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Science
Vo
l.
3
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No
.
1
,
A
p
r
il
20
2
5
,
p
p
.
459
~
468
I
SS
N:
2
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4
7
52
,
DOI
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0
.
1
1
5
9
1
/ijee
cs
.v
3
8
.
i
1
.
pp
459
-
4
6
8
459
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22
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30
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Dia
b
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d
e
tec
ti
o
n
a
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d
p
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ictio
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a
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c
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m
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to
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v
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ra
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ters
a
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d
a
rti
ficia
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in
telli
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n
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(AI)
tec
h
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iq
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e
s
in
d
iab
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tes
a
ss
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ss
m
e
n
t,
id
e
n
ti
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n
.
T
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tec
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n
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d
m
o
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e
v
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lu
a
ti
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n
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Th
e
stu
d
y
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ti
li
z
e
s
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sy
ste
m
a
ti
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re
v
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a
p
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h
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a
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ly
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g
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n
t
li
tera
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b
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se
d
d
iab
e
tes
d
e
tec
ti
o
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d
p
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o
n
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fo
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o
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v
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ta
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n
d
m
a
c
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rn
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(
M
L)
tec
h
n
iq
u
e
s.
F
i
n
d
i
n
g
s
re
v
e
a
l
a
sig
n
if
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t
lac
k
o
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te
g
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ti
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f
d
iv
e
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d
a
ta
s
o
u
rc
e
s,
li
m
it
e
d
f
o
c
u
s
o
n
e
a
rly
d
e
tec
ti
o
n
stra
te
g
ies
,
a
n
d
c
h
a
ll
e
n
g
e
s
in
m
o
d
e
l
e
v
a
lu
a
ti
o
n
.
Th
e
st
u
d
y
c
o
n
c
lu
d
e
s
wit
h
a
p
ro
p
o
se
d
in
n
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v
a
ti
v
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m
e
wo
rk
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m
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re
a
c
c
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te an
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z
e
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d
ia
b
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tes
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g
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e
tes
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se
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d
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ig
h
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g
h
ti
n
g
t
h
e
p
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ten
ti
a
l
o
f
AI
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d
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n
h
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a
lt
h
c
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terv
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s.
Th
is
re
se
a
rc
h
u
n
d
e
rsc
o
re
s
th
e
imp
o
rtan
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e
o
f
c
o
m
p
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n
si
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d
a
ta
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teg
ra
ti
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ro
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v
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ti
o
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th
o
d
s i
n
e
n
h
a
n
c
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g
d
iab
e
tes
d
e
tec
ti
o
n
a
n
d
p
re
d
icti
o
n
.
K
ey
w
o
r
d
s
:
Ar
tific
ial
in
tellig
en
ce
Diab
etes d
etec
tio
n
Hea
lth
ca
r
e
Ma
ch
in
e
lear
n
in
g
Mu
ltimo
d
al
f
r
am
ewo
r
k
Pre
d
ictio
n
T
h
is i
s
a
n
o
p
e
n
a
c
c
e
ss
a
rticle
u
n
d
e
r th
e
CC B
Y
-
SA
li
c
e
n
se
.
C
o
r
r
e
s
p
o
nd
ing
A
uth
o
r
:
Gu
r
u
r
aj
N
.
Ku
lk
a
r
n
i
Dep
ar
tm
en
t o
f
C
o
m
p
u
ter
Scie
n
ce
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p
licatio
n
,
Sh
r
i Jag
d
is
h
p
r
asad
J
h
ab
ar
m
al
T
ib
r
ewa
la
Un
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er
s
ity
Vid
y
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ag
r
i,
J
h
u
n
jh
u
n
u
B
is
au
R
o
ad
,
C
h
u
d
ela,
Dis
tr
ict
-
J
h
u
n
j
h
u
n
u
,
R
ajasth
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-
3
3
3
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u
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@
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m
ail.
co
m
1.
I
NT
RO
D
UCT
I
O
N
Diab
etes,
a
ch
r
o
n
ic
m
etab
o
lic
d
is
o
r
d
er
,
af
f
ec
ts
m
illi
o
n
s
o
f
p
eo
p
le
wo
r
ld
wid
e,
m
ak
in
g
it
a
s
ig
n
if
ican
t
p
u
b
lic
h
ea
lth
co
n
ce
r
n
[
1
]
.
I
t
is
ch
ar
ac
ter
ized
b
y
elev
ated
b
lo
o
d
g
l
u
co
s
e
lev
els
r
esu
ltin
g
f
r
o
m
eith
er
in
s
u
f
f
icien
t
in
s
u
lin
p
r
o
d
u
ctio
n
o
r
th
e
b
o
d
y
’
s
in
ab
ilit
y
to
u
s
e
in
s
u
lin
ef
f
ec
tiv
ely
.
T
h
er
e
a
r
e
p
r
im
ar
ily
two
ty
p
es
o
f
d
iab
etes:
T
y
p
e
1
a
n
d
T
y
p
e
2
as
p
r
esen
ted
in
Fig
u
r
e
1
.
T
y
p
e
1
d
iab
etes,
o
f
ten
d
iag
n
o
s
ed
in
ch
ild
h
o
o
d
o
r
ad
o
lescen
ce
,
o
cc
u
r
s
wh
en
th
e
im
m
u
n
e
s
y
s
tem
m
is
tak
en
ly
attac
k
s
an
d
d
estro
y
s
in
s
u
lin
-
p
r
o
d
u
cin
g
ce
lls
in
th
e
p
an
cr
ea
s
,
lead
i
n
g
t
o
in
s
u
lin
d
ef
icien
cy
[
2
]
.
