f
m
a
c
hi
ne
l
e
a
r
ni
ng
a
l
go
r
i
t
hm
s
.
V
i
d
hy
a
a
n
d
S
ha
nm
ug
a
l
a
ks
hmi
[
21]
c
o
n
s
i
de
r
e
d
m
a
ny
r
i
s
k
f
a
c
t
or
s
to
pr
e
di
c
t
di
a
be
t
e
s
m
e
ll
i
t
us
i
nc
l
ud
i
ng,
t
h
e
pa
t
i
e
n
t
s
’
b
o
d
y
m
a
s
s
i
nde
x
l
e
v
e
l
,
b
a
d
e
a
t
i
n
g
h
a
bi
t
s
,
poo
r
e
x
e
r
c
i
s
e
,
s
m
o
k
i
n
g,
n
a
t
ur
e
o
f
w
o
r
k
,
a
n
d
ot
h
e
r
f
a
c
t
or
s
r
e
ga
r
d
l
e
s
s
o
f
t
h
e
a
ge
o
r
ge
n
de
r
o
f
t
h
e
p
a
t
i
e
n
t
.
T
h
e
y
f
o
u
n
d
t
h
e
e
xi
s
t
i
n
g
t
e
c
hni
que
s
,
A
NN
a
n
d
S
VM
,
a
c
hi
e
v
e
d
a
n
a
c
c
ur
a
c
y
o
f
57.
41%
a
n
d
62.
81
%
.
I
n
c
o
m
pa
r
i
s
o
n
,
de
e
p
b
e
l
i
e
f
ne
t
wo
r
k
(
DB
N
)
a
c
hi
e
v
e
d
a
n
a
c
c
ur
a
c
y
o
f
80.
99%
,
a
c
hi
e
vi
ng
b
e
t
t
e
r
r
e
s
ul
t
s
t
h
a
n
t
he
o
t
h
e
r
m
a
c
hi
ne
l
e
a
r
ni
ng
m
e
t
h
o
ds
.
D
i
ng
e
t
al
.
[
22]
pr
o
p
o
s
e
d
a
n
o
v
e
l
a
ppr
o
a
c
h
f
o
r
pr
e
di
c
t
i
n
g
d
i
a
b
e
t
i
c
c
o
m
p
li
c
a
t
i
o
n
s
b
a
s
e
d
o
n
t
h
e
s
i
mi
l
a
r
i
t
y
e
nh
a
n
c
e
d
l
a
t
e
n
t
D
i
r
i
c
hl
e
t
a
s
s
i
g
nm
e
n
t
(
s
e
L
D
A
)
m
o
de
l
.
Af
t
e
r
pr
e
pr
o
c
e
s
s
i
n
g
t
h
e
da
t
a
,
t
h
e
y
c
o
m
put
e
d
t
h
e
s
im
il
a
r
i
t
y
b
e
t
we
e
n
e
a
c
h
pa
i
r
b
a
s
e
d
o
n
m
e
d
i
c
a
l
r
e
c
o
r
ds
,
t
h
e
n
us
e
d
t
h
e
s
i
mi
l
a
r
i
t
y
e
s
t
i
m
a
t
e
s
a
s
c
o
ns
t
r
a
i
n
t
s
i
n
s
e
L
D
A
-
ba
s
e
d
d
i
a
b
e
t
e
s
c
o
m
p
li
c
a
t
i
o
n
s
mi
n
i
ng.
T
h
e
pr
o
p
o
s
e
d
a
ppr
o
a
c
h
(
S
VM
-
s
e
L
D
A
)
c
o
n
s
i
s
t
e
n
t
l
y
b
e
a
t
t
h
e
tr
a
di
t
i
o
n
a
l
a
n
d
s
e
L
D
A
-
b
a
s
e
d
a
ppr
o
a
c
h
e
s
by
22.
49
%
i
n
e
s
t
i
m
a
t
i
n
g
s
im
il
a
r
i
t
y
a
n
d
pr
e
d
i
c
t
i
n
g
d
i
a
b
e
t
i
c
c
o
m
p
li
c
a
t
i
o
ns
,
a
c
c
o
r
di
n
g
t
o
t
h
e
e
x
pe
r
im
e
n
t
a
l
da
t
a
.
L
i
u
e
t
al.
[
23
]
t
h
e
d
i
s
e
a
s
e
t
y
pe
2
d
i
a
b
e
t
e
s
m
e
ll
i
t
us
(
T
2DM
)
wa
s
us
e
d
a
s
a
c
a
s
e
s
t
ud
y
,
w
i
t
h
t
h
e
f
o
c
us
o
n
i
s
s
ue
s
t
h
a
t
o
c
c
ur
a
f
t
e
r
t
h
e
f
i
r
s
t
d
i
a
g
no
s
i
s
.
M
o
de
l
i
ng
s
o
m
e
r
i
s
k
c
o
m
p
li
c
a
t
i
o
n
s
a
n
d
a
n
a
lyz
i
n
g
t
h
e
li
nka
ge
s
b
e
t
we
e
n
r
i
s
k
f
a
c
t
o
r
s
e
l
e
c
t
i
o
n
pa
t
t
e
r
n
s
a
r
e
t
h
e
f
o
un
d
a
t
i
o
ns
o
f
t
h
e
i
r
s
t
r
a
t
e
gy
.
T
h
e
y
c
o
nc
l
ud
e
d
t
h
a
t
t
h
e
B
a
y
e
s
i
a
n
hi
e
r
a
r
c
hi
c
a
l
f
r
a
m
e
wo
r
k
o
u
t
pe
r
f
o
r
m
e
d
t
h
e
m
o
s
t
r
e
c
e
n
t
m
o
de
l
s
.
Evaluation Warning : The document was created with Spire.PDF for Python.
I
n
do
n
e
s
i
a
n
J
E
l
e
c
E
n
g
&
C
o
m
p
S
c
i
I
S
S
N:
2502
-
4752
Sof
t
c
omputing
tec
hniques
f
or
e
ar
ly
diabete
s
pr
e
diction
(
Sabah
A
n
w
e
r
A
bdulkar
e
e
m
)
1169
F
A
HP
ha
s
be
e
n
u
s
e
d
i
n
m
a
ny
a
pp
l
i
c
a
t
i
o
n
s
a
n
d
d
i
f
f
e
r
e
n
t
f
i
e
l
ds
,
s
uc
h
a
s
we
a
po
n
s
e
l
e
c
t
i
o
n
[
24]
,
pe
r
s
o
n
n
e
l
s
e
l
e
c
t
i
o
n
[
25]
,
j
o
b
s
e
l
e
c
t
i
o
n
[
26]
,
e
n
e
r
g
y
a
l
t
e
r
n
a
t
i
v
e
s
s
e
l
e
c
t
i
o
n
[
27]
,
a
n
d
pe
r
f
o
r
m
a
nc
e
e
v
a
l
ua
t
i
o
n
s
y
s
t
e
m
s
[
28]
,
[
29]
.
C
h
a
m
o
dr
a
ka
s
e
t
a
l.
[
30]
us
e
d
a
f
uz
z
y
A
HP
m
e
t
h
o
d
f
o
r
s
upp
l
i
e
r
s
e
l
e
c
t
i
o
n
i
n
e
lec
t
r
o
ni
c
m
a
r
ke
t
p
l
a
c
e
s
.
S
i
mi
l
a
r
l
y
,
Kili
nc
c
i
e
t
al
.
[
31]
us
e
d
th
e
f
uz
z
y
A
HP
a
ppr
o
a
c
h
i
n
t
h
e
wa
s
hi
ng
m
a
c
hi
ne
c
o
m
pa
ny
f
o
r
s
e
l
e
c
t
i
o
n
pur
po
s
e
s
.
S
h
a
w
e
t
al
.
[
32]
pr
o
p
o
s
e
d
c
o
m
bi
n
i
ng
f
u
z
z
y
AH
P
a
n
d
f
u
z
z
y
o
bj
e
c
t
i
v
e
li
ne
a
r
pr
o
g
r
a
m
mi
ng
t
o
b
e
tt
e
r
s
e
l
e
c
t
a
s
upp
l
i
e
r
f
o
r
de
v
e
l
o
p
i
n
g
a
l
o
w
-
c
a
r
b
o
n
s
upp
ly
c
h
a
i
n.
F
ur
t
h
e
r
m
o
r
e
,
A
r
i
ka
n
[
33]
to
h
a
n
d
l
e
m
u
l
t
i
p
l
e
o
bj
e
c
t
i
v
e
s
upp
l
i
e
r
s
e
l
e
c
t
i
o
n
c
h
a
ll
e
n
ge
s
,
we
c
r
e
a
t
e
d
a
n
i
n
t
e
r
a
c
t
i
v
e
s
o
l
ut
i
o
n
u
s
in
g
F
uz
z
y
A
HP.
T
h
e
i
r
pr
o
p
o
s
e
d
s
t
r
a
t
e
gy
h
a
d
t
h
r
e
e
o
bj
e
c
t
i
ve
s
:
r
e
duc
e
tot
a
l
f
i
na
nc
i
a
l
c
o
s
t
s
,
i
m
pr
o
v
e
o
v
e
r
a
l
l
qu
a
l
i
t
y
,
a
n
d
im
pr
o
v
e
c
us
t
o
m
e
r
s
e
r
vi
c
e
.
Da
t
a
m
i
n
i
ng
t
e
c
hni
qu
e
s
w
e
r
e
us
e
d
i
n
t
h
e
m
o
s
t
r
e
c
e
n
t
l
y
e
s
t
a
bli
s
h
e
d
d
i
a
b
e
t
e
s
pr
e
d
i
c
t
i
o
n
a
l
go
r
i
t
hm
s
[
34]
.
I
s
l
a
m
e
t
al.
[
34]
a
da
t
a
s
e
t
o
f
520
o
c
c
ur
r
e
n
c
e
s
wa
s
e
x
a
mi
ne
d
us
i
ng
t
h
r
e
e
m
a
c
hi
ne
l
e
a
r
ni
ng
t
e
c
hni
que
s
:
t
h
e
Na
i
v
e
B
a
y
e
s
m
e
t
h
o
d,
t
h
e
l
o
g
i
s
t
i
c
r
e
gr
e
s
s
i
o
n
a
l
go
r
i
t
hm
,
a
n
d
t
h
e
r
a
n
do
m
f
o
r
e
s
t
a
l
go
r
i
t
hm
.
R
a
n
do
m
f
o
r
e
s
t
a
l
go
r
i
t
hm
s
pr
o
duc
e
d
t
h
e
m
o
s
t
a
c
c
ur
a
t
e
r
e
s
ul
t
s
,
a
c
c
o
r
di
n
g
to
t
h
e
i
r
r
e
s
e
a
r
c
h
.
T
o
pr
e
di
c
t
d
i
a
b
e
t
e
s
Ga
r
c
í
a
-
Or
dá
s
e
t
al.
[
35]
us
e
d
s
t
a
t
e
-
of
-
t
h
e
-
a
r
t
de
e
p
l
e
a
r
ni
ng
a
ppr
o
a
c
h
e
s
.
O
n
t
h
e
P
im
a
I
n
d
i
a
ns
d
i
a
b
e
t
e
s
da
t
a
s
e
t,
t
h
e
y
u
s
e
d
a
v
a
r
i
a
t
i
o
n
a
l
a
ut
o
e
n
c
o
de
r
(
VA
E
)
to
i
n
c
r
e
a
s
e
da
t
a
,
a
s
pa
r
s
e
a
ut
o
e
n
c
o
de
r
(
S
A
E
)
to
i
n
c
r
e
a
s
e
f
e
a
t
ur
e
s
,
a
n
d
a
C
NN
f
o
r
c
l
a
s
s
i
f
i
c
a
t
i
o
n
,
us
i
ng
a
v
a
r
i
a
t
i
o
na
l
a
uto
e
n
c
o
de
r
(
VA
E
)
to
i
n
c
r
e
a
s
e
da
t
a
,
a
s
pa
r
s
e
a
uto
e
n
c
o
de
r
(
S
A
E
)
to
i
n
c
r
e
a
s
e
f
e
a
t
ur
e
s
,
a
n
d
a
C
NN
to
i
n
c
r
e
a
s
e
c
l
a
s
s
if
i
c
a
t
i
o
n
.
De
s
p
i
t
e
t
h
e
r
e
s
e
a
r
c
he
r
s
'
b
e
s
t
e
f
f
o
r
t
s
,
m
o
r
e
r
e
s
e
a
r
c
h
a
n
d
c
o
m
pa
r
i
s
o
n
o
f
d
i
a
b
e
t
e
s
pr
e
d
i
c
t
i
o
n
a
ppr
o
a
c
h
e
s
a
r
e
s
t
i
ll
n
e
e
de
d
a
n
d
o
pe
n
f
o
r
i
nv
e
s
t
i
g
a
t
i
o
n
.
F
o
r
e
a
r
l
y
d
i
a
b
e
t
e
s
pr
e
d
i
c
t
i
o
n
,
t
h
i
s
pa
pe
r
pr
e
s
e
n
t
s
a
c
o
m
pa
r
a
t
i
v
e
s
t
ud
y
i
nv
o
l
v
i
ng
t
h
r
e
e
d
i
f
f
e
r
e
n
t
s
o
f
t
c
o
m
put
i
n
g
t
e
c
hni
que
s
a
n
d
a
m
u
l
t
i
-
c
r
i
t
e
r
i
a
de
c
i
s
i
o
n
a
n
a
ly
s
is
m
e
t
h
o
d.
F
o
r
di
a
b
e
t
e
s
pr
e
d
i
c
t
i
o
n
a
n
d
c
l
a
s
s
if
i
c
a
t
i
o
n
,
t
h
e
r
e
s
e
a
r
c
he
r
s
us
e
d
t
h
e
f
uz
z
y
a
n
a
ly
t
i
c
a
l
hi
e
r
a
r
c
hy
pr
o
c
e
s
s
(
F
A
HP)
,
a
r
t
i
f
i
c
i
a
l
n
e
ur
a
l
n
e
t
wo
r
k
(
A
NN
)
,
a
n
d
s
up
po
r
t
v
e
c
to
r
m
a
c
hi
ne
(
S
VM
)
m
e
t
h
o
do
l
o
g
i
e
s
.
S
e
l
e
c
t
i
ng
t
h
e
s
e
t
h
r
e
e
s
o
f
t
c
o
m
put
i
n
g
a
ppr
o
a
c
h
e
s
i
n
o
ur
s
t
udy
i
s
ba
s
e
d
o
n
t
h
e
d
i
ve
r
s
i
t
y
o
f
c
o
nc
e
pt
s
a
n
d
s
t
r
e
n
gt
h
/we
a
kn
e
s
s
e
s
o
f
t
h
o
s
e
m
e
t
h
o
ds
.