I
n
co
n
tr
ast,
T
y
p
e
2
d
iab
etes,
m
o
r
e
c
o
m
m
o
n
in
ad
u
lts
,
d
ev
el
o
p
s
wh
en
th
e
b
o
d
y
b
ec
o
m
es
r
esis
tan
t
to
in
s
u
lin
o
r
d
o
esn
’
t
p
r
o
d
u
ce
en
o
u
g
h
i
n
s
u
lin
to
m
ain
t
ain
n
o
r
m
al
g
lu
co
s
e
lev
els
[
3
]
.
T
h
e
p
r
e
v
alen
ce
o
f
d
iab
etes
h
as
b
ee
n
s
tead
ily
r
is
in
g
,
attr
ib
u
te
d
to
f
a
cto
r
s
s
u
ch
as
s
ed
en
tar
y
life
s
ty
les,
u
n
h
ea
lth
y
d
iets
,
o
b
esit
y
,
an
d
g
e
n
etic
p
r
ed
is
p
o
s
itio
n
[
4
]
.
Acc
o
r
d
in
g
t
o
g
lo
b
al
h
ea
lth
s
tatis
tic
s
,
d
iab
etes
af
f
ec
ts
ap
p
r
o
x
im
atel
y
1
0
%
o
f
th
e
a
d
u
lt
p
o
p
u
latio
n
wo
r
ld
wid
e,
with
T
y
p
e
2
d
iab
etes
ac
co
u
n
tin
g
f
o
r
th
e
m
ajo
r
ity
o
f
ca
s
es
[
5
]
.
T
h
e
s
y
m
p
to
m
s
o
f
d
iab
etes
ca
n
v
a
r
y
b
u
t
o
f
ten
in
clu
d
e
in
cr
ea
s
ed
th
ir
s
t
an
d
h
u
n
g
e
r
,
f
r
eq
u
e
n
t
u
r
in
atio
n
,
u
n
ex
p
lain
e
d
weig
h
t
lo
s
s
,
f
atig
u
e,
b
l
u
r
r
ed
v
is
io
n
,
an
d
s
lo
w
wo
u
n
d
h
ea
lin
g
[
6
]
.
T
h
e
ca
u
s
es
o
f
d
iab
etes
ar
e
m
u
ltifa
cto
r
ial,
in
v
o
lv
in
g
a
co
m
p
lex
i
n
ter
p
lay
o
f
g
e
n
etic,
en
v
ir
o
n
m
en
tal,
an
d
life
s
ty
le
f
ac
to
r
s
.
Gen
etic
p
r
ed
is
p
o
s
itio
n
,
o
b
esit
y
,
lack
o
f
p
h
y
s
ical
ac
tiv
ity
,
p
o
o
r
d
iet,
an
d
a
g
in
g
ar
e
am
o
n
g
t
h
e
k
ey
r
is
k
f
ac
to
r
s
ass
o
ciate
d
with
th
e
d
ev
elo
p
m
e
n
t o
f
d
ia
b
etes [
7
]
.
Evaluation Warning : The document was created with Spire.PDF for Python.
I
SS
N
:
2
5
0
2
-
4
7
52
In
d
o
n
esian
J
E
lec
E
n
g
&
C
o
m
p
Sci
,
Vo
l.
3
8
,
No
.
1
,
Ap
r
il
20
2
5
:
4
5
9
-
4
6
8
460
Fig
u
r
e
1
.
T
y
p
es o
f
d
iab
etes
On
e
o
f
t
h
e
m
o
s
t
c
o
n
ce
r
n
i
n
g
asp
ec
ts
o
f
d
iab
etes
is
its
p
o
ten
tial
co
m
p
licatio
n
s
,
wh
ich
ca
n
af
f
ec
t
v
ar
io
u
s
o
r
g
an
s
a
n
d
s
y
s
tem
s
in
th
e
b
o
d
y
.
L
o
n
g
-
ter
m
co
m
p
licatio
n
s
m
ay
in
cl
u
d
e
ca
r
d
i
o
v
ascu
lar
d
is
ea
s
es,
k
id
n
ey
d
am
a
g
e,
n
er
v
e
d
am
ag
e
(
n
eu
r
o
p
ath
y
)
,
e
y
e
d
am
ag
e
(
r
etin
o
p
ath
y
)
,
f
o
o
t
u
lcer
s
,
an
d
in
cr
ea
s
ed
r
is
k
o
f
in
f
ec
tio
n
s
[
8
]
.
Diab
etes
also
s
i
g
n
if
ican
tly
c
o
n
tr
ib
u
tes
to
m
o
r
b
id
ity
an
d
m
o
r
tality
r
ates g
lo
b
ally
,
with
a
n
o
tab
le
p
r
o
p
o
r
tio
n
o
f
d
ea
th
s
attr
ib
u
te
d
to
d
iab
etes
-
r
elate
d
co
m
p
licatio
n
s
[
9
]
.
Diag
n
o
s
in
g
d
iab
et
es
in
v
o
lv
es
v
ar
io
u
s
test
s
an
d
ass
e
s
s
m
en
ts
to
m
ea
s
u
r
e
b
lo
o
d
g
lu
co
s
e
lev
els,
in
s
u
lin
lev
els,
an
d
o
t
h
er
r
elev
a
n
t
p
ar
am
eter
s
[
1
0
]
.