T
hi
s
i
s
t
h
e
f
i
r
s
t
s
t
udy
c
o
n
duc
t
e
d
to
c
o
m
pa
r
e
t
h
e
pe
r
f
o
r
m
a
n
c
e
o
f
a
m
u
l
t
i
-
c
r
i
t
e
r
i
a
de
c
i
s
i
o
n
-
m
a
k
i
ng
m
e
t
h
o
d
f
o
r
di
a
b
e
t
e
s
pr
e
d
i
c
t
i
o
n
to
t
h
e
ot
h
e
r
c
o
m
put
a
t
i
o
n
a
l
i
n
t
e
ll
i
ge
n
c
e
m
e
t
h
o
ds
.
On
publi
c
ly
a
v
a
il
a
ble
da
t
a
s
e
t
s
ga
t
h
e
r
e
d
f
r
o
m
520
pa
t
i
e
n
t
a
n
d
c
o
n
t
r
o
l
s
ubjec
t
s
,
t
h
e
m
e
t
h
o
ds
we
r
e
t
e
s
t
e
d.
T
h
e
r
e
m
a
i
ni
ng
s
e
c
t
i
o
ns
o
f
t
hi
s
wo
r
k
a
r
e
pr
e
s
e
n
t
e
d
a
s
in
:
t
h
e
a
ppr
o
a
c
h
e
s
we
r
e
pr
o
vi
d
e
d
a
n
d
e
x
p
l
a
i
ne
d
i
n
s
e
c
t
i
o
n
2.
T
h
e
e
x
pe
r
im
e
n
t
a
l
da
t
a
a
nd
c
o
m
m
e
n
t
s
a
r
e
pr
e
s
e
n
t
e
d
i
n
s
ec
t
i
o
n
3.
F
i
na
ll
y
,
s
e
c
t
i
o
n
4
b
r
i
ngs
t
h
e
pr
o
c
e
s
s
to
a
c
l
o
s
e
.
2.
RE
S
E
AR
CH
M
E
T
HO
D
T
o
c
o
n
duc
t
o
ur
c
o
m
pa
r
a
t
i
v
e
s
t
ud
y
,
t
h
r
e
e
m
e
t
h
o
ds
h
a
v
e
be
e
n
c
o
ns
i
d
e
r
e
d.
T
h
e
de
s
c
r
i
pt
i
o
n
o
f
t
h
e
s
e
m
e
t
h
o
ds
i
s
pr
e
s
e
n
t
e
d
b
r
i
e
f
ly
i
n
t
hi
s
s
e
c
t
i
o
n
a
s
in
:
2.
1.
F
u
z
z
y
an
al
yt
ica
l
h
ier
a
r
c
h
y
p
r
oc
e
s
s
(
F
AHP
)
S
a
a
t
y
[
17]
a
m
u
l
t
i
-
c
r
i
t
e
r
i
a
de
c
i
s
i
o
n
-
m
a
k
i
n
g
t
e
c
hni
que
c
a
ll
e
d
t
h
e
F
A
HP
wa
s
de
vi
s
e
d.
T
o
b
ui
l
d
F
A
HP,
t
h
e
f
uz
z
y
t
h
e
o
r
y
i
s
i
nc
o
r
por
a
t
e
d
i
n
t
o
t
h
e
f
u
n
da
m
e
n
t
a
l
A
HP.
T
h
e
A
HP
a
ppr
o
a
c
h
,
f
o
r
e
x
a
mp
l
e
,
h
a
s
b
e
e
n
u
s
e
d
i
n
t
h
e
pa
s
t
to
f
o
r
e
c
a
s
t
s
i
c
k
n
e
s
s
[
36]
.
A
s
a
r
e
s
u
l
t
,
F
A
HP
i
s
us
e
d
t
o
pr
e
di
c
t
t
h
e
o
c
c
ur
r
e
n
c
e
o
f
d
i
a
b
e
t
e
s
i
n
t
hi
s
s
t
ud
y
.
F
A
HP
i
s
a
de
c
i
s
i
o
n
-
m
a
k
i
ng
pr
oc
e
dur
e
t
h
a
t
i
s
f
r
e
que
n
t
l
y
e
m
p
l
o
y
e
d
i
n
c
a
s
e
s
i
nv
o
l
vi
ng
c
o
nf
li
c
t
i
n
g
c
r
i
t
e
r
i
a
.
T
h
e
pa
i
r
w
i
s
e
c
o
m
pa
r
i
s
o
n
s
o
f
t
h
e
c
r
i
t
e
r
i
a
a
n
d
a
l
t
e
r
n
a
t
i
v
e
s
a
r
e
im
p
l
e
m
e
n
t
e
d
i
n
F
A
HP
b
y
us
i
n
g
t
r
i
a
n
gu
l
a
r
n
u
m
be
r
s
to
r
e
pr
e
s
e
n
t
t
h
e
v
a
r
i
a
bl
e
s
[
37]
.
T
h
e
s
t
e
ps
o
f
t
h
e
F
A
HP
m
e
t
h
o
d
c
a
n
b
e
de
s
c
r
ib
e
d
a
s
:
-
S
t
e
p
1:
B
y
u
s
i
n
g
t
h
e
l
a
n
gu
a
ge
c
o
n
c
e
pt
s
i
n
T
a
bl
e
1
,
t
h
e
de
c
i
s
i
o
n
-
m
a
ke
r
c
o
m
pa
r
e
s
t
h
e
c
r
i
t
e
r
i
a
a
n
d
o
p
ti
o
n
s
.
As
a
r
e
s
u
l
t
,
t
h
e
c
o
m
pa
r
i
s
o
n
m
a
t
r
i
x
c
a
n
be
c
r
e
a
ted.
I
n
t
e
r
m
s
o
f
t
h
e
f
uz
z
y
t
r
i
a
n
gu
l
a
r
s
c
a
l
e
s
t
h
a
t
t
h
e
s
e
li
ngu
i
s
t
i
c
c
o
n
c
e
pt
s
c
o
r
r
e
s
po
n
d
to,
f
o
r
e
x
a
m
p
l
e
,
i
f
t
h
e
de
c
i
s
i
o
n
-
m
a
k
e
r
de
c
l
a
r
e
s
t
h
a
t
C
r
i
t
e
r
i
o
n
1
(
C
1)
i
s
l
e
s
s
im
po
r
t
a
n
t
t
h
a
n
C
r
i
t
e
r
i
o
n
2
(
C
2)
,
i
t
us
e
s
t
h
e
f
u
z
z
y
t
r
i
a
n
g
l
e
s
c
a
l
e
a
s
:
(2
)
-
(
4)
.
I
n
t
h
e
pa
i
r
w
i
s
e
c
o
n
t
r
i
but
i
o
n
m
a
t
r
i
x
o
f
t
h
e
c
r
i
t
e
r
i
a
,
t
h
e
c
o
m
p
a
r
i
s
o
n
o
f
C
2
t
o
C
1
w
i
ll
u
s
e
t
h
e
f
uz
z
y
t
r
i
a
n
g
l
e
s
c
a
l
e
(
1/4,
1/3,
1
/2)
.
T
h
e
pa
i
r
w
i
s
e
c
o
n
t
r
i
b
ut
i
o
n
m
a
t
r
i
x
i
s
s
h
o
wn
i
n
(
1
)
,
wh
e
r
e
s
i
g
ni
f
i
e
s
t
h
e
ℎ
de
c
i
s
i
o
n
m
a
ke
r
's
pr
e
f
e
r
e
n
c
e
f
o
r
t
h
e
ℎ
c
r
i
t
e
r
i
o
n
o
v
e
r
t
h
e
y
t
h
c
r
i
t
e
r
i
o
n
,
us
i
ng
f
uz
z
y
t
r
i
a
n
g
u
l
a
r
s
c
a
l
e
s
.
“
T
i
l
de
”
i
n
d
i
c
a
t
e
s
to
t
h
e
tr
i
a
n
gu
l
a
r
s
c
a
le
de
s
c
r
i
pt
i
o
n
,
f
o
r
t
h
e
i
n
s
t
a
nc
e
,
t
h
e
f
i
r
s
t
de
c
i
s
i
o
n
m
a
ke
r
'
s
pr
e
f
e
r
e
n
c
e
o
f
c
r
i
t
e
r
i
o
n
1
o
v
e
r
c
r
i
t
e
r
i
o
n
2,
e
qu
a
l
s
to
12
1
=
(
2
,
3
,
4
)
.
m
a
t
r
i
x
o
f
pa
i
r
w
i
s
e
c
o
m
pa
r
i
s
o
n
i
s
f
o
r
m
e
d
(
1
)
,
w
h
e
r
e
̃
de
n
ot
e
s
t
h
a
t
t
h
e
ℎ
m
a
k
e
s
t
h
e
s
e
l
e
c
t
i
o
n
o
f
t
h
e
ℎ
di
m
e
n
s
i
o
n
o
v
e
r
t
h
e
ℎ
di
m
e
ns
i
o
n.
̃
=
[
̃
11
m
12
…
̃
1
̃
21
…
…
̃
2
…
…
…
…
̃
1
̃
2
…
̃
]
(
1)
-
S
t
e
p
2
:
P
r
i
o
r
i
t
y
o
f
e
a
c
h
de
f
e
n
d
a
n
t
(
)
̃
i
s
c
o
l
l
e
c
t
e
d
̃
us
i
ng
(
2
):
Evaluation Warning : The document was created with Spire.PDF for Python.
I
S
S
N
:
2502
-
4752
I
n
do
n
e
s
i
a
n
J
E
l
e
c
E
n
g
&
C
o
m
p
S
c
i
,
Vo
l
.
25
,
N
o
.
2
,
F
e
b
r
ua
r
y
20
22
:
1167
-
1176
1170
m
̃
xy
=
∑
z
z
=
1
m
̃
xy
z
z
(
2)
T
a
bl
e
1.
L
i
n
gu
i
s
t
i
c
t
e
r
m
s
a
n
d
t
h
e
c
o
r
r
e
s
po
n
di
n
g
f
u
z
z
y
t
r
i
a
n
gu
l
a
r
s
c
a
l
e
s
F
uz
z
y
T
r
ia
ngul
a
r
S
c
a
le
L
in
gui
s
ti
c
D
e
s
c
r
ip
ti
o
n
I
nt
e
g
r
a
te
S
c
a
l
e
(
1/
9, 1/
9, 1/
9)
E
xt
r
e
me
l
y
l
e
s
s
i
mp
o
r
ta
nt
1/
9
(
1/
9, 1/
8, 1/
7)
T
h
e
i
nt
e
r
me
di
a
t
e
v
a
lu
e
s
be
tw
e
e
n t
w
o
a
dj
a
c
e
nt
s
c
a
l
e
s
1/
8
(
1/
8, 1/
7, 1/
6)
V
e
r
y
s
tr
o
ngl
y
l
e
s
s
i
mpor
ta
nt
1/
7
(
1/
7,
1/
6, 1/
5)
T
h
e
i
nt
e
r
me
di
a
t
e
v
a
lu
e
s
be
tw
e
e
n t
w
o
a
dj
a
c
e
nt
s
c
a
l
e
s
1/
6
(
1/
6, 1/
5, 1/
4)
s
tr
o
ngl
y
l
e
s
s
i
mpor
ta
nt
1/
5
(
1/
5, 1/
4, 1/
3)
T
h
e
i
nt
e
r
me
di
a
t
e
v
a
lu
e
s
be
tw
e
e
n t
w
o
a
dj
a
c
e
nt
s
c
a
l
e
s
1/
4
(
1/
4, 1/
3, 1/
2)
M
o
de
r
a
te
l
y
l
e
s
s
i
mp
o
r
ta
nt
1/
3
(
1/
3, 1/
2, 1/
1)
T
h
e
i
nt
e
r
me
di
a
t
e
v
a
lu
e
s
be
tw
e
e
n t
w
o
a
dj
a
c
e
nt
s
c
a
l
e
s
1/
2
(
1, 1, 1)
E
qua
l
I
mp
o
r
ta
nt
1
(
1, 2, 3)
T
h
e
i
nt
e
r
me
di
a
t
e
v
a
lu
e
s
be
tw
e
e
n t
w
o
a
dj
a
c
e
nt
s
c
a
l
e
s
2
(
2, 3, 4)
M
o
de
r
a
te
l
y
m
or
e
i
mp
o
r
ta
nt
3
(
3, 4, 5)
T
h
e
i
nt
e
r
me
di
a
t
e
v
a
lu
e
s
be
tw
e
e
n t
w
o
a
dj
a
c
e
nt
s
c
a
l
e
s
4
(
4, 5, 6
)
S
tr
o
ngl
y
m
o
r
e
i
mp
or
ta
nt
5
(
5, 6, 7)
T
h
e
i
nt
e
r
me
di
a
t
e
v
a
lu
e
s
be
tw
e
e
n t
w
o
a
dj
a
c
e
nt
s
c
a
l
e
s
6
(
6, 7, 8)
V
e
r
y
s
tr
o
ngl
y
m
or
e
i
mp
or
ta
nt
7
(
7, 8, 9)
T
h
e
i
nt
e
r
me
di
a
t
e
v
a
lu
e
s
be
tw
e
e
n t
w
o
a
dj
a
c
e
nt
s
c
a
l
e
s
8
(
9, 9, 9)
E
xt
r
e
me
l
y
m
o
r
e
i
mp
o
r
ta
nt
9
-
S
t
e
p
3:
T
h
e
m
a
t
r
i
x
o
f
pa
i
r
w
i
s
e
c
o
m
pa
r
i
s
o
n
i
s
upda
t
e
d
b
a
s
e
d
o
n
t
h
e
a
v
e
r
a
ge
o
f
r
e
s
po
n
s
e
s
.
̃
=
[
̃
11
⋯
̃
1
⋮
⋱
⋮
̃
1
⋯
̃
]
(
3)
-
S
t
e
p
4:
C
a
l
c
u
l
a
t
i
n
g
t
h
e
ge
o
m
e
t
r
i
c
m
e
a
n
o
f
a
f
uz
z
y
v
a
l
ua
t
i
o
n
m
a
t
r
i
x
f
o
r
e
a
c
h
d
i
m
e
ns
i
o
n
.
̃
=
(
∏
=
1
̃
)
1
/
,
=
1
,
2
…
…
(
4)
-
S
t
e
p
5:
M
e
r
ge
d
3
s
t
e
ps
to
ge
t
h
e
r
a
n
d
c
a
l
c
u
l
a
t
i
n
g
t
he
f
u
z
z
y
w
e
i
g
h
t
s
o
f
e
a
c
h
c
r
i
t
e
r
i
o
n
a
s
s
h
o
wn
i
n
:
-
S
t
e
p
5.