C
o
m
m
o
n
d
iag
n
o
s
tic
test
s
in
cl
u
d
e
f
asti
n
g
b
lo
o
d
g
l
u
co
s
e
(
FB
G)
tes
t
[
1
1
]
,
o
r
al
g
lu
co
s
e
to
ler
an
ce
test
(
OGT
T
)
[
1
2
]
,
g
ly
ca
te
d
h
e
m
o
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in
(
Hb
A1
c)
test
[
1
3
]
,
an
d
r
a
n
d
o
m
b
lo
o
d
g
lu
co
s
e
test
[
1
4
]
.
T
h
ese
t
ests
h
elp
h
ea
lth
ca
r
e
p
r
o
f
ess
io
n
a
ls
d
eter
m
in
e
wh
et
h
er
an
in
d
iv
id
u
al
h
as
d
ia
b
etes,
p
r
ed
iab
etes,
o
r
n
o
r
m
al
g
l
u
c
o
s
e
m
etab
o
lis
m
.
I
n
r
ec
en
t
y
ea
r
s
,
m
ac
h
in
e
lear
n
i
n
g
(
ML
)
[
1
5
]
,
[
1
6
]
an
d
d
ee
p
lear
n
in
g
(
DL
)
[
1
7
]
,
[
1
8
]
a
p
p
r
o
a
ch
es
h
av
e
em
er
g
ed
as v
alu
ab
le
to
o
ls
f
o
r
d
etec
tin
g
an
d
p
r
ed
ictin
g
d
iab
etes.
T
h
ese
co
m
p
u
tatio
n
al
tech
n
iq
u
es
an
aly
ze
lar
g
e
d
atasets
co
n
tain
in
g
d
em
o
g
r
ap
h
ic
in
f
o
r
m
atio
n
(
s
u
ch
as
ag
e,
s
ex
)
[
1
9
]
,
h
ea
lth
p
ar
am
eter
s
(
g
lu
co
s
e
l
ev
el,
h
ea
r
t
r
ate,
a
n
d
b
lo
o
d
p
r
ess
u
r
e)
[
2
0
]
,
b
io
c
h
em
ical
p
ar
am
eter
s
(
g
lu
co
s
e
lev
el
,
lip
id
p
r
o
f
ile)
[
2
1
]
,
p
h
y
s
ical
ac
tiv
ity
p
ar
am
eter
s
(
ex
er
cise
f
r
eq
u
en
cy
,
in
ten
s
ity
)
[
2
2
]
,
o
p
h
th
alm
ic
p
ar
am
eter
s
(
r
etin
al
ch
a
n
g
es)
[
2
3
]
,
s
leep
p
atter
n
s
[
2
4
]
,
a
n
d
s
tr
ess
lev
els
[
2
5
]
.
B
y
in
teg
r
ati
n
g
an
d
an
al
y
zin
g
t
h
ese
d
iv
er
s
e
d
ata
s
o
u
r
ce
s
,
ML
an
d
DL
m
o
d
els
ca
n
id
e
n
tify
p
atter
n
s
,
tr
en
d
s
,
an
d
r
is
k
f
ac
to
r
s
ass
o
ciate
d
with
d
iab
etes
o
n
s
et,
p
r
o
g
r
ess
io
n
,
an
d
co
m
p
licatio
n
s
.
Ho
wev
er
,
m
o
s
t
ML
a
n
d
DL
r
esear
c
h
i
n
d
iab
etes
d
etec
tio
n
h
as
p
r
ed
o
m
in
an
tly
f
o
c
u
s
ed
o
n
d
em
o
g
r
ap
h
ic
i
n
f
o
r
m
atio
n
,
h
ea
lth
p
a
r
am
eter
s
,
a
n
d
b
io
c
h
em
ical
p
ar
a
m
eter
s
ca
lled
a
s
lo
n
g
itu
d
i
n
al
d
ata
[
2
6
]
,
[
2
7
]
.
T
h
er
e
is
lim
ited
em
p
h
asis
o
n
i
n
co
r
p
o
r
atin
g
s
leep
p
atter
n
s
,
s
tr
ess
lev
els,
an
d
p
h
y
s
ical
ac
tiv
ity
p
ar
am
et
er
s
in
to
p
r
ed
ictiv
e
m
o
d
els,
d
esp
ite
th
eir
k
n
o
w
n
in
f
lu
en
ce
o
n
d
iab
etes
r
is
k
an
d
m
an
ag
em
e
n
t.
Fu
r
th
er
m
o
r
e,
th
e
av
ailab
ilit
y
o
f
m
u
ltimo
d
al
d
atasets
co
n
tain
i
n
g
co
m
p
r
eh
e
n
s
iv
e
in
f
o
r
m
atio
n
ac
r
o
s
s
th
ese
d
o
m
ain
s
r
em
a
in
s
lim
ited
,
p
o
s
in
g
ch
allen
g
es to
d
ev
el
o
p
in
g
r
o
b
u
s
t a
n
d
g
en
er
alize
d
ML
/DL
m
o
d
els f
o
r
d
iab
etes d
etec
tio
n
an
d
p
r
ed
ictio
n
[
2
8
]
.
Hen
ce
,
th
is
wo
r
k
aim
s
to
ad
d
r
ess
s
ev
er
al
k
ey
is
s
u
es
in
th
e
f
i
eld
o
f
d
iab
etes
d
etec
tio
n
a
n
d
p
r
ed
ictio
n
as
p
r
esen
ted
ab
o
v
e.