1:
C
a
l
c
u
l
a
t
e
t
h
e
v
e
c
to
r
s
um
m
a
t
i
o
n
o
f
̃
.
̃
=
∑
̃
=
1
(
5)
-
S
t
e
p
5.
2:
C
a
l
c
u
l
a
t
e
t
h
e
po
we
r
o
f
n
e
g
a
t
i
v
e
o
n
e
o
f
s
u
mm
a
t
i
o
n
v
e
c
t
or
.
(
̃
)
−
1
=
(
∑
̃
)
−
1
=
1
(
6)
-
S
t
e
p
5.
3:
C
a
l
c
u
l
a
t
e
d
t
h
e
f
uz
z
y
we
i
g
h
t
s
o
f
e
a
c
h
c
r
i
t
e
r
i
o
n
by
m
u
l
t
i
p
ly
w
i
t
h
r
e
v
e
r
s
e
e
a
c
h
̃
o
f
s
u
m
m
a
t
i
o
n
v
e
c
t
o
r
:
̃
=
̃
⊗
(
̃
1
⊕
̃
2
⊕
…
…
.
̃
)
−
1
=
(
,
,
)
(
7)
-
S
t
e
p
6:
Us
i
n
g
t
h
e
a
r
e
a
c
e
n
t
e
r
m
e
t
h
o
d,
t
h
e
n
o
n
-
f
uz
z
y
v
a
l
ue
i
s
c
a
l
c
u
l
a
t
e
d
by
us
i
ng
(
8
)
.
=
+
m
+
u
3
(
8)
-
S
t
e
p
7:
N
o
n
-
F
uz
z
y
v
a
l
ue
s
i
s
n
o
r
m
a
l
i
z
e
d,
by
u
s
i
ng
(
9
):
N
=
A
x
∑
n
x
=
1
A
(
9)
2.
2.
Ar
t
if
icial
n
e
u
r
a
l
n
e
t
wor
k
s
(
AN
Ns
)
A
m
u
l
t
i
-
l
a
y
e
r
pe
r
c
e
pt
r
o
n
a
r
c
hi
t
e
c
t
ur
e
c
a
l
l
e
d
a
n
AN
N
[
38]
i
s
u
s
e
d
t
o
t
r
a
i
n
a
n
d
c
l
a
s
s
if
y
i
nput
pa
tt
e
r
n
s
to
p
r
o
duc
e
t
h
e
r
e
qu
i
r
e
d
o
u
t
pu
t.
W
e
us
e
d
a
t
h
r
e
e
-
l
a
y
e
r
n
e
t
wor
k
i
n
o
ur
A
NN
m
o
de
l
,
w
i
t
h
14
n
e
ur
o
ns
i
n
t
h
e
Evaluation Warning : The document was created with Spire.PDF for Python.
I
n
do
n
e
s
i
a
n
J
E
l
e
c
E
n
g
&
C
o
m
p
S
c
i
I
S
S
N:
2502
-
4752
Sof
t
c
omputing
tec
hniques
f
or
e
ar
ly
diabete
s
pr
e
diction
(
Sabah
A
n
w
e
r
A
bdulkar
e
e
m
)
1171
i
nput
l
a
y
e
r
,
16
n
e
ur
o
n
s
i
n
t
h
e
hi
dde
n
l
a
y
e
r
,
a
n
d
t
wo
n
e
ur
o
n
s
i
n
t
h
e
o
ut
pu
t
l
a
y
e
r
.
T
h
e
n
u
m
be
r
o
f
ne
ur
o
n
s
i
n
hi
dde
n
l
a
y
e
r
wa
s
c
h
o
s
e
n
e
m
p
i
r
i
c
a
ll
y
,
pr
o
vi
d
i
ng
t
h
e
b
e
s
t
pe
r
f
o
r
m
a
n
c
e
.
B
a
c
kpr
o
pa
ga
t
i
o
n
wa
s
us
e
d
a
s
a
n
o
p
t
i
mi
z
a
t
i
o
n
t
oo
l
t
o
a
dj
u
s
t
t
h
e
n
e
t
wo
r
k'
s
we
i
g
h
t
s
w
i
t
h
s
l
o
pe
de
c
li
ne
a
f
t
e
r
o
ur
A
NN
m
o
de
l
wa
s
t
r
a
i
ne
d
o
n
t
h
e
da
t
a
.
T
o
i
m
pr
o
v
e
t
h
e
pe
r
f
o
r
m
a
n
c
e
o
f
t
h
e
AN
N
m
o
de
l
,
we
u
s
e
t
h
e
c
r
o
s
s
-
e
n
t
r
o
py
l
o
s
s
f
u
n
c
t
i
o
n
t
o
a
l
t
e
r
t
h
e
we
i
g
h
t
s
by
mi
n
i
mi
z
i
ng
t
h
e
e
r
r
o
r
a
t
e
a
c
h
s
t
a
ge
.
T
h
e
s
o
f
t
-
m
a
x
a
c
t
i
va
t
i
o
n
f
u
n
c
t
i
o
n
i
s
e
m
p
l
o
y
e
d
i
n
t
h
e
o
u
t
pu
t
l
a
y
e
r
t
o
ge
n
e
r
a
t
e
t
h
e
f
i
na
l
pr
e
d
i
c
t
i
o
n
.
T
h
e
f
o
ur
t
e
e
n
c
r
i
t
e
r
i
a
s
e
r
v
e
a
s
t
h
e
n
e
t
wo
r
k'
s
i
nput
.
A
t
t
h
e
s
a
m
e
t
im
e
,
t
h
e
o
u
t
pu
t
l
a
y
e
r
d
i
s
t
i
n
gu
i
s
h
e
s
be
t
we
e
n
d
i
a
b
e
t
e
s
pr
e
s
e
nc
e
(
1)
a
n
d
di
a
be
t
e
s
a
b
s
e
n
c
e
(
0)
.
T
h
e
e
qua
t
i
o
n
o
f
t
h
e
c
r
o
s
s
-
e
n
t
r
o
py
l
o
s
s
f
u
n
c
t
i
o
n
c
a
n
b
e
de
f
i
ne
d
a
s
(
10)
:
=
−
∑
(
)
2
=
1
(
10)
w
h
e
r
e
r
e
f
e
r
s
t
o
t
h
e
gr
o
un
d
t
r
u
t
h
v
a
l
ue
o
f
s
a
m
p
l
e
a
n
d
r
e
pr
e
s
e
n
t
s
t
h
e
pr
o
b
a
bil
i
t
y
o
f
t
h
e
s
a
m
p
l
e
r
e
s
u
l
t
e
d
f
r
o
m
s
o
f
t
-
m
a
x
a
c
t
i
v
a
t
i
o
n
f
u
n
c
t
i
o
n
.
2.
3.
S
u
p
p
or
t
ve
c
t
or
m
ac
h
in
e
s
(
S
VM
s)
S
VM
s
a
r
e
a
t
y
pe
o
f
m
a
c
hi
ne
l
e
a
r
ni
ng
a
n
d
a
r
t
i
f
i
c
i
a
l
i
n
t
e
ll
i
ge
n
c
e
m
e
t
h
o
d
t
h
a
t
i
s
o
f
t
e
n
e
m
p
l
o
y
e
d
i
n
s
upe
r
vi
s
e
d
l
e
a
r
ni
ng.
One
o
f
t
h
e
m
o
s
t
p
o
we
r
f
u
l
pr
e
d
i
c
t
i
o
n
a
l
go
r
i
t
hm
s
i
s
S
VM
s
.
Dur
i
n
g
t
h
e
tr
a
i
ni
ng
pr
o
c
e
s
s
,
S
VM
s
de
v
e
l
o
p
a
m
o
de
l
f
o
r
t
h
e
da
t
a
s
e
t
to
pr
e
di
c
t
p
o
i
n
t
l
a
be
l
s
.
S
VM
s
l
e
a
r
n
a
li
ne
a
r
de
c
i
s
i
o
n
b
o
un
da
r
y
t
o
d
i
s
t
i
n
gu
i
s
h
b
e
t
we
e
n
t
h
e
t
wo
c
l
a
s
s
e
s
b
a
s
e
d
o
n
a
s
e
t
o
f
bi
na
r
i
e
s
l
a
b
e
l
e
d
t
r
a
i
ni
ng
v
e
c
t
o
r
s
.
T
h
e
m
o
de
l
i
s
e
v
a
l
ua
t
e
d
us
i
ng
t
h
e
de
r
i
v
e
d
li
ne
a
r
c
l
a
s
s
i
f
i
c
a
t
i
o
n
r
u
l
e
to
c
a
t
e
gor
i
z
e
a
dd
i
t
i
o
n
a
l
t
e
s
t
i
ns
t
a
n
c
e
s
[
39]
.
A
s
o
l
i
d
m
a
r
g
i
n
c
l
a
s
s
i
f
i
e
r
,
t
h
e
s
i
m
p
l
e
s
t
s
o
r
t
o
f
S
VM
,
i
s
u
s
e
d
t
o
di
s
c
o
ve
r
t
h
e
l
i
ne
a
r
c
l
a
s
s
i
f
i
e
r
r
u
l
e
w
i
t
h
t
h
e
gr
e
a
t
e
s
t
ge
o
m
e
t
r
i
c
m
a
r
g
i
n
.
M
a
ny
o
pt
i
mi
z
a
t
i
o
n
pr
o
bl
e
m
s
c
a
n
b
e
s
o
l
ve
d
w
i
t
h
li
ne
a
r
S
VM
.
T
h
e
S
V
M
s
t
a
t
i
c
m
a
r
g
i
n
de
v
e
l
o
p
s
t
h
e
h
a
r
d
hy
pe
r
p
l
a
n
e
i
n
t
h
e
li
ne
a
r
ly
s
e
pa
r
a
bl
e
s
t
a
t
e
to
o
b
t
a
i
n
a
ll
da
t
a
a
c
c
ur
a
t
e
l
y
s
o
r
t
e
d
a
n
d
i
nc
r
e
a
s
e
t
h
e
d
i
s
t
a
n
c
e
to
t
h
e
n
e
a
r
e
s
t
tr
a
i
ni
ng
da
t
a
po
i
n
t
s
.
I
n
r
e
a
l
-
wo
r
l
d
da
t
a
,
da
t
a
s
e
t
s
a
r
e
f
r
e
que
n
t
l
y
n
o
t
l
i
ne
a
r
l
y
s
e
pa
r
a
bl
e
,
n
e
c
e
s
s
i
t
a
t
i
n
g
t
h
e
a
d
j
u
s
t
m
e
n
t
o
f
t
h
e
S
V
M
.
B
y
us
i
ng
t
h
e
s
o
f
t
m
a
r
g
i
n
pr
i
nc
i
p
l
e
,
t
hi
s
m
o
d
i
f
i
c
a
t
i
o
n
i
s
r
e
qu
i
r
e
d
t
o
a
c
hi
e
ve
a
t
r
a
de
-
o
f
f
be
t
we
e
n
m
a
xim
i
z
in
g
ge
o
m
e
t
r
i
c
m
a
r
g
i
n
a
n
d
de
c
r
e
a
s
i
n
g
c
l
a
s
s
i
f
i
c
a
t
i
o
n
e
r
r
or
o
n
t
h
e
po
i
n
t
s
o
f
t
r
a
i
ni
ng
da
t
a
.
T
h
e
s
o
f
t
m
a
r
g
i
n
c
r
e
a
t
e
s
a
hy
p
e
r
p
l
a
ne
,
a
l
l
o
w
i
ng
i
nc
o
r
r
e
c
t
c
l
a
s
s
if
i
c
a
t
i
o
n
o
f
d
i
f
f
i
c
u
l
t
c
a
s
e
s
t
o
i
n
c
r
e
a
s
e
t
he
d
i
s
t
a
nc
e
b
e
t
we
e
n
t
h
e
m
a
n
d
t
h
e
n
e
x
t
e
n
t
i
r
e
ly
s
e
pa
r
a
t
e
d
da
t
a
s
a
m
p
l
e
s
[
39]
,
[
40]
.
S
uppo
s
e
t
h
e
t
r
a
i
ni
ng
s
e
t
i
s
g
i
ve
n
a
s
(
1
,
1
)
,
(
2
,
2
)
,
…
,
(
,
)
,
wh
e
r
e
x
i
s
t
h
e
f
e
a
t
ur
e
s
e
t
a
n
d
a
r
e
t
h
e
l
a
b
e
l
s
w
i
t
h
c
l
a
s
s
e
s
,
∈
{
1
,
2
,
…
,
}
.
T
h
e
pr
i
m
a
l
pr
o
bl
e
m
f
o
r
S
VM
i
s
g
i
ve
n
a
s
(
11)
:
,
,
1
2
∑
‖
‖
2
∈
+
∑
∑
≠
=
1
.
.
∶
.
+
−
(
.
+
)
≥
1
−
≥
0
,
=
1
,
…
,
;
∈
{
1
,
…
,
}
(
11)
w
h
e
r
e
r
e
pr
e
s
e
n
t
s
t
h
e
di
s
t
a
n
c
e
o
f
t
h
e
po
i
n
t
t
h
a
t
i
s
c
l
a
s
s
if
i
e
d
i
n
t
h
e
wr
o
n
g
c
l
a
s
s
f
r
o
m
t
h
e
m
a
r
g
i
n
,
r
e
pr
e
s
e
n
t
s
t
h
e
we
i
g
h
t
s
,
i
s
bi
a
s
,
a
n
d
i
s
a
c
o
n
s
t
a
n
t
c
o
e
f
f
i
c
i
e
n
t
t
h
a
t
i
t
s
v
a
l
ue
r
e
f
l
e
c
t
s
t
h
e
we
i
g
h
t
o
f
t
h
e
pe
n
a
l
t
y
.
3.
E
XP
E
RM
E
NA
L
RE
S
UL
T
S
AN
D
AY
S
I
S
NT
AL
T
o
c
a
r
r
y
o
ut
t
h
e
e
x
pe
r
im
e
n
t
s
,
we
us
e
d
a
da
t
a
s
e
t
c
o
l
l
e
c
t
e
d
by
I
s
l
a
m
e
t
al.
[
34]
.
T
h
e
n
u
m
be
r
o
f
s
ubj
e
c
t
s
i
n
t
h
e
pr
o
vi
de
d
da
t
a
s
e
t
i
s
520
p
e
r
s
o
n
s
w
i
t
h
f
o
ur
t
e
e
n
a
tt
r
i
b
ut
e
s
r
e
pr
e
s
e
n
t
s
y
m
pt
o
m
s
t
h
a
t
m
a
y
c
a
us
e
d
i
a
b
e
t
e
s
.