T
o
ac
h
ie
v
e
th
is
g
o
al,
a
co
m
p
r
eh
en
s
iv
e
r
ev
iew
is
p
r
esen
ted
,
f
o
cu
s
in
g
o
n
th
e
d
iv
er
s
e
p
ar
am
eter
s
u
tili
ze
d
in
p
r
e
v
io
u
s
s
tu
d
ies
f
o
r
d
iab
etes
d
etec
tio
n
an
d
p
r
ed
ictio
n
o
v
er
th
e
y
ea
r
s
.
B
y
an
aly
zin
g
th
e
m
eth
o
d
o
l
o
g
ies
an
d
f
in
d
i
n
g
s
o
f
th
ese
s
tu
d
ies,
v
alu
a
b
le
in
s
ig
h
ts
in
to
th
e
s
tr
en
g
th
s
an
d
lim
itatio
n
s
o
f
ex
is
tin
g
ap
p
r
o
ac
h
es
ar
e
g
ain
ed
.
Fu
r
th
er
m
o
r
e,
th
is
r
ev
iew
id
en
tifie
s
v
ar
io
u
s
d
atasets
th
at
h
av
e
b
ee
n
u
s
ed
in
d
iab
etes
r
esear
ch
,
alo
n
g
with
th
eir
r
esp
ec
tiv
e
f
in
d
in
g
s
.
U
n
d
er
s
tan
d
i
n
g
th
e
o
u
tco
m
es
o
f
th
ese
s
tu
d
ies
p
r
o
v
id
es
a
b
asis
f
o
r
e
v
alu
atin
g
th
e
e
f
f
ec
tiv
en
e
s
s
an
d
ap
p
licab
ilit
y
o
f
d
if
f
er
e
n
t
d
atasets
in
d
ia
b
etes
d
etec
tio
n
a
n
d
p
r
ed
ictio
n
task
s
.
Ho
wev
er
,
d
esp
ite
th
e
p
r
o
g
r
ess
m
ad
e
in
d
iab
etes
r
esear
ch
,
t
h
er
e
ar
e
n
o
tab
le
lim
itatio
n
s
an
d
ch
allen
g
es
th
at
n
ee
d
to
b
e
ad
d
r
ess
ed
.
T
h
ese
lim
itatio
n
s
en
co
m
p
ass
i
s
s
u
es
s
u
ch
as
th
e
lim
ited
in
teg
r
atio
n
o
f
m
u
ltimo
d
al
d
ata,
in
s
u
f
f
icien
t
em
p
h
asis
o
n
ea
r
ly
d
etec
tio
n
s
tr
ateg
ies,
an
d
g
ap
s
i
n
in
c
o
r
p
o
r
atin
g
f
ac
to
r
s
lik
e
s
leep
p
atter
n
s
,
s
tr
ess
lev
el
s
,
an
d
p
h
y
s
ical
ac
tiv
ity
p
ar
am
eter
s
in
to
p
r
e
d
ictiv
e
m
o
d
els.
I
d
e
n
tify
in
g
th
ese
g
ap
s
,
is
s
u
es,
an
d
ch
allen
g
es
is
cr
u
cial
f
o
r
ad
v
a
n
cin
g
th
e
f
ield
o
f
d
ia
b
etes
r
e
s
ea
r
ch
an
d
d
ev
elo
p
in
g
m
o
r
e
ac
cu
r
ate
an
d
r
o
b
u
s
t
p
r
ed
ictiv
e
m
o
d
els.
T
o
b
r
id
g
e
th
ese
g
ap
s
a
n
d
o
v
e
r
co
m
e
t
h
e
id
en
tifie
d
ch
allen
g
es,
th
is
wo
r
k
p
r
o
p
o
s
es
a
m
u
ltimo
d
al
f
r
am
ewo
r
k
f
o
r
d
ia
b
etes
d
etec
tio
n
a
n
d
p
r
e
d
ictio
n
.
T
h
is
f
r
a
m
ewo
r
k
in
teg
r
ates
d
i
v
er
s
e
d
ata
s
o
u
r
ce
s
,
in
clu
d
in
g
d
em
o
g
r
a
p
h
ic
in
f
o
r
m
atio
n
,
h
ea
lth
p
ar
am
eter
s
,
b
io
ch
em
ical
m
ar
k
er
s
,
p
h
y
s
ical
ac
ti
v
ity
p
atter
n
s
,
o
p
h
th
alm
ic
p
ar
am
eter
s
,
s
lee
p
p
atter
n
s
,
an
d
s
tr
ess
lev
els.
B
y
lev
er
ag
in
g
a
m
u
ltim
o
d
al
ap
p
r
o
ac
h
,
th
is
f
r
am
ewo
r
k
aim
s
to
im
p
r
o
v
e
th
e
ac
cu
r
ac
y
,
s
en
s
itiv
ity
,
an
d
s
p
ec
if
icity
o
f
d
iab
etes
d
etec
tio
n
an
d
p
r
ed
ictio
n
m
o
d
els.
Ad
d
itio
n
ally
,
it
s
ee
k
s
to
e
n
h
an
ce
ea
r
ly
id
en
tific
ati
o
n
s
tr
ateg
ies
an
d
p
r
o
v
id
e
a
m
o
r
e
co
m
p
r
e
h
en
s
iv
e
u
n
d
er
s
tan
d
i
n
g
o
f
d
iab
etes r
is
k
f
ac
to
r
s
an
d
p
r
o
g
r
ess
io
n
p
ath
w
ay
s
.