I
n
a
dd
i
t
i
o
n
,
t
w
o
m
o
r
e
a
tt
r
i
b
ut
e
s
(
a
ge
a
nd
ge
n
de
r
)
r
e
pr
e
s
e
n
t
i
n
g
s
o
c
i
o
-
de
m
o
gr
a
phi
c
a
r
e
i
nc
l
ude
d
i
n
t
h
e
da
t
a
s
e
t
.
T
hi
s
s
t
ud
y
o
nly
f
o
c
u
s
e
s
o
n
s
ym
pt
om
a
tt
r
i
b
ut
e
s
o
f
d
i
a
b
e
t
e
s
d
i
s
e
a
s
e
,
i
n
c
l
ud
i
ng
ge
ni
t
a
l
t
h
r
us
h
,
a
l
o
pe
c
i
a
,
we
a
kn
e
s
s
,
o
b
e
s
i
t
y
,
m
u
s
c
l
e
s
t
i
f
f
ne
s
s
,
de
l
a
y
e
d
h
e
a
li
ng,
po
l
y
d
i
p
s
i
a
,
po
l
y
ur
i
a
,
po
l
y
p
h
a
g
i
a
,
a
n
d
vi
s
ua
l
b
l
ur
r
i
n
g,
i
r
r
i
t
a
bil
i
t
y
,
s
udde
n
we
i
g
h
t
l
o
s
s
,
pa
r
t
i
a
l
pa
r
e
s
i
s
,
a
n
d
i
t
c
hi
ng.
T
hi
s
da
t
a
s
e
t
h
a
s
b
e
e
n
c
o
l
l
e
c
t
e
d
by
a
c
o
n
duc
t
i
n
g
s
ur
ve
y
us
i
ng
que
s
t
i
o
nn
a
i
r
e
s
t
a
r
ge
t
i
n
g
pe
o
p
l
e
w
h
o
h
a
v
e
r
e
c
e
n
t
l
y
got
di
a
b
e
t
i
c
o
r
a
r
e
s
t
i
l
l
n
o
n
d
i
a
b
e
t
i
c
b
ut
h
a
ve
s
o
m
e
s
ym
pto
m
s
.
T
h
e
r
e
a
r
e
404
f
e
m
a
l
e
a
n
d
116
m
a
l
e
pe
r
s
o
n
s
,
t
h
e
i
r
a
ge
b
e
t
we
e
n
(
16
-
90)
.
T
h
e
a
l
l
o
c
a
t
i
o
n
o
f
s
ym
pt
o
m
s
a
m
o
n
g
pe
r
s
o
n
s
i
s
il
l
us
t
r
a
t
e
d
i
n
F
i
gur
e
1.
T
h
e
pa
i
r
w
i
s
e
c
o
m
pa
r
i
s
o
n
(
P
W
C
)
m
a
t
r
i
x
o
f
t
h
e
F
A
HP
s
h
o
u
l
d
b
e
pr
e
pa
r
e
d
i
n
i
t
i
a
ll
y
to
e
s
t
a
bl
i
s
h
t
h
e
F
A
HP
d
i
a
g
n
o
s
i
s
t
e
c
hni
que
d
e
p
i
c
t
e
d
i
n
F
i
gur
e
2.
T
h
e
im
po
r
t
a
n
c
e
o
f
s
ym
pt
o
m
s
w
a
s
r
a
t
e
d
us
i
n
g
t
h
e
do
c
to
r
'
s
j
udg
m
e
n
t
,
a
s
i
n
d
i
c
a
t
e
d
i
n
T
a
bl
e
2.
T
h
e
s
ym
pt
o
m
s
a
r
e
a
bb
r
e
vi
a
t
e
d
a
s
:
GT
:
ge
ni
t
a
l
t
h
r
us
h
,
AC
:
Al
o
pe
c
i
a
,
W
N:
w
e
a
kne
s
s
,
OS:
o
b
e
s
i
t
y
,
M
S
:
m
u
s
c
l
e
s
t
i
f
f
ne
s
s
,
DH
:
de
l
a
y
e
d
h
e
a
li
ng
,
P
D:
P
o
l
y
d
i
p
s
i
a
,
P
R
:
P
o
l
yur
i
a
,
P
G:
P
o
l
y
p
h
a
g
i
a
,
VB
:
vi
s
ua
l
bl
ur
r
i
n
g
,
I
A
:
i
r
r
i
t
a
bil
i
t
y
,
S
W
L
:
s
udd
e
n
we
i
g
h
t
l
o
s
s
,
P
P
:
pa
r
t
i
a
l
pa
r
e
s
i
s
,
a
n
d
L
I
:
i
t
c
hi
ng.
T
h
e
c
o
n
s
i
s
t
e
n
t
c
o
m
pa
r
i
s
o
n
m
a
t
r
i
x
,
w
hi
c
h
i
s
v
a
li
d
f
o
r
e
x
pe
r
i
m
e
n
t
s
,
wa
s
e
s
t
a
bli
s
he
d
a
f
t
e
r
m
u
l
t
i
p
l
e
r
e
vi
s
i
o
ns
i
n
t
h
e
pa
i
r
w
i
s
e
c
o
m
p
a
r
i
s
o
n
m
a
t
r
i
x
w
i
t
h
t
h
e
do
c
tor
'
s
a
s
s
i
s
t
a
n
c
e
.
T
h
e
a
c
hi
e
v
e
d
c
o
ns
i
s
t
e
n
c
y
r
a
t
i
o
o
f
t
h
e
pa
i
r
w
i
s
e
c
o
m
pa
r
i
s
o
n
m
a
t
r
i
x
i
s
0.
09,
whi
c
h
i
s
l
e
s
s
t
h
a
n
0.
1.
Evaluation Warning : The document was created with Spire.PDF for Python.
I
S
S
N
:
2502
-
4752
I
n
do
n
e
s
i
a
n
J
E
l
e
c
E
n
g
&
C
o
m
p
S
c
i
,
Vo
l
.
25
,
N
o
.
2
,
F
e
b
r
ua
r
y
20
22
:
1167
-
1176
1172
F
i
gur
e
1.
S
y
m
pt
o
m
s
d
i
s
t
r
i
b
ut
i
o
n
a
m
o
n
g
520
pe
r
s
on
s
.
Ye
s
:
i
nd
i
c
a
t
e
s
t
h
e
s
y
m
pt
o
m
s
pr
e
s
e
n
c
e
a
n
d
N
o
:
i
n
d
i
c
a
t
e
s
t
h
e
s
y
m
pt
o
m
a
bs
e
n
c
e
F
i
gur
e
2.
C
o
n
c
e
pt
ua
l
l
e
ve
l
o
f
F
A
HP
f
o
r
di
a
b
e
t
e
s
p
r
e
d
i
c
t
i
o
n
T
h
e
v
a
l
ue
o
f
t
h
e
ge
o
m
e
t
r
i
c
m
e
a
n
o
f
t
h
e
f
uz
z
y
m
a
t
r
i
x
i
s
o
b
t
a
i
ne
d
a
s
i
nd
i
c
a
t
e
d
i
n
T
a
bl
e
3
a
f
t
e
r
ge
n
e
r
a
t
i
n
g
pa
i
r
w
i
s
e
a
c
o
m
pa
r
i
s
o
n
m
a
t
r
i
x
.
F
o
r
e
x
a
m
p
l
e
,
a
c
c
o
r
di
n
g
to
(
4
)
,
t
h
e
pr
o
duc
t
o
f
t
h
e
f
o
ur
t
e
e
n
ve
c
t
or
s
yi
e
l
ds
t
h
e
v
a
l
ue
o
f
t
h
e
ge
o
m
e
t
r
i
c
m
e
a
n
o
f
f
uz
z
y
(
r
x)
f
o
r
t
h
e
f
i
r
s
t
c
r
i
t
e
r
i
o
n
.
As
s
h
o
w
n
i
n
:
̃
1
=
(
∏
14
=
1
̃
)
1
14
=
[
(
1
∗
2
∗
1
∗
4
∗
3
∗
4
∗
0
.
250
∗
0
.
2
∗
1
∗
1
∗
1
∗
1
∗
1
∗
1
)
1
14
;
(
1
∗
3
∗
1
∗
5
∗
4
∗
5
∗
0
.
333
∗
0
.
250
∗
1
∗
1
∗
1
∗
1
∗
1
∗
1
)
1
14
;
(
1
∗
4
∗
1
∗
6
∗
5
∗
6
∗
0
.
500
∗
0
.
333
∗
1
∗
1
∗
1
∗
1
∗
1
∗
1
)
1
14
]
=
[
1
.
408
;
1
.
258
;
1
.
119
]
t
h
us
,
t
h
e
tot
a
l
va
l
u
e
s
a
r
e
f
o
un
d
by
t
he
s
u
m
o
f
t
h
e
f
o
ur
t
e
e
n
c
r
i
t
e
r
i
a
o
f
.
T
h
e
r
e
v
e
r
s
e
v
a
l
ue
s
o
f
tot
a
l
s
u
m
P
(
-
1
)
,
s
h
o
wn
i
n
T
a
bl
e
3,
a
r
e
f
o
un
d
by
(
tot
a
l
s
u
m
)
^
-
1,
(
14
.
227)
^
-
1=
0
.
070.
I
n
a
dd
i
t
i
o
n
,
I
n
c
r
e
a
s
i
ng
Or
de
r
o
f
P
(
-
1)
i
s
o
b
t
a
i
ne
d
by
e
x
c
h
a
n
g
e
f
o
r
t
h
e
f
i
r
s
t
c
o
l
u
m
n
w
i
t
h
f
o
r
t
h
e
t
hi
r
d
c
o
l
u
m
n
a
s
s
h
o
wn
i
n
t
h
e
l
a
s
t
r
o
w
(
I
NC
R
)
o
f
T
a
bl
e
3.
Evaluation Warning : The document was created with Spire.PDF for Python.
I
n
do
n
e
s
i
a
n
J
E
l
e
c
E
n
g
&
C
o
m
p
S
c
i
I
S
S
N:
2502
-
4752
Sof
t
c
omputing
tec
hniques
f
or
e
ar
ly
diabete
s
pr
e
diction
(
Sabah
A
n
w
e
r
A
bdulkar
e
e
m
)
1173
T
a
b
e
l
2.
P
a
i
r
w
i
s
e
c
o
m
pa
r
i
s
o
n
m
a
t
r
i
x
(
P
W
C
)
m
a
t
r
i
x
CRI
GT
AC
WN
OS
MS
DH
PD
PR
PG
VB
IA
S
W
L
PP
LI
GT
(
1,1,1)
(
2,3,4)
(
1,1,1)
(
4,5,6)
(
3,4,5)
(
4,5,6)
(
1
4
,
1
3
,
1
2
)
(
1
5
,
1
4
,
1
3
)
(
1,1,1)
(
1,1,1)
(
1,1,1)
(
1,1,1)
(
1,1,1)
(
1,1,1)
AC
(
1
4
,
1
3
,
1
2
)
(
1,1,1)
(
1
4
,
1
3
,
1
2
)
(
1,2,3)
(
2,3,4)
(
2,3,4)
(
1
6
,
1
5
,
1
4
)
(
1
9
,
1
9
,
1
9
)
(
1,1,1)
(
1,1,1)
(
1,1,1)
(
1,1,1)
(
1,1,1)
(
1,1,1)
WN
(
1,1,1)
(
2,3,4)
(
1,1,1)
(
4,5,6)
(
6,7,8)
(
7,8,9)
(
1,1,1)
(
1,1,1)
(
1,1,1)
(
1,1,1)
(
1,1,1)
(
1,1,1)
(
1,1,1)
(
1,1,1)
OS
(
1
6
,
1
5
,
1
4
)
(
1
3
,
1
2
,
1
1
)
(
1
6
,
1
5
,
1
4
)
(
1,1,1)
(
1
4
,
1
3
,
1
2
)
(
1
5
,
1
4
,
1
3
)
(
1
8
,
1
7
,
1
6
)
(
1
9
,
1
8
,
1
7
)
(
1
9
,
1
9
,
1
9
)
(
1
7
,
1
6
,
1
5
)
(
1
4
,
1
3
,
1
2
)
(
1
5
,
1
4
,
1
3
)
(
1
3
,
1
2
,
1
1
)
(
1
9
,
1
8
,
1
7
)
MS
(
1
5
,
1
4
,
1
3
)
(
1
4
,
1
3
,
1
2
)
(
1
8
,
1
7
,
1
6
)
(
2,3,4)
(
1,1,1)
(
1
3
,
1
2
,
1
1
)
(
1
6
,
1
5
,
1
4
)
(
1
7
,
1
6
,
1
5
)
(
1
4
,
1
3
,
1
2
)
(
1
3
,
1
2
,
1
1
)
(
1,1,1)
(
1
4
,
1
3
,
1
2
)
(
1
3
,
1
2
,
1
1
)
(
1
5
,
1
4
,
1
3
)
DH
(
1
6
,
1
5
,
1
4
)
(
1
4
,
1
3
,
1
2
)
(
1
9
,
1
8
,
1
7
)
(
3,4,5)
(
1,2,3)
(
1,1,1)
(
1
9
,
1
8
,
1
7
)
(
1
7
,
1
6
,
1
5
)
(
1,1,1)
(
1,1,1)
(
1,1,1)
(
1
7
,
1
6
,
1
5
)
(
1
5
,
1
4
,
1
3
)
(
1
4
,
1
3
,
1
2
)
PD
(
2,3,4)
(
4,5,6)
(
1,1,1)
(
6,7,8)
(
4,5,6)
(
7,8,9)
(
1,1,1)
(
1
3
,
1
2
,
1
1
)
(
1,1,1)
(
1,1,1)
(
1,
1,1)
(
1,1,1)
(
1,1,1)
(
1,1,1)
PR
(
3,4,5)
(
9,9,9)
(
1,1,1)
(
7,8,9)
(
5,6,7)
(
5,6,7)
(
1,2,3)
(
1,1,1)
(
1,1,1)
(
1,1,1)
(
1,1,1)
(
1,1,1)
(
1,1,1)
(
1,1,1)
PG
(
1,1,1)
(
1,1,1)
(
1,1,1)
(
9,9,9)
(
2,3,4)
(
1,1,1)
(
1,1,1)
(
1,1,1)
(
1,1,1)
(
1,1,1)
(
2,3,4)
(
1
3
,
1
2
,
1
1
)
(
1
3
,
1
2
,
1
1
)
(
1,1,1)
VB
(
1,1,1)
(
1,1,1)
(
1,1,1)
(
5,6,7)
(
1,2,3)
(
1,1,1)
(
1,1,1)
(
1,1,1)
(
1,1,1)
(
1,1,1)
(
1,1,1)
(
1,1,1)
(
1,1,1)
(
1,1,1)
IA
(
1,1,1)
(
1,1,1)
(
1,1,1)
(
2,3,4)
(
1,1,1)
(
1,1,1)
(
1,1,1)
(
1,1,1)
(
1
4
,
1
3
,
1
2
)
(
1,1,1)
(
1,1,1)
(
1,1,1)
(
1,1,1)
(
1,1,1)
S
W
L
(
1,1,1)
(
1,1,1)
(
1,1,1)
(
3,4,5)
(
2,3,4)
(
5,6,7)
(
1,1,1)
(
1,1,1)
(
1,2,3)
(
1,1,1)
(
1,1,1)
(
1,1,1)
(
3,4,5)
(
1,1,1)
PP
(
1,1,1)
(
1,1,1)
(
1,1,1)
(
1,2,3)
(
1,2,3)
(
3,4,5)
(
1,1,1)
(
1,1,1)
(
1,2,3)
(
1,1,1)
(
1,1,1)
(
1
5
,
1
4
,
1
3
)
(
1,1,1)
(1
,1,1)
LI
(
1,1,1)
(
1,1,1)
(
1,1,1)
(
7,8,9)
(
3,4,5)
(
2,3,4)
(
1,1,1)
(
1,1,1)
(
1,1,1)
(
1,1,1)
(
1,1,1)
(
1,1,1)
(
1,1,1)
(
1,1,1)
T
o
c
a
l
c
u
l
a
t
e
t
h
e
f
uz
z
y
we
i
g
h
t
s
o
f
e
a
c
h
c
r
i
t
e
r
i
o
n
(
Wx
)
,
i
n
(
7
)
i
s
t
h
e
n
a
pp
li
e
d
a
s
f
o
l
l
o
w
s
:
W
1=
[
1.
119*0.
055;
1.
258*0.
063;
1.
408*
0.
0
70
]
=
[
0.
0
62;
0.
079;
0.
099]
.
I
n
t
h
e
l
a
s
t
s
t
e
p,
t
h
e
n
o
n
-
f
u
z
z
y
we
i
g
h
t
v
a
l
ue
o
f
e
a
c
h
c
r
i
t
e
r
i
o
n
(
A
x
)
i
s
f
o
u
n
d
by
t
a
k
i
n
g
t
h
e
a
v
e
r
a
ge
o
f
f
uz
z
y
we
i
g
h
t
v
a
l
ue
s
f
o
r
e
a
c
h
c
r
i
t
e
r
i
o
n
us
i
n
g
(
8
)
a
s
f
o
l
l
o
ws
:
A
1
=
[
(
0.
062+
0
.
079+
0.
099)
/3
]
=
0.
080
.
T
h
us
,
t
h
e
c
a
l
c
u
l
a
t
i
o
n
o
f
t
h
e
tot
a
l
o
f
A
x
i
s
o
b
ta
i
n
e
d
b
y
s
u
m
mi
ng
o
f
A
x
v
a
l
u
e
s
f
o
r
e
a
c
h
c
r
i
t
e
r
i
o
n
.
F
i
na
ll
y
,
t
h
e
n
o
r
m
a
l
i
z
a
t
i
o
n
o
f
n
o
n
–
F
uz
z
y
v
a
l
ue
s
(
N
x
)
i
s
c
a
l
c
u
l
a
t
e
d
by
u
s
i
ng
(
9
)
.
F
o
r
e
x
a
m
p
l
e
,
N
1
=A
1
/
tot
a
l
o
f
N1=
0.
080/1.
008
=
0.
079.
T
h
e
we
i
g
h
t
c
a
l
c
u
l
a
t
i
o
n
f
o
r
a
l
l
c
r
i
t
e
r
i
a
i
s
pr
e
s
e
n
t
e
d
i
n
T
a
bl
e
3.
T
a
bl
e
3.
T
h
e
ge
o
m
e
t
r
i
c
m
e
a
n
o
f
f
uz
z
y
(
r
x
)
,
w
i
t
h
f
u
z
z
y
we
i
g
h
t
(
w
x
)
,
wi
t
h
a
v
e
r
a
ge
d
we
i
g
h
t
c
r
i
t
e
r
i
o
n
(
A
x
)
,
a
n
d
n
o
r
m
a
li
z
e
d
we
i
g
h
t
c
r
i
t
e
r
i
o
n
(
N
i
)
N
x
A
x
W
x
r
x
CRI
0.079
0.080
0.099
0.079
0.062
1.119
1.258
1.408
C1
0.050
0.051
0.065
0.050
0.037
0.
681
0.801
0.925
C2
0.101
0.101
0.119
0.102
0.083
1.515
1.618
1.703
C3
0.013
0.013
0.023
0.015
0.000
0.000
0.245
0.322
C4
0.019
0.019
0.040
0.000
0.018
0.324
0.000
0.578
C5
0.031
0.031
0.044
0.029
0.021
0.385
0.461
0.627
C6
0.109
0.110
0.135
0.109
0.08
5
1.547
1.727
1.936
C7
0.126
0.127
0.154
0.128
0.101
1.830
2.034
2.193
C8
0.079
0.080
0.100
0.078
0.061
1.104
1.240
1.426
C9
0.074
0.075
0.087
0.075
0.062
1.122
1.194
1.243
C
10
0.062
0.063
0.074
0.063
0.052
0.952
1.000
1.051
C
11
0.098
0.099
0.121
0.09
9
0.076
1.379
1.575
1.727
C
12
0.072
0.073
0.092
0.073
0.053
0.964
1.160
1.312
C
13
0.086
0.087
0.101
0.087
0.072
1.306
1.385
1.449
C
14
1.000
1.008
17.900
15.698
14.227
T
ot
a
l
0.056
0.064
0.070
P
(
-
1)
0.070
0.064
0.056
I
N
C
R
T
h
e
hi
g
h
e
s
t
we
i
g
h
t
s
we
r
e
a
s
s
i
g
n
e
d
a
m
o
n
g
t
h
e
s
ym
pt
o
m
s
t
o
P
o
l
y
ur
i
a
(
P
R
)
f
o
l
l
o
we
d
by
P
o
l
y
d
i
p
s
i
a
s
y
m
pt
o
m
(
P
D)
.
T
h
e
l
o
we
s
t
we
i
g
h
t
i
s
a
s
s
i
g
n
e
d
o
b
e
s
i
t
y
s
ym
pt
o
m
(
OS)
f
o
l
l
o
we
d
by
de
l
a
y
e
d
h
e
a
l
i
n
g
s
y
m
pt
o
m
(
DH
)
.
Af
t
e
r
c
a
l
c
u
l
a
t
i
n
g
t
h
e
w
e
i
g
h
t
o
f
t
he
c
r
i
t
e
r
i
a
,
t
h
e
da
t
a
e
n
t
r
y
o
f
e
a
c
h
c
r
i
t
e
r
i
o
n
i
n
t
h
e
d
a
t
a
s
e
t
i
s
t
h
e
n
m
u
l
t
i
p
li
e
d
by
t
h
e
a
s
s
i
g
n
e
d
we
i
g
h
t
.
T
h
e
s
u
m
m
a
t
i
o
n
va
l
u
e
o
f
e
a
c
h
s
u
bj
e
c
t
i
s
t
h
e
n
c
a
l
c
u
l
a
t
e
d
by
s
u
m
mi
ng
t
h
e
we
i
g
h
t
e
d
c
r
i
t
e
r
i
a
.
He
nc
e
,
e
a
c
h
s
u
bj
e
c
t
w
i
ll
h
a
v
e
a
c
e
r
t
a
i
n
v
a
l
u
e
r
e
s
u
l
t
e
d
f
r
o
m
t
h
e
t
ot
a
l
s
u
m
o
f
t
h
e
we
i
g
h
t
e
d
c
r
i
t
e
r
i
a
v
a
l
ue
s
.
T
h
e
m
e
a
n
o
f
t
h
e
s
e
we
i
g
h
t
e
d
c
r
i
t
e
r
i
a
i
s
t
h
e
n
c
o
m
put
e
d
a
n
d
t
a
ke
n
a
s
t
h
e
t
h
r
e
s
h
o
l
d
v
a
l
ue
.
T
h
e
t
h
r
e
s
h
o
l
d
i
s
t
h
e
n
c
o
m
pa
r
e
d
to
e
a
c
h
va
lue
i
n
t
h
e
we
i
g
h
t
e
d
s
u
m
c
o
nn
e
c
t
e
d
to
a
s
pe
c
i
f
i
c
c
r
it
e
r
i
o
n
.
I
f
t
h
e
we
i
g
h
t
e
d
s
u
m
v
a
l
ue
i
s
e
qu
a
l
t
o
o
r
m
o
r
e
t
h
a
n
t
h
e
t
h
r
e
s
h
o
l
d,
t
h
e
r
e
s
u
l
t
i
s
(
1)
,
i
n
d
i
c
a
t
i
n
g
t
h
a
t
t
h
e
pa
t
i
e
n
t
h
a
s
d
i
a
b
e
t
e
s
.
I
f
t
h
e
we
i
g
h
t
e
d
s
u
m
v
a
l
ue
i
s
l
e
s
s
t
h
a
n
t
he
t
h
r
e
s
h
o
l
d,
t
h
e
r
e
s
u
l
t
i
s
(
0)
,
i
n
d
i
c
a
t
i
n
g
a
n
e
g
a
t
i
ve
d
i
a
b
e
t
e
s
d
i
a
g
n
o
s
i
s
.
T
h
e
v
a
l
ue
o
f
t
h
e
t
h
r
e
s
h
o
l
d
o
b
t
a
i
n
e
d
f
r
o
m
o
ur
e
x
pe
r
i
m
e
n
t
wa
s
2.
43.
T
o
a
c
hi
e
v
e
t
h
e
pur
po
s
e
o
f
o
ur
c
o
m
pa
r
i
s
o
n
s
t
ud
y
,
we
a
l
s
o
t
r
a
i
n
e
d
a
n
d
t
e
s
t
e
d
A
NN
a
n
d
S
VM
m
o
de
l
s
.
T
o
pr
o
c
e
s
s
l
a
be
l
im
ba
l
a
nc
e
,
t
h
e
da
t
a
s
e
t
'
s
m
i
n
o
r
i
t
y
c
l
a
s
s
(
n
e
ga
t
i
v
e
c
l
a
s
s
e
s
)
wa
s
o
v
e
r
s
a
m
p
l
e
d
s
u
b
s
t
a
n
t
i
a
ll
y
dur
i
n
g
tr
a
i
ni
ng.
T
h
e
o
v
e
r
s
a
m
p
l
i
ng
do
ubl
e
s
t
h
e
s
i
z
e
o
f
t
h
e
n
e
ga
t
i
v
e
e
x
a
m
p
l
e
s
a
n
d
s
u
b
s
e
que
n
t
l
y
,
b
a
l
a
n
c
e
s
t
h
e
Evaluation Warning : The document was created with Spire.PDF for Python.
I
S
S
N
:
2502
-
4752
I
n
do
n
e
s
i
a
n
J
E
l
e
c
E
n
g
&
C
o
m
p
S
c
i
,
Vo
l
.
25
,
N
o
.
2
,
F
e
b
r
ua
r
y
20
22
:
1167
-
1176
1174
t
w
o
c
l
a
s
s
e
s
,
pr
o
duc
i
n
g
be
tt
e
r
da
t
a
r
e
pr
e
s
e
n
t
a
t
i
o
n
dur
i
n
g
t
h
e
t
r
a
i
ni
n
g
p
h
a
s
e
.
T
h
e
da
t
a
wa
s
s
p
li
t
i
n
t
o
t
h
r
e
e
c
a
t
e
g
o
r
i
e
s
:
60%
f
o
r
tr
a
i
ni
ng,
15%
f
o
r
v
a
li
da
t
i
o
n
,
a
n
d
25%
f
o
r
t
e
s
t
i
n
g.
B
y
c
o
m
pa
r
i
n
g
t
h
e
l
a
b
e
l
o
f
a
nt
i
c
i
pa
t
e
d
d
i
a
b
e
t
e
s
w
i
t
h
t
h
e
a
c
t
ua
l
va
l
ue
pr
o
vi
de
d
a
l
o
n
g
w
i
t
h
t
h
e
da
t
a
s
e
t,
t
h
e
pe
r
f
o
r
m
a
n
c
e
f
o
r
d
i
a
b
e
t
e
s
pr
e
di
c
t
i
o
n
i
s
e
v
a
l
ua
t
e
d
u
s
i
n
g
v
a
r
i
o
us
e
v
a
l
ua
t
i
o
n
c
r
i
t
e
r
i
a
.
a
c
c
ur
a
c
y
,
s
e
n
s
i
t
i
v
i
t
y
,
s
p
e
c
i
f
i
c
i
t
y
,
pr
e
c
i
s
i
o
n
,
F
-
m
e
a
s
ur
e
,
a
n
d
G
-
m
e
a
n
a
r
e
a
m
o
n
g
t
h
e
e
v
a
l
ua
t
i
o
n
m
e
t
r
i
c
s
,
w
hi
c
h
a
r
e
de
f
i
ne
d
a
s
:
ACC
(
)
=
TP
+
TN
TP
+
TN
+
FP
+
FN
(1
2
)
S
E
NS
I
T
I
V
I
T
Y
(
S
E
N
)
=
TP
TP
+
FN
(
13)
(
)
=
TN
TN
+
FP
(
14)
P
RE
CI
S
I
O
N
(
)
=
TP
TP
+
FP
(
15)
−
M
E
A
S
U
RE
=
2
×
se
n
si
t
i
v
i
t
y
×
p
r
ec
i
si
o
n
se
n
si
t
i
v
i
t
y
+
p
r
ec
i
si
o
n
(
16)
−
M
E
A
N
=
√
S
e
ns
it
ivit
y
×
S
p
e
cificity
(
17)
w
h
e
r
e
T
P
,
F
P
,
T
N
,
a
n
d
F
N
r
e
pr
e
s
e
n
t
t
r
ue
p
os
i
t
i
v
e
,
f
a
l
s
e
po
s
i
t
i
v
e
,
t
r
ue
n
e
ga
t
i
v
e
,
a
n
d
f
a
l
s
e
n
e
ga
t
i
v
e
,
r
e
s
pe
c
t
i
ve
ly
.
T
he
r
e
s
u
l
t
s
o
b
t
a
i
n
e
d
f
r
o
m
t
h
e
t
h
r
e
e
d
i
a
b
e
t
e
s
pr
e
d
i
c
t
i
o
n
m
o
de
l
s
i
nc
l
ud
i
n
g,
F
A
HP,
AN
N,
a
n
d
S
VM
,
us
i
n
g
t
h
e
s
i
x
-
e
v
a
l
ua
t
i
o
n
m
e
t
r
i
c
s
h
a
ve
b
e
e
n
d
e
p
i
c
t
e
d
i
n
T
a
bl
e
4.