I
n
s
ec
tio
n
2
o
f
th
is
m
a
n
u
s
cr
ip
t
,
an
ex
ten
s
iv
e
liter
atu
r
e
s
u
r
v
ey
is
co
n
d
u
cted
,
f
o
cu
s
in
g
o
n
th
e
d
etec
tio
n
an
d
p
r
e
d
ictio
n
o
f
d
ia
b
etes
u
s
in
g
ar
tific
ial
in
tellig
en
ce
(
AI
)
tech
n
iq
u
es.
Mo
v
i
n
g
o
n
to
s
ec
tio
n
3
,
a
n
o
v
el
m
u
ltimo
d
al
f
r
am
ewo
r
k
f
o
r
th
e
d
etec
tio
n
a
n
d
p
r
ed
ictio
n
o
f
d
iab
etes
is
p
r
esen
ted
.
Fin
ally
,
in
s
ec
tio
n
4
,
th
e
m
an
u
s
cr
ip
t c
o
n
clu
d
es b
y
s
u
m
m
ar
izin
g
th
e
o
v
er
all
wo
r
k
.
Evaluation Warning : The document was created with Spire.PDF for Python.
I
n
d
o
n
esian
J
E
lec
E
n
g
&
C
o
m
p
Sci
I
SS
N:
2
5
0
2
-
4
7
52
Dia
b
etes d
etec
tio
n
a
n
d
p
r
ed
ic
tio
n
th
r
o
u
g
h
a
mu
ltimo
d
a
l
…
(
Gu
r
u
r
a
j N.
K
u
lka
r
n
i
)
461
2.
L
I
T
E
R
AT
U
RE
SU
RVE
Y
I
n
th
is
s
ec
tio
n
an
ex
ten
s
iv
e
liter
atu
r
e
s
u
r
v
ey
is
co
n
d
u
cted
,
f
o
cu
s
in
g
o
n
th
e
d
etec
tio
n
an
d
p
r
ed
ictio
n
o
f
d
iab
etes
u
s
in
g
AI
tech
n
iq
u
es.
T
h
e
s
u
r
v
ey
en
co
m
p
ass
es
d
is
cu
s
s
io
n
s
o
n
th
e
u
tili
za
tio
n
o
f
v
ar
io
u
s
m
u
lti
-
p
ar
am
eter
s
s
u
ch
as
h
ea
lth
p
ar
am
e
ter
s
,
s
leep
p
atter
n
s
,
s
tr
ess
lev
els,
p
h
y
s
ical
ac
tiv
ity
p
ar
a
m
eter
s
,
b
io
ch
e
m
ical
m
ar
k
er
s
,
an
d
o
p
h
t
h
alm
ic
p
ar
a
m
eter
s
in
d
iab
etes
r
esear
ch
.
T
h
r
o
u
g
h
th
is
s
u
r
v
ey
,
t
h
e
r
esu
lts
an
d
f
in
d
i
n
g
s
f
r
o
m
p
r
ev
io
u
s
s
tu
d
ies
ar
e
id
en
tifi
ed
,
p
r
o
v
id
in
g
i
n
s
ig
h
ts
in
to
th
e
ef
f
ec
tiv
en
ess
an
d
ap
p
licab
ilit
y
o
f
AI
-
b
ase
d
ap
p
r
o
ac
h
es
in
d
iab
etes
d
etec
ti
o
n
an
d
p
r
e
d
ictio
n
task
s
.
Fo
llo
win
g
th
e
d
is
cu
s
s
io
n
o
f
r
esu
lts
an
d
f
in
d
in
g
s
,
th
e
lim
itatio
n
s
id
en
tifie
d
f
r
o
m
th
e
liter
atu
r
e
s
u
r
v
ey
ar
e
p
r
esen
ted
.
T
h
ese
lim
itatio
n
s
h
ig
h
lig
h
t
ar
ea
s
o
f
im
p
r
o
v
em
e
n
t
an
d
c
h
allen
g
es
f
ac
ed
b
y
e
x
is
tin
g
r
esear
ch
m
et
h
o
d
o
lo
g
ies
an
d
ap
p
r
o
ac
h
es
in
d
iab
etes
d
etec
tio
n
an
d
p
r
ed
ictio
n
u
s
in
g
AI
tech
n
iq
u
es.
Un
d
er
s
tan
d
in
g
th
ese
l
im
itatio
n
s
is
cr
u
cial
f
o
r
d
e
v
elo
p
in
g
m
o
r
e
r
o
b
u
s
t
an
d
ac
cu
r
ate
p
r
e
d
ictiv
e
m
o
d
el
s
in
f
u
tu
r
e
r
esear
ch
wo
r
k
s
.
Fu
r
th
er
m
o
r
e
,
in
th
is
s
ec
tio
n
,
th
e
s
u
r
v
ey
d
elv
es
in
to
th
e
id
en
tific
atio
n
an
d
d
is
cu
s
s
io
n
o
f
g
ap
s
,
is
s
u
es,
an
d
ch
al
len
g
es
o
b
s
er
v
ed
i
n
th
e
liter
at
u
r
e
s
u
r
v
ey
.
T
h
ese
d
is
cu
s
s
io
n
s
s
h
ed
lig
h
t
o
n
th
e
g
ap
s
i
n
c
u
r
r
en
t
r
esear
ch
,
i
s
s
u
es
f
ac
ed
in
im
p
lem
en
tin
g
AI
tech
n
iq
u
es
f
o
r
d
iab
etes
d
etec
tio
n
,
an
d
ch
alle
n
g
es
th
at
n
ee
d
to
b
e
ad
d
r
ess
ed
f
o
r
a
d
v
an
cin
g
th
e
f
ield
o
f
AI
-
b
ased
d
iab
etes
r
esear
ch
.