T
a
bl
e
4
.
T
h
e
c
o
m
pa
r
i
s
o
n
a
m
o
n
g
t
h
e
t
h
r
e
e
d
i
a
b
e
t
e
s
pr
e
d
i
c
t
i
o
n
m
o
de
l
s
M
o
de
l
A
c
c
u
r
a
c
y
S
e
ns
it
i
v
it
y
S
pe
c
i
f
i
c
it
y
P
r
e
c
is
i
o
n
F
-
M
e
a
s
ur
e
G
-
M
e
a
n
F
A
H
P
0.7654
0.7312
0.82
0.8667
0.7932
0.7744
ANN
0.8385
0.747
1
1
0.8552
0.864
3
S
V
M
0.8923
0.8793
0.9028
0.8793
0.8793
0.891
T
h
e
f
i
nd
i
ngs
s
h
o
w
t
h
a
t
t
h
e
F
A
HP
m
o
de
l
i
s
a
n
e
xc
e
l
l
e
n
t
t
oo
l
f
o
r
d
i
a
g
n
o
s
i
n
g
m
e
d
i
c
a
l
d
i
s
o
r
de
r
s
b
a
s
e
d
o
n
m
a
ny
c
r
i
t
e
r
i
a
,
w
h
e
r
e
t
h
e
r
e
l
a
t
i
v
e
i
m
po
r
t
a
n
c
e
(
pr
i
o
r
i
t
y
)
o
f
e
a
c
h
c
r
i
t
e
r
i
o
n
t
o
t
h
e
ot
h
e
r
s
i
s
n
o
t
we
l
l
de
f
i
ne
d.
T
h
e
r
e
po
r
t
e
d
s
e
ns
i
t
i
vi
t
y
s
h
o
ws
0.
7312,
0.
747,
a
n
d
0.
8793
f
r
o
m
F
A
HP,
A
NN
,
a
n
d
S
V
M
,
r
e
s
pe
c
t
i
v
e
ly
.
T
h
e
s
e
v
a
l
ue
s
i
n
d
i
c
a
t
e
t
h
a
t
t
h
e
m
e
t
h
o
ds
c
o
ul
d
b
e
us
e
d
i
n
c
l
i
n
i
c
a
l
pr
a
c
t
i
c
e
a
s
a
c
o
m
put
e
r
-
a
s
s
i
s
t
a
n
t
di
a
g
n
o
s
i
s
too
l
a
n
d
a
s
a
s
e
c
o
n
d
o
bs
e
r
v
e
r
.
Ye
t
,
i
t
w
i
ll
n
o
t
r
e
pl
a
c
e
t
h
e
d
e
c
i
s
i
o
n
t
a
ke
n
by
a
n
e
x
pe
r
t
phy
s
i
c
i
a
n
.
I
t
'
s
wo
r
t
h
n
o
t
i
n
g
t
h
a
t
t
h
e
a
s
s
e
s
s
m
e
n
t
m
e
t
r
i
c
s
f
o
r
F
AH
P
a
r
e
s
li
g
h
t
l
y
l
o
we
r
t
h
a
n
t
h
o
s
e
pu
bli
s
h
e
d
f
o
r
A
NN
a
n
d
S
VM
s
i
n
c
e
i
t
wa
s
t
e
s
t
e
d
o
n
t
h
e
e
n
t
i
r
e
da
t
a
s
e
t
wi
t
h
n
o
o
v
e
r
s
a
m
p
l
i
ng,
unli
ke
m
a
c
hi
ne
l
e
a
r
ni
ng
m
o
de
l
s
.
Ov
e
r
a
ll
,
t
h
e
t
h
r
e
e
d
i
a
b
e
t
e
s
pr
e
d
i
c
t
i
o
n
m
o
de
l
s
pr
o
duc
e
c
o
m
p
e
t
i
t
i
v
e
f
i
nd
i
ng
s
a
n
d
go
o
d
pe
r
f
o
r
m
a
n
c
e
,
de
m
o
ns
t
r
a
t
i
n
g
t
h
e
i
r
po
s
s
ibi
li
t
y
o
f
de
t
e
c
t
i
n
g
d
i
a
b
e
t
e
s
e
a
r
l
y
.
4.
CONC
L
USI
ON
I
n
t
hi
s
pa
pe
r
,
a
c
o
m
pa
r
i
s
o
n
s
t
ud
y
ha
s
b
e
e
n
c
o
n
duc
t
i
ng
h
a
r
n
e
s
s
i
ng
s
o
f
t
c
o
m
put
i
n
g
t
e
c
hni
que
s
t
o
e
a
r
ly
pr
e
d
i
c
t
d
i
a
b
e
t
e
s
f
r
o
m
pu
bli
c
ly
a
v
a
il
a
bl
e
da
t
a
.
T
h
e
ge
n
e
r
a
t
e
d
m
o
de
l
s
,
whi
c
h
i
nc
l
ude
d
F
A
HP,
A
NN
,
a
n
d
S
VM
,
we
r
e
s
uc
c
e
s
s
f
u
l
i
n
de
t
e
c
t
i
n
g
d
i
a
be
t
e
s
i
n
a
gr
o
up
o
f
pe
o
p
l
e
.
T
h
e
F
A
HP
a
ppr
o
a
c
h
wa
s
u
s
e
d
t
o
c
r
e
a
t
e
,
im
p
l
e
m
e
n
t
,
a
n
d
e
v
a
l
ua
t
e
t
h
e
i
n
t
e
r
e
s
t
o
f
w
e
i
g
h
t
i
n
g
c
r
i
t
e
r
i
a
f
r
o
m
d
i
a
b
e
t
i
c
s
ym
pt
o
m
s
.
F
ur
t
h
e
r
m
o
r
e
,
i
n
t
e
r
m
s
o
f
a
c
c
ur
a
c
y
,
s
e
n
s
i
t
i
v
i
t
y
,
s
pe
c
if
i
c
i
t
y
,
pr
e
c
i
s
i
o
n
,
F
-
m
e
a
s
ur
e
,
a
n
d
F
-
m
e
a
n
,
t
h
e
f
i
nd
i
ng
s
o
b
t
a
i
n
e
d
f
r
o
m
a
pp
lyi
ng
t
h
e
pr
o
p
o
s
e
d
a
ppr
o
a
c
h
e
s
de
m
o
ns
t
r
a
t
e
d
t
h
a
t
i
t
i
s
pr
o
m
i
s
i
n
g
i
n
a
c
c
ur
a
t
e
l
y
a
n
d
e
f
f
e
c
t
i
v
e
ly
pr
e
d
i
c
t
i
n
g
d
i
a
b
e
t
e
s
.
T
h
e
pr
o
p
o
s
e
d
di
a
be
t
e
s
pr
e
d
i
c
t
i
o
n
a
l
go
r
i
t
hm
s
c
a
n
b
e
r
e
a
d
i
ly
a
n
d
s
m
o
o
t
h
l
y
a
pp
l
i
e
d
to
m
o
de
l
s
f
o
r
ot
h
e
r
d
i
s
e
a
s
e
s
.
F
o
r
f
ut
ur
e
r
e
s
e
a
r
c
h
,
we
s
ugge
s
t
s
t
udy
i
ng
t
h
e
pe
r
f
o
r
m
a
n
c
e
o
f
t
h
e
s
e
m
e
t
h
o
ds
b
a
s
e
d
o
n
a
n
e
ns
e
m
b
l
e
l
e
a
r
ni
ng
pa
r
a
d
i
g
m
.
AC
K
NOWL
E
DGE
M
E
NT
S
T
h
e
a
ut
h
o
r
s
wo
u
l
d
li
ke
t
o
e
x
pr
e
s
s
t
h
e
i
r
s
p
e
c
i
a
l
t
h
a
n
k
s
t
o
Dr
.
A
b
du
ll
a
t
e
e
f
Al
-
B
a
y
a
t
i
f
r
o
m
Al
-
M
us
t
a
n
s
i
r
iy
a
h
U
ni
ve
r
s
i
t
y
,
C
o
l
l
e
ge
o
f
M
e
d
i
c
i
ne
f
o
r
pr
o
vi
d
i
ng
t
h
e
gr
o
un
d
t
r
u
t
h
j
udg
m
e
n
t
o
f
t
h
e
c
o
m
pa
r
i
s
o
n
m
a
t
r
i
x
us
e
d
i
n
t
hi
s
s
t
udy
.
W
e
a
l
s
o
wo
ul
d
l
i
k
e
to
t
h
a
n
k
Dr
.
B
a
i
d
a
a
Al
-
B
a
n
de
r
f
o
r
h
e
r
a
dvi
c
e
t
h
r
o
ugh
c
o
n
duc
t
i
n
g
t
hi
s
wo
r
k
.
Evaluation Warning : The document was created with Spire.PDF for Python.
I
n
do
n
e
s
i
a
n
J
E
l
e
c
E
n
g
&
C
o
m
p
S
c
i
I
S
S
N:
2502
-
4752
Sof
t
c
omputing
tec
hniques
f
or
e
ar
ly
diabete
s
pr
e
diction
(
Sabah
A
n
w
e
r
A
bdulkar
e
e
m
)
1175
RE
F
E
R
E
NC
E
S
[
1]
A
me
r
i
c
a
n
D
ia
b
e
t
e
s
A
s
s
o
c
ia
ti
o
n,
“
S
ta
nda
r
ds
of
M
e
di
c
a
l
C
a
r
e
in
D
ia
be
t
e
s
-
2018
A
br
id
ge
d
f
o
r
P
r
im
a
r
y
C
a
r
e
P
r
ov
id
e
r
s
,”
C
li
n
ic
al
D
ia
be
te
s
, v
o
l.
36, n
o
. 1, pp. 14
–
37, J
a
n. 2018, d
o
i:
10.2337/
c
d1
7
-
0119.
[
2]
M
.
W
e
gmul
le
r
,
J
.
P
.
W
e
id
,
P
.
O
be
r
s
o
n,
a
nd
N
.
G
is
in
,
“
H
ig
h
r
e
s
o
lu
ti
o
n
f
ib
e
r
di
s
tr
ib
u
te
d
m
e
a
s
ur
e
me
nt
s
w
it
h
c
o
he
r
e
nt
O
F
D
R
,
”
E
C
O
C
’
00
, vo
l.
11, n
o
. 4, p. 109, 2000.
[
3]
C
.
F
ia
r
ni
,
E
.
M
.
S
ip
a
y
ung,
a
nd
S
.
M
a
e
muna
h,
“
A
na
ly
s
is
a
nd
P
r
e
di
c
ti
o
n
of
D
ia
b
e
t
e
s
C
o
mpl
i
c
a
ti
o
n
D
is
e
a
s
e
us
in
g
D
a
ta
M
i
ni
ng
A
lg
o
r
i
th
m,”
P
r
oc
e
di
a C
om
put
e
r
Sc
ie
n
c
e
, v
o
l.
161, pp. 449
–
45
7, 2019, do
i:
10.1016/j
.p
r
oc
s
.2019.11.144.
[
4]
A
me
r
i
c
a
n
D
ia
be
t
e
s
A
s
s
o
c
ia
ti
o
n,
“
2.
C
l
a
s
s
if
i
c
a
ti
o
n
a
nd
D
ia
gno
s
is
of
D
ia
b
e
t
e
s
:
S
ta
nda
r
ds
of
M
e
di
c
a
l
C
a
r
e
in
D
ia
b
e
te
s
—
20
18,”
D
ia
be
te
s
C
a
r
e
, v
o
l.
41, n
o
.
S
uppl
e
me
nt
_1, pp.
S
13
–
S
27, J
a
n. 2018, do
i:
10.2337/d
c
18
-
S
002.
[
5]
D
ia
be
te
s
A
us
tr
a
li
a
,
“
F
a
il
ur
e
t
o
d
e
te
c
t
t
y
p
e
2
di
a
be
t
e
s
e
a
r
l
y
c
o
s
ti
ng
$700
mi
l
li
o
n
p
e
r
y
e
a
r
,”
D
ia
be
te
s
A
us
tr
al
ia
.
ht
tp
s
:/
/ww
w
.di
a
be
te
s
a
us
tr
a
li
a
.c
o
m.a
u/
n
e
w
s
/
f
a
il
ur
e
-
to
-
de
t
e
c
t
-
t
ype
-
2
-
d
ia
be
t
e
s
-
e
a
r
l
y
-
c
o
s
ti
ng
-
700
-
mi
ll
i
o
n
-
p
e
r
-
y
e
a
r
/
(
a
c
c
e
s
s
e
d
J
ul
.
30, 2021)
.
[
6]
M
. I
. H
a
r
r
is
, R
.
K
le
in
,
T
.
A
. W
e
lb
or
n, a
nd M
. W
.
K
nui
ma
n, “
O
ns
e
t
of
N
I
D
D
M
oc
c
u
r
s
a
t
L
e
a
s
t
4
–
7
y
r
B
e
f
or
e
C
li
ni
c
a
l
D
ia
gn
o
s
is
,”
D
ia
be
te
s
C
a
r
e
, v
o
l.
15, n
o
. 7, pp. 815
–
819, J
ul
. 1992, d
o
i:
10.2
337/
di
a
c
a
r
e
.15.7.815.
[
7]
J
.
M
.
F
o
r
be
s
a
nd
M
.
E
.
C
o
o
p
e
r
,
“
M
e
c
ha
ni
s
ms
of
D
ia
be
ti
c
C
ompl
ic
a
ti
o
ns
,”
P
hy
s
io
lo
gi
c
al
R
e
v
ie
w
s
,
vo
l.
93,
no
.
1,
pp.
137
–
1
88,
J
a
n. 2013, do
i:
10.1152/ph
y
s
r
e
v
.00045.2011.
[
8]
C
e
nt
e
r
s
f
or
D
is
e
a
s
e
C
o
nt
r
o
l
a
nd
P
r
e
v
e
nt
i
o
n
,
“
N
a
ti
o
na
l
di
a
be
t
e
s
s
ta
ti
s
ti
c
s
r
e
por
t:
e
s
ti
ma
te
s
of
di
a
be
t
e
s
a
nd
it
s
bur
de
n
in
th
e
U
ni
te
d
S
ta
te
s
, 2014,”
A
tl
a
nt
a
, 2014.
[
9]
A
me
r
i
c
a
n
D
ia
be
t
e
s
A
s
s
o
c
ia
ti
o
n
,
“
E
c
o
n
o
mi
c
C
o
s
ts
of
D
ia
b
e
te
s
in
th
e
U
.S
.
in
2017,”
D
ia
be
te
s
C
a
r
e
,
v
o
l.