-
Dete
ctio
n
an
d
p
r
e
d
ictio
n
o
f
d
iab
etics u
s
in
g
AI
tech
n
iq
u
es
co
n
s
id
er
in
g
m
u
lti
-
p
ar
am
ete
r
s
T
h
is
s
ec
tio
n
d
elv
es
in
to
th
e
d
etec
tio
n
an
d
p
r
e
d
ictio
n
o
f
d
iab
etes
u
s
in
g
AI
b
y
co
n
s
id
er
in
g
m
u
lti
-
p
ar
am
eter
s
.
T
h
ese
m
u
lti
-
p
ar
a
m
eter
s
in
clu
d
e
d
em
o
g
r
a
p
h
ic
i
n
f
o
r
m
atio
n
(
ag
e,
s
ex
)
,
h
ea
lth
p
ar
am
eter
s
(
b
lo
o
d
p
r
ess
u
r
e,
h
ea
r
t
r
ate)
,
s
leep
p
atter
n
s
(
q
u
ality
,
d
u
r
atio
n
)
,
s
tr
ess
,
(
p
h
y
s
io
lo
g
ical
r
esp
o
n
s
e,
co
r
tis
o
l
lev
el)
,
p
h
y
s
ical
ac
tiv
ity
p
ar
am
eter
(
ex
er
cise
f
r
eq
u
e
n
cy
,
in
ten
s
ity
)
,
b
io
ch
em
ical
p
a
r
am
eter
s
(
g
lu
co
s
e
lev
el,
lip
id
p
r
o
f
iles
)
a
n
d
o
p
h
th
alm
ic
p
ar
a
m
eter
s
(
r
etin
al
ch
an
g
es).
B
o
d
ap
ati
et
a
l.
[
2
9
]
,
th
is
wo
r
k
f
o
c
u
s
s
ed
o
n
ev
alu
atin
g
th
e
o
p
h
t
h
alm
ic
p
ar
a
m
eter
(
i.e
.
,
d
iab
etic
r
etin
o
p
ath
y
(
DR
)
)
.
I
n
th
is
wo
r
k
,
th
ey
co
n
s
id
er
e
d
t
h
e
Kag
g
le
APTO
S
2
0
1
9
[
3
0
]
d
ataset
(
im
ag
e
d
a
taset)
f
o
r
ev
alu
atin
g
th
e
o
p
h
th
alm
ic
p
ar
am
eter
.
T
h
is
wo
r
k
u
tili
ze
d
v
ar
io
u
s
co
n
v
o
l
u
tio
n
al
n
etwo
r
k
(
C
o
v
Net)
ap
p
r
o
ac
h
es
to
ex
tr
ac
t
th
e
o
p
tim
al
ch
ar
ac
ter
is
tics
f
r
o
m
th
e
d
ataset.
T
h
ese
ch
ar
ac
ter
is
tics
wer
e
u
s
ed
f
o
r
tr
ain
in
g
d
ee
p
n
eu
r
al
n
etw
o
r
k
(
DNN)
f
o
r
id
en
tify
in
g
DR
.
T
h
is
ap
p
r
o
ac
h
ac
h
iev
ed
ac
c
u
r
ac
y
o
f
0
.
9
7
4
1
f
o
r
id
en
tify
i
n
g
DR
an
d
ac
c
u
r
ac
y
o
f
0
.
8
1
7
f
o
r
s
ev
er
ity
ex
te
n
t
p
r
e
d
ictio
n
.
C
o
r
d
eir
o
et
a
l.
[
3
1
]
,
th
is
wo
r
k
f
o
c
u
s
ed
o
n
elec
tr
o
ca
r
d
i
g
r
a
m
(
E
C
G)
s
ig
n
als
(
b
io
m
e
d
ical
s
ig
n
als)
wh
ich
h
ad
v
ar
io
u
s
p
ar
am
eter
s
o
th
er
th
a
n
E
C
G,
i.e
.
,
h
ea
r
t
-
r
ate,
b
lo
o
d
g
l
u
co
s
e
co
n
ce
n
tr
atio
n
lev
el,
weig
h
t,
g
en
d
e
r
,
h
eig
h
t
an
d
a
g
e.
T
h
is
wo
r
k
f
o
c
u
s
ed
o
n
h
y
p
er
g
l
y
ca
em
ia,
a
ty
p
e
o
f
d
iab
etes
wh
er
e
s
u
g
a
r
lev
els
ar
e
v
er
y
m
u
c
h
h
ig
h
i
n
co
m
p
ar
is
o
n
to
a
n
o
r
m
al
p
er
s
o
n
.
Fu
r
t
h
er
,
th
ey
f
ac
ed
a
ch
alle
n
g
e
r
eg
ar
d
i
n
g
th
e
d
ataset,
i.e
.
,
m
o
s
t
o
f
t
h
e
n
o
r
m
al
E
C
G
s
ig
n
als
d
ataset
s
s
u
ch
as
Ph
y
s
io
n
et
B
an
k
[
3
2
]
d
o
n
o
t
in
clu
d
e
t
h
e
g
lu
c
o
s
e
co
n
ce
n
tr
atio
n
lev
els.
T
o
ad
d
r
ess
th
is
is
s
u
e,
th
ey
g
e
n
er
ated
a
n
o
v
el
d
ataset
wh
ich
co
n
tain
s
E
C
G
s
ig
n
al
h
av
in
g
g
lu
co
s
e
co
n
ce
n
tr
atio
n
lev
els.
T
h
ey
co
n
s
id
er
ed
1
,
1
1
9
in
d
iv
id
u
als
o
f
b
o
th
n
o
n
-
h
y
p
er
g
ly
ca
em
ias
an
d
h
y
p
er
g
l
y
c
ae
m
ia
in
d
iv
id
u
als.
T
h
ey
clas
s
if
ied
an
i
n
d
iv
id
u
al
as
h
y
p
er
g
ly
ca
em
ias
if
th
e
b
lo
o
d
g
l
u
co
s
e
co
n
ce
n
tr
atio
n
lev
e
l
was
1
0
0
m
g
/d
L
.
Fu
r
th
er
,
th
is
wo
r
k
co
n
s
id
er
ed
1
0
-
f
o
l
d
cr
o
s
s
v
alid
atio
n
(
C
V)
DNN
ap
p
r
o
ac
h
wh
ich
ac
h
iev
ed
ar
ea
-
u
n
d
er
th
e
cu
r
v
e
(
AUC)
o
f
0
.
9
4
5
3
.
Her
v
ella
et
a
l.
[
3
3
]
,
co
n
s
id
er
ed
o
p
h
th
alm
ic
p
ar
am
eter
f
o
r
d
etec
tin
g
wh
eth
e
r
a
n
in
d
iv
id
u
al
is
n
o
n
-
d
iab
etic
o
r
d
iab
etic.
Fo
r
t
h
is
wo
r
k
,
f
o
r
tr
ain
in
g
th
eir
ap
p
r
o
ac
h
,
th
e
y
u
tili
ze
d
a
m
u
ltimo
d
al
d
ataset
[
3
4
]
wh
ich
co
n
s
is
ted
o
f
im
ag
es
r
elate
d
to
r
etin
a.
Fu
r
th
er
,
test
in
g
was
d
o
n
e
o
n
v
ar
io
u
s
DR
im
ag
e
d
ataset,
i.e
.
,
E
y
ePAC
S
-
Kag
g
le
[
3
5
]
,
Me
s
s
id
o
r
[
3
6
]
,
Me
s
s
id
o
r
-
2
[
3
7
]
,
an
d
I
DR
iD
[
3
8
]
.
T
h
ese
v
a
r
io
u
s
d
atasets
in
clu
d
e
im
ag
e
d
ataset
wh
ich
f
o
cu
s
o
n
th
e
DR
.
T
h
is
wo
r
k
m
ain
aim
was
to
g
r
ad
e
th
e
DR
im
ag
es
an
d
d
etec
t
a
d
iab
etic
in
d
iv
id
u
al.
Hen
ce
,
th
ey
p
r
esen
te
d
a
m
o
d
e
l c
alled
as
m
u
lti
-
m
o
d
al
im
ag
e
-
en
co
d
in
g
(
MI
E
)
,
wh
ich
u
s
ed
t
h
e
C
o
v
Net
en
co
d
e
r
f
o
r
ex
tr
ac
tin
g
f
ea
tu
r
es
f
r
o
m
th
e
im
ag
es.
Fu
r
th
er
,
th
e
r
esu
lt
s
f
o
r
ex
tr
ac
tio
n
o
f
f
ea
tu
r
es
u
s
in
g
I
DR
iD
s
h
o
wed
th
at
th
e
MI
E
ac
h
iev
e
d
ac
cu
r
ac
y
o
f
0
.
6
1
1
7
an
d
DR
AUC
o
f
0
.
9
1
9
0
.
A
ls
o
,
wh
en
e
x
tr
a
ctin
g
f
ea
tu
r
es
f
r
o
m
Me
s
s
id
o
r
s
h
o
wed
t
h
at
MI
E
ac
h
iev
ed
ac
cu
r
ac
y
o
f
0
.
6
6
0
5
an
d
DR
AUC
o
f
0
.
8
4
3
9
,
r
esp
ec
tiv
ely
.
W
h
en
g
r
a
d
in
g
th
e
I
DR
iD
an
d
Me
s
s
id
o
r
,
t
h
e
MI
E
ac
h
iev
ed
ac
cu
r
a
cy
o
f
0
.
6
5
0
5
a
n
d
0
.
7
2
5
5
r
esp
ec
tiv
ely
.
Ku
lk
ar
n
i
et
a
l.
[
3
9
]
,
f
o
cu
s
ed
o
n
d
etec
tin
g
T
y
p
e
-
2
d
iab
etics
u
s
in
g
E
C
G
s
ig
n
als.
I
n
th
is
wo
r
k
,
t
h
ey
also
co
n
s
id
er
e
d
o
t
h
er
p
ar
am
eter
s
o
th
er
th
an
E
C
G,
i.e
.
,
b
lo
o
d
p
r
ess
u
r
e,
b
o
d
y
-
m
ass
-
in
d
e
x
,
s
ex
,
ag
e,
h
ea
r
t,
an
d
o
t
h
er
b
io
c
h
em
i
ca
l p
ar
am
eter
s
.
Fo
r
th
is
s
tu
d
y
th
e
y
h
av
e
g
en
e
r
ated
th
eir
o
wn
d
ataset
co
llected
f
r
o
m
Nag
p
u
r
.
T
h
e
d
ataset
i
n
clu
d
ed
1
,
2
6
2
s
u
b
jects.