41,
no
.
5,
pp.
917
–
928,
M
a
y
2018, d
o
i
:
10.2337/dci18
-
0007.
[
10]
B
.
Z
h
o
u
e
t
al
.
,
“
W
or
ld
w
id
e
t
r
e
nds
in
di
a
b
e
te
s
s
in
c
e
1980:
a
poo
l
e
d
a
na
l
y
s
is
of
751
p
o
pul
a
ti
o
n
-
ba
s
e
d
s
tu
di
e
s
w
it
h
4·
4
mi
l
li
o
n
pa
r
ti
c
ip
a
nt
s
,”
T
he
L
anc
e
t
, vo
l.
387, n
o
. 10027, pp. 1513
–
1530,
A
pr
. 2016, do
i:
10.1016
/S
0140
-
6736(
16
)
00618
-
8.
[
11]
M
.
T
.
O
ge
d
e
ngb
e
a
nd
C
.
O
.
E
gbunu,
“
C
S
E
-
D
T
F
e
a
tu
r
e
s
s
e
le
c
ti
o
n
t
e
c
hni
que
f
or
D
ia
be
t
e
s
c
la
s
s
if
i
c
a
ti
o
n,”
A
ppl
ic
at
io
n
s
o
f
M
ode
ll
in
g and Simulat
io
n
,
v
o
l.
4, pp. 101
–
109, 2020.
[
12]
M
.
S
huj
a
,
S
.
M
it
ta
l,
a
nd
M
.
Z
a
ma
n,
“
E
f
f
e
c
ti
ve
P
r
e
di
c
ti
o
n
of
T
y
pe
I
I
D
ia
be
t
e
s
M
e
ll
it
us
U
s
in
g
D
a
ta
M
in
in
g
C
la
s
s
if
ie
r
s
a
nd
S
M
O
T
E
,”
2020, pp. 195
–
211.
[
13]
G
.
S
w
a
pna
,
R
.
V
in
a
y
a
kuma
r
,
a
nd
K
.
P
.
S
o
ma
n,
“
D
ia
be
t
e
s
de
t
e
c
ti
o
n
us
in
g
d
e
e
p
l
e
a
r
ni
ng
a
lg
o
r
it
hms
,”
I
C
T
E
x
pr
e
s
s
,
vo
l.
4,
n
o
.
4,
pp. 243
–
246, De
c
. 2018, d
oi
:
10.1016/j
.i
c
t
e
.2018.10.005.
[
14]
T
.
H
a
nn
e
,
“
O
n
th
e
c
la
s
s
i
f
ic
a
ti
o
n
of
M
C
D
M
li
te
r
a
tu
r
e
,”
in
P
r
oc
e
e
di
ngs
o
f
th
e
5t
h
W
or
k
s
hop
o
f
th
e
D
G
O
R
-
W
or
k
in
g
G
r
oup.
M
ul
ti
c
r
it
e
r
ia
O
pt
imi
z
at
io
n
and De
c
is
io
n T
he
o
r
y
, 1995, pp. 113
–
120.
[
15]
A
.
M
a
r
da
ni
,
E
.
K
.
Z
a
v
a
ds
ka
s
,
Z
.
K
ha
li
f
a
h,
A
.
J
us
o
h,
a
nd
K
.
M
.
N
o
r
,
“
M
ul
ti
pl
e
c
r
i
te
r
ia
de
c
is
i
o
n
-
ma
ki
ng
te
c
hni
qu
e
s
in
tr
a
ns
po
r
ta
ti
o
n
s
y
s
t
e
ms
:
A
s
y
s
t
e
ma
ti
c
r
e
vi
e
w
of
th
e
s
ta
te
of
th
e
a
r
t
li
te
r
a
tu
r
e
,”
T
R
A
N
S
P
O
R
T
,
vo
l.
31,
no
.
3,
pp.
359
–
385,
D
e
c
.
2015, do
i:
10.3846/16484142.
2015.1121517.
[
16]
P
.
A
dhi
ka
r
y
a
nd
S
.
K
undu,
“
M
C
D
A
o
r
M
C
D
M
ba
s
e
d
s
e
le
c
ti
o
n
of
t
r
a
ns
mi
s
s
io
n
li
ne
c
o
ndu
c
t
o
r
:
S
ma
ll
h
y
dr
o
p
o
w
e
r
pr
oj
e
c
t
pl
a
nni
ng a
nd de
ve
l
o
pm
e
nt
,”
I
n
te
r
nat
io
nal
J
our
nal
of
E
ngi
ne
e
r
i
ng
R
e
s
e
ar
c
h and A
ppl
ic
at
io
ns
, v
o
l.
4, n
o
. 2, pp. 357
–
361, 2014.
[
17]
T
.
L
. S
a
a
t
y
, “
O
pt
im
i
z
a
ti
o
n b
y
t
he
A
na
l
y
ti
c
H
i
e
r
a
r
c
h
y
P
r
o
c
e
s
s
,”
J
a
n. 1979. do
i:
10.21236/ADA
214804.
[
18]
V
.
M
is
hr
a
,
C
.
S
a
mue
l,
a
nd
S
.
S
.K
,
“
U
s
e
of
M
a
c
hi
n
e
L
e
a
r
n
in
g
to
P
r
e
d
ic
t
th
e
O
ns
e
t
of
D
ia
be
te
s
,”
I
nt
e
r
nat
io
nal
J
our
nal
of
R
e
c
e
nt
adv
anc
e
s
i
n M
e
c
hani
c
al
E
ngi
ne
e
r
in
g
, v
ol
. 4, n
o
. 2, pp. 9
–
14, M
a
y
2015, d
o
i:
10.14810/i
jm
e
c
h.2015.4202.
[
19]
S
.
A
.
M
o
r
ga
n
e
t
al
.
,
“
P
r
e
v
a
le
n
c
e
a
nd
c
o
r
r
e
la
t
e
s
of
di
a
b
e
t
e
s
a
nd
it
s
c
o
m
o
r
bi
d
it
i
e
s
in
f
o
ur
G
ul
f
C
oo
pe
r
a
ti
o
n
C
o
unc
il
c
o
un
tr
ie
s
:
e
v
id
e
nc
e
f
r
o
m
th
e
W
or
ld
H
e
a
lt
h
S
ur
ve
y
P
lu
s
,”
J
our
nal
of
E
pi
de
m
io
lo
gy
and
C
om
m
uni
ty
H
e
al
th
,
vo
l.
73,
n
o
.
7,
pp.
630
–
636,
J
ul
.
2019, do
i:
10.1136/j
e
c
h
-
2018
-
211187.
[
20]
D
.
A
.
A
.
G
.
S
in
gh,
E
.
J
.
L
e
a
v
li
n
e
,
a
nd
B
.
S
.
B
a
ig
,
“
D
ia
b
e
t
e
s
pr
e
di
c
ti
o
n
us
in
g
m
e
di
c
a
l
da
ta
,”
J
our
nal
of
C
om
put
at
io
nal
I
nt
e
ll
ig
e
nc
e
i
n B
io
in
f
or
m
at
ic
s
, v
o
l.
10, n
o
. 1, pp. 1
–
8, 2017.
[
21]
K
. V
id
h
y
a
a
nd R
. S
ha
nmuga
la
ks
hmi
, “
D
e
e
p
l
e
a
r
ni
ng ba
s
e
d bi
g
me
di
c
a
l
da
ta
a
na
l
y
ti
c
m
o
d
e
l
f
or
di
a
b
e
t
e
s
c
o
mpl
i
c
a
ti
o
n p
r
e
di
c
ti
on,”
J
our
nal
of
A
m
bi
e
nt
I
nt
e
ll
ig
e
nc
e
and
H
um
ani
z
e
d
C
om
put
in
g
,
vo
l.
11,
n
o
.
11,
pp.
5691
–
5702,
N
ov
.
2020,
d
o
i
:
10.1007/s
12
652
-
020
-
01930
-
2.
[
22]
S
.
D
in
g,
Z
.
L
i,
X
.
L
iu
,
H
.
H
ua
ng,
a
nd
S
.
Y
a
ng,
“
D
ia
be
ti
c
c
ompl
ic
a
ti
o
n
pr
e
di
c
ti
o
n
us
in
g
a
s
im
il
a
r
it
y
-
e
nha
nc
e
d
la
te
n
t
D
ir
ic
hl
e
t
a
ll
oc
a
ti
o
n m
o
d
e
l,
”
I
nf
or
m
at
io
n Sc
ie
nc
e
s
, v
o
l.
499, pp. 12
–
24,
O
c
t.
2019, d
o
i:
10.1016/j
.i
ns
.2019.05.037.
[
23]
B
.
L
iu
,
Y
.
L
i,
S
.
G
h
o
s
h,
Z
.
S
un,
K
.
N
g,
a
nd
J
.
H
u,
“
C
o
mpl
i
c
a
ti
o
n
R
is
k
P
r
of
il
in
g
in
D
ia
b
e
t
e
s
C
a
r
e
:
A
B
a
y
e
s
ia
n
M
ul
ti
-
T
a
s
k
a
nd
F
e
a
tu
r
e
R
e
la
ti
o
ns
hi
p
L
e
a
r
ni
ng
A
ppr
o
a
c
h,”
I
E
E
E
T
r
ans
ac
ti
ons
on
K
now
le
dge
and
D
at
a
E
ngi
ne
e
r
in
g
,
v
o
l.
32,
no
.
7,
pp.
1276
–
1289, J
ul
. 2020, do
i:
10.110
9/
T
K
D
E
.2019.2904060.
[
24]
M
.
D
a
ğde
v
ir
e
n,
S
.
Y
a
v
u
z
,
a
nd
N
.
K
ıl
ın
ç
,
“
W
e
a
po
n
s
e
l
e
c
ti
o
n
us
in
g
th
e
A
H
P
a
nd
T
O
P
S
I
S
m
e
th
o
ds
unde
r
f
uz
z
y
e
n
vi
r
o
nm
e
nt
,”
E
x
pe
r
t
Sy
s
te
m
s
w
it
h A
ppl
ic
at
io
ns
, vo
l.
36, n
o
. 4, pp. 8143
–
815
1, M
a
y
2009, d
o
i
:
10.1016/j
.
e
s
w
a
.2008.10.016.
[
25]
Z
.
G
üngör
,
G
.
S
e
r
ha
dl
ı
o
ğl
u,
a
nd
S
.
E
.
K
e
s
e
n,
“
A
f
u
z
z
y
A
H
P
a
ppr
o
a
c
h
t
o
pe
r
s
o
nn
e
l
s
e
l
e
c
t
i
o
n
pr
o
bl
e
m,”
A
ppl
ie
d
Sof
t
C
o
m
put
in
g
,
vo
l.
9, n
o
. 2, pp. 641
–
646, M
a
r
. 2009, d
o
i:
10.1016/j
.a
s
o
c
.2008.
09.003.
[
26]
H
.
S
.
K
ıl
ıç
a
nd
E
.
Ç
e
v
ik
c
a
n,
“
J
o
b
s
e
l
e
c
ti
o
n
ba
s
e
d
o
n
f
u
z
z
y
A
H
P
:
a
n
in
ve
s
ti
ga
ti
o
n
in
c
lu
di
ng
th
e
s
tu
d
e
nt
s
of
I
s
ta
nbul
T
e
c
hni
c
a
l
U
ni
ve
r
s
it
y
M
a
na
ge
m
e
nt
F
a
c
u
lt
y
,”
I
nt
e
r
nat
io
nal
j
our
nal
of
bus
i
ne
s
s
and manage
m
e
nt
s
tu
di
e
s
, v
ol
. 3, n
o
. 1, pp. 173
–
182, 2011.
[
27]
C
.
K
a
hr
a
ma
n
a
nd
İ
.
K
a
y
a
,
“
A
f
u
z
z
y
mul
t
ic
r
it
e
r
ia
m
e
th
o
d
o
l
og
y
f
or
s
e
l
e
c
ti
o
n
a
m
o
ng
e
n
e
r
g
y
a
l
te
r
na
ti
v
e
s
,”
E
x
pe
r
t
Sy
s
te
m
s
w
it
h
A
ppl
ic
at
io
ns
, vo
l.
37, n
o
. 9, pp. 6270
–
6281,
S
e
p. 2010, d
o
i
:
10.
1016/j
.e
s
w
a
.2010.02.095.
[
28]
H
.
S
.
K
ıl
ıç
,
“
A
f
u
z
z
y
A
H
P
ba
s
e
d
p
e
r
f
or
ma
nc
e
a
s
s
e
s
s
me
nt
s
y
s
t
e
m
f
or
th
e
s
tr
a
t
e
gi
c
pl
a
n
of
T
ur
ki
s
h
M
uni
c
ip
a
li
ti
e
s
,”
I
nt
e
r
nat
io
nal
J
our
nal
of
B
us
in
e
s
s
and M
anage
m
e
nt
St
udi
e
s
, v
o
l.
3, n
o
. 2, pp.
77
–
86, 2011.
[
29]
H
.
S
.
K
ıl
ıç
a
nd
E
.
Ç
e
vi
kc
a
n,
“
A
H
y
b
r
id
W
e
ig
ht
in
g
M
e
th
o
d
o
l
og
y
f
or
P
e
r
f
o
r
ma
n
c
e
A
s
s
e
s
s
me
nt
in
T
ur
k
is
h
M
uni
c
ip
a
li
ti
e
s
,”
20
12,
pp. 354
–
363.
[
30]
I
.
C
ha
mo
d
r
a
ka
s
,
D
.
B
a
ti
s
,
a
nd
D
.
M
a
r
ta
ko
s
,
“
S
upp
li
e
r
s
e
le
c
ti
o
n
in
e
l
e
c
tr
o
ni
c
ma
r
k
e
tp
la
c
e
s
us
in
g
s
a
ti
s
f
i
c
in
g
a
nd
f
u
z
z
y
A
H
P
,”
E
x
pe
r
t
Sy
s
te
m
s
w
it
h A
ppl
ic
at
io
ns
, vo
l.
37, n
o
. 1, pp. 490
–
498, J
a
n. 2010, do
i:
10.1016/j
.
e
s
w
a
.2009.05.043.
[
31]
O
.
K
il
in
c
c
i
a
nd
S
.
A
.
O
n
a
l,
“
F
u
z
z
y
A
H
P
a
ppr
o
a
c
h
f
or
s
uppl
ie
r
s
e
l
e
c
ti
o
n
in
a
w
a
s
hi
ng
ma
c
hi
n
e
c
o
mpa
n
y
,”
E
x
pe
r
t
Sy
s
te
m
s
w
it
h
A
ppl
ic
at
io
ns
, vo
l.
38, n
o
. 8, pp. 9656
–
9664, Aug. 2011, d
o
i:
10
.1016/j
.e
s
w
a
.2011.01.159.
[
32]
K
.
S
ha
w
,
R
.
S
ha
nka
r
,
S
.
S
.
Y
a
da
v
,
a
nd
L
.
S
.
T
ha
kur
,
“
S
up
pl
ie
r
s
e
l
e
c
ti
o
n
us
in
g
f
u
z
z
y
A
H
P
a
nd
f
u
z
z
y
mul
ti
-
o
bj
e
c
t
i
v
e
li
ne
a
r
pr
o
g
r
a
mm
in
g
f
o
r
de
ve
l
o
pi
ng
l
o
w
c
a
r
b
o
n
s
uppl
y
c
ha
in
,”
E
x
pe
r
t
Sy
s
te
m
s
w
it
h
A
ppl
ic
at
io
ns
,
vo
l.
39,
n
o
.
9,
pp.
8182
–
8192,
J
ul
.
2012, do
i:
10.1016/j
.
e
s
w
a
.2012.01.149.
[
33]
F
.
A
r
ik
a
n,
“
A
n
in
te
r
a
c
ti
ve
s
ol
ut
i
o
n
a
ppr
o
a
c
h
f
or
mul
ti
pl
e
o
bj
e
c
ti
ve
s
uppl
i
e
r
s
e
l
e
c
ti
o
n
pr
o
b
le
m
w
i
th
f
u
z
z
y
pa
r
a
me
t
e
r
s
,”
J
our
nal
of
I
nt
e
ll
ig
e
nt
M
anuf
ac
tu
r
in
g
, v
o
l.
26, n
o
. 5, pp. 989
–
998, O
c
t.
201
5, do
i:
10.1007/s
10845
-
013
-
0782
-
6.
[
34]
M
.
M
.
F
.
I
s
la
m, R
.
F
e
r
do
us
i,
S
.
R
a
hma
n,
a
nd
H
.
Y
.
B
us
h
r
a
,
“
L
ik
e
li
h
oo
d
P
r
e
di
c
ti
o
n
of
D
ia
be
t
e
s
a
t
E
a
r
l
y
S
ta
g
e
U
s
in
g
D
a
ta
M
i
ni
ng
T
e
c
hni
qu
e
s
,”
2020, pp. 113
–
125.
Evaluation Warning : The document was created with Spire.PDF for Python.
I
S
S
N
:
2502
-
4752
I
n
do
n
e
s
i
a
n
J
E
l
e
c
E
n
g
&
C
o
m
p
S
c
i
,
Vo
l
.
25
,
N
o
.
2
,
F
e
b
r
ua
r
y
20
22
:
1167
-
1176
1176
[
35]
M
. T
.
G
a
r
c
ía
-
O
r
dá
s
, C
. B
e
na
v
id
e
s
, J
. A
. B
e
ní
t
e
z
-
A
ndr
a
d
e
s
,
H
.
A
la
iz
-
M
o
r
e
tó
n, a
nd I
.
G
a
r
c
ía
-
R
o
dr
íg
u
e
z
, “
D
ia
be
te
s
d
e
t
e
c
ti
o
n u
s
in
g
de
e
p
le
a
r
ni
ng
t
e
c
hni
qu
e
s
w
it
h
ove
r
s
a
mpl
in
g
a
nd
f
e
a
tu
r
e
a
ugme
nt
a
ti
o
n,”
C
om
put
e
r
M
e
th
od
s
and
P
r
ogr
am
s
in
B
io
m
e
di
c
in
e
,
vo
l.
202, p. 105968, Apr
. 2021, do
i:
10.1016
/j
.c
mpb.2021.105968.
[
36]
B
. A
l
-
B
a
nd
e
r
, Y
.
A
. F
a
di
l,
a
nd
H
. M
a
hdi
, “
M
ul
ti
-
C
r
it
e
r
ia
D
e
c
i
s
io
n S
upp
or
t
S
y
s
t
e
m
f
or
L
ung
C
a
n
c
e
r
P
r
e
d
ic
t
i
o
n,”
I
O
P
C
onf
e
r
e
nc
e
Se
r
ie
s
:
M
at
e
r
ia
ls
S
c
ie
nc
e
and E
ngi
ne
e
r
in
g
, v
ol
. 1076, n
o
. 1, p.
012036, F
e
b. 2021, d
o
i:
10.1088/1757
-
899X/1076/1/
012036.
[
37]
P
.
J
.
M
.
v
a
n
L
a
a
r
h
ove
n
a
nd
W
.
P
e
dr
y
c
z
,
“
A
f
u
z
z
y
e
x
t
e
ns
i
o
n
of
S
a
a
t
y
’
s
pr
i
o
r
it
y
th
e
o
r
y
,”
F
uz
z
y
Se
ts
and
Sy
s
te
m
s
,
v
o
l.
11,
n
o
.
1
–
3,
pp. 229
–
241, 1983, do
i:
10.1016/
S
0165
-
0114(
83
)
80082
-
7.
[
38]
D
.
E
.
R
ume
lh
a
r
t,
G
.
E
.
H
in
t
o
n,
a
nd
R
.
J
.
W
il
li
a
ms
,
“
L
e
a
r
n
in
g
r
e
pr
e
s
e
n
ta
ti
o
ns
b
y
ba
c
k
-
p
r
o
pa
ga
ti
ng
e
r
r
o
r
s
,”
N
at
ur
e
,
vo
l.
323,
no
.
6088, pp. 533
–
536, Oc
t.
1986, d
o
i:
10.1038/323533a0.
[
39]
V
. N
. V
a
pni
k,
St
at
is
ti
c
al
L
e
ar
ni
ng T
he
or
y
, 1
s
t
e
d. N
e
w
Y
or
k:
W
il
e
y
, 1998.
[
40]
N
.
C
r
is
ti
a
ni
ni
a
nd
J
.
S
ha
w
e
-
T
a
y
l
o
r
,
A
n
I
nt
r
oduc
ti
on
to
Suppor
t
V
e
c
to
r
M
ac
hi
ne
s
and
O
th
e
r
K
e
r
n
e
l
-
bas
e
d
L
e
a
r
ni
ng
M
e
th
ods
.
C
a
mbr
id
ge
U
ni
v
e
r
s
it
y
P
r
e
s
s
, 2000.
B
I
OG
RA
P
HI
E
S
OF
AU
T
HO
RS
S
a
b
a
h
A
n
w
e
r
A
bdu
l
k
a
re
em
re
c
e
i
v
e
d
t
h
e
B
.
Sc
.
d
e
g
re
e
i
n
c
o
m
p
u
t
e
r
an
d
s
o
f
t
w
ar
e
e
n
g
i
n
e
e
ri
n
g
fro
m
t
h
e
t
h
e
c
o
l
l
e
g
e
o
f
e
n
g
i
n
e
e
ri
n
g
,
U
n
i
v
e
r
s
i
t
y
o
f
D
i
y
al
a,
I
ra
q
,
t
h
e
M.
Sc
.
d
e
g
re
e
i
n
s
o
f
t
w
are
E
n
g
i
n
e
e
ri
n
g
fro
m
Ch
o
n
g
q
i
n
g
U
n
i
v
e
r
s
i
t
y
Ch
i
n
a.
H
e
r
re
s
e
arc
h
i
n
t
e
re
s
t
s
i
n
c
l
u
d
e
s
o
f
t
c
o
m
p
u
t
i
n
g
,
an
d
i
n
t
e
l
l
i
g
e
n
t
s
y
s
t
e
m
s
.
Sh
e
c
an
b
e
c
o
n
t
ac
t
e
d
a
t
e
m
ai
l
:
s
b
h
_
an
w
ar@
u
o
d
i
y
al
a.
e
d
u
.
i
q
.
Hu
s
s
i
e
n
Y
o
s
s
i
f
R
a
d
h
i
H
e
g
o
t
t
h
e
BS
c
d
eg
r
ee
i
n
E
l
ec
t
ro
n
i
c
E
n
g
i
n
ee
ri
n
g
fr
o
m
Co
l
l
eg
e
o
f
E
n
g
i
n
ee
ri
n
g
,
U
n
i
v
e
rs
i
t
y
o
f
D
i
y
al
a,
I
raq
.
H
e
al
s
o
g
o
t
t
h
e
MS
c
d
eg
r
ee
i
n
E
l
ec
t
ro
n
i
c
an
d
C
o
mm
u
n
i
c
at
i
o
n
fr
o
m
Co
l
l
eg
e
o
f
E
n
g
i
n
ee
r
i
n
g
,
Mu
s
t
an
s
i
ri
y
a
U
n
i
v
e
rs
i
t
y
,
I
raq
.
H
e
i
s
w
o
rk
i
n
g
as
a
l
ec
t
u
r
e
r
at
Co
m
p
u
t
e
r
E
n
g
i
n
ee
ri
n
g
D
e
p
art
me
n
t
.
A
l
s
o
,
h
e
i
s
ap
p
o
i
n
t
e
d
as
me
d
i
a
d
e
p
art
men
t
an
d
w
e
b
s
i
t
e
m
a
n
ag
e
r
f
o
r
t
h
e
c
o
l
l
e
g
e
o
f
e
n
g
i
n
ee
ri
n
g
,
d
i
y
a
l
a
u
n
i
v
e
rs
i
t
y
s
i
n
ce
2
0
1
4
.
H
i
s
r
e
s
e
ar
ch
i
n
t
e
r
e
s
t
s
are
:
(Cr
y
p
t
o
g
rap
h
y
,
W
i
r
e
l
e
s
s
s
en
s
o
r
s
ecu
ri
t
y
,
Im
ag
e
p
ro
ce
s
s
i
n
g
,
a
n
d
i
n
t
el
l
i
g
en
t
s
y
s
t
em
s
)
.
H
e
c
a
n
b
e
c
o
n
t
ac
t
e
d
at
em
a
i
l
:
h
u
s
s
i
e
n
.
y
o
s
s
i
f1
9
8
2
0
@
g
m
ai
l
.
c
o
m
.
D
r.
Y
o
u
s
ra
A
hm
ed
F
a
di
l
h
as
r
ece
i
v
e
d
h
e
r
BS
c
i
n
c
o
m
p
u
t
e
r
s
c
i
en
ce
fr
o
m
Co
m
p
u
t
e
r
s
c
i
en
c
e
D
e
p
art
me
n
t
,
U
n
i
v
e
rs
i
t
y
o
f
T
ech
n
o
l
o
g
y
,
I
raq
i
n
1
9
9
7
.
T
h
e
n
s
h
e
c
o
m
p
l
e
t
e
d
h
e
r
MS
c
i
n
Co
m
p
u
t
e
r
S
c
i
en
ce
fr
o
m
In
fo
r
m
at
i
o
n
i
n
s
t
i
t
u
t
e
fo
r
p
o
s
t
g
rad
u
at
e
s
t
u
d
i
e
s
,
I
raq
i
n
200
5
,
an
d
s
h
e
w
o
r
k
e
d
at
U
n
i
v
e
rs
i
t
y
o
f
D
i
y
a
l
a,
a
n
d
s
h
e
r
ece
i
v
e
d
a
P
h
.
D
d
e
g
r
ee
i
n
A
rt
i
fi
ci
al
I
n
t
e
l
l
i
g
e
n
ce
fr
o
m
t
h
e
U
n
i
v
e
rs
i
t
y
o
f
Be
s
an
ç
o
n
,
Fran
ce
i
n
2
0
1
7
.
D
u
ri
n
g
t
h
at
t
i
me
,
s
h
e
p
u
b
l
i
s
h
e
d
m
an
y
p
ap
e
rs
i
n
I
n
t
e
rn
at
i
o
n
a
l
Co
n
f
e
r
e
n
ce
s
an
d
J
o
u
rn
a
l
s
.
Cu
rren
t
l
y
,
s
h
e
c
o
n
t
i
n
u
e
s
t
o
w
o
rk
at
U
n
i
v
e
rs
i
t
y
o
f
D
i
y
a
l
a
.
S
h
e
c
a
n
b
e
c
o
n
t
ac
t
ed
at
em
ai
l
:
Y
o
u
s
ra.
co
m
p
@
u
o
d
i
y
a
l
a.
e
d
u
.
i
q
.
D
r.
Hu
s
s
a
i
n
F
a
l
i
h
M
a
h
d
i
recei
v
ed
t
h
e
P
h
D
fro
m
u
n
i
v
e
rs
i
t
y
o
f
K
e
b
an
g
s
aan
Mal
a
y
s
i
a
an
d
Mas
t
e
r
o
f
Sc
i
en
ce
fro
m
U
n
i
v
e
rs
i
t
y
o
f
T
ec
h
n
o
l
o
g
y
,
Bag
d
a
d
,
I
raq
.
H
e
i
s
I
E
E
E
Re
g
i
o
n
1
0
Y
o
u
n
g
Pro
f
e
s
s
i
o
n
al
C
o
mm
i
t
t
ee
So
u
t
h
-
E
as
t
A
s
i
a
co
o
r
d
i
n
at
o
r
(2
0
1
7
-
2
0
1
9
),
I
E
E
E
Re
g
i
o
n
1
0
H
u
m
an
i
t
ari
an
a
c
t
i
v
i
t
i
e
s
c
o
mmi
t
t
ee
(2
0
1
7
-
2
0
2
0
),
IE
E
E
P
E
S
Y
o
u
n
g
Pr
o
f
e
s
s
i
o
n
a
l
Co
mm
i
t
t
ee
a
c
ad
emi
c
l
e
ad
(2
0
1
7
-
2
0
2
0
),
I
E
E
E
IA
S
Ch
a
p
t
e
rs
A
re
a
Ch
ai
r
,
R1
0
So
u
t
h
e
as
t
A
s
i
a,
A
u
s
t
ral
i
a,
a
n
d
Pa
c
i
f
i
c
(2
0
1
8
-
2
0
1
9
),
an
d
IE
E
E
R
eg
i
o
n
1
0
PE
S
s
t
u
d
en
t
s
Ch
ap
t
e
rs
Ch
ai
r
(2
0
1
9
-
2
0
2
0
),
I
E
E
E
PE
S
D
a
y
2
0
1
9
G
l
o
b
al
Ch
ai
r,
an
d
I
E
E
E
H
A
C
E
v
e
n
t
c
o
mm
i
t
t
ee
mem
b
e
r
2
0
1
9
-
2
0
2
0
.
H
e
c
a
n
b
e
c
o
n
t
ac
t
e
d
at
em
ai
l
:
H
u
s
s
ai
n
.
m
ah
d
i
@
i
eee
.
o
rg
.
Evaluation Warning : The document was created with Spire.PDF for Python.