Fu
r
th
er
,
f
o
r
d
etec
tin
g
wh
eth
e
r
th
e
s
u
b
ject
is
d
iab
etic
o
r
n
o
n
-
d
iab
etic,
th
is
wo
r
k
u
tili
ze
d
v
ar
io
u
s
ML
an
d
DL
ap
p
r
o
ac
h
es,
i.e
.
,
lo
n
g
s
h
o
r
t
-
t
er
m
m
em
o
r
y
(
L
STM
)
,
co
n
v
o
lu
tio
n
al
n
eu
r
al
n
etwo
r
k
(
C
NN)
,
an
d
XGBo
o
s
t
(
XGB).
Als
o
,
th
is
wo
r
k
p
r
o
p
o
s
ed
an
alg
o
r
ith
m
ca
lled
as Dia
B
ea
ts
wh
ich
ac
h
iev
ed
ac
cu
r
ac
y
o
f
0
.
9
6
8
.
Yu
et
a
l.
[
4
0
]
,
co
n
s
id
er
e
d
v
ar
io
u
s
h
ea
lth
p
ar
am
ete
r
s
an
d
d
em
o
g
r
a
p
h
ic
in
f
o
r
m
atio
n
f
o
r
d
etec
tin
g
T
y
p
e
-
2
d
iab
etes
in
p
atien
ts
.
I
n
th
is
wo
r
k
th
ey
co
n
s
i
d
er
e
d
th
e
r
esear
ch
o
n
ea
r
ly
-
life
a
n
d
ag
in
g
-
tr
e
n
d
s
an
d
ef
f
ec
ts
(
R
E
L
AT
E
)
d
ataset
[
4
1
]
.
Fo
r
d
etec
tin
g
d
ia
b
etes,
th
ey
p
r
o
p
o
s
ed
a
n
ap
p
r
o
ac
h
ca
lled
d
iab
etes
-
m
ellitu
s
n
etwo
r
k
(
DM
Net)
.
T
h
e
DM
Net
ap
p
r
o
ac
h
c
o
n
s
is
ted
o
f
SMOT
E
-
T
o
m
ek
f
o
r
f
ea
t
u
r
e
e
x
tr
ac
tio
n
,
T
a
n
d
em
-
L
STM
(T
-
L
STM
)
f
o
r
ca
p
tu
r
i
n
g
r
is
k
f
ac
to
r
s
r
elate
d
to
d
iab
etes
an
d
f
in
ally
ML
P
was
u
s
ed
f
o
r
class
if
icatio
n
.
T
h
e
r
esu
lts
s
h
o
wed
th
at
DM
Net
ac
h
iev
ed
ac
cu
r
ac
y
o
f
9
4
%.
T
h
eir
m
ain
aim
was
to
s
elec
t
o
p
tim
al
f
ea
tu
r
es
f
o
r
class
if
icatio
n
an
d
h
an
d
le
cla
s
s
im
b
alan
ce
.
B
o
t
ella
-
Ser
r
an
o
et
a
l.
[
4
2
]
,
th
is
wo
r
k
s
tu
d
ied
h
o
w
th
e
s
leep
p
ar
am
eter
ca
n
h
elp
to
co
n
tr
o
l
T
y
p
e
-
1
d
iab
etes.
T
h
is
wo
r
k
co
n
tin
u
o
u
s
ly
ev
alu
ated
th
e
g
lu
c
o
s
e
lev
els
o
f
th
e
2
5
Evaluation Warning : The document was created with Spire.PDF for Python.
I
SS
N
:
2
5
0
2
-
4
7
52
In
d
o
n
esian
J
E
lec
E
n
g
&
C
o
m
p
Sci
,
Vo
l.
3
8
,
No
.
1
,
Ap
r
il
20
2
5
:
4
5
9
-
4
6
8
462
p
atien
ts
f
o
r
a
d
u
r
atio
n
o
f
1
4
d
ay
s
.
Als
o
,
in
th
is
wo
r
k
,
t
h
e
b
i
o
ch
em
ical
p
a
r
am
eter
was
als
o
co
n
s
id
er
ed
f
o
r
th
e
s
tu
d
y
wh
ich
was
co
n
tin
u
o
u
s
ly
m
o
n
ito
r
ed
u
s
in
g
a
f
it
b
a
n
d
.
T
h
e
lo
g
is
tic
r
eg
r
ess
io
n
(
LR
)
was
u
s
ed
f
o
r
ev
alu
atin
g
th
e
d
ata
g
ath
er
ed
f
r
o
m
th
e
p
atien
ts
.
T
h
e
r
esu
lts
s
h
o
wed
th
at
p
atien
ts
wh
o
h
ad
g
o
o
d
s
leep
im
p
r
o
v
ed
th
eir
g
ly
ca
e
m
ic
co
n
tr
o
l.
Ro
d
r
i
g
u
ez
-
L
eo
n
et
a
l.
[
4
3
]
,
p
r
esen
t
ed
a
m
u
ltimo
d
al
d
ataset
wh
ich
co
n
s
is
ted
o
f
h
ea
lth
p
ar
am
eter
s
,
o
p
h
th
alm
ic
p
a
r
a
m
eter
s
,
b
io
ch
em
ical
p
ar
am
et
er
s
an
d
d
em
o
g
r
a
p
h
ic
in
f
o
r
m
atio
n
f
o
r
d
etec
tin
g
T
y
p
e
-
1
m
ellitu
s
d
ia
b
etes.
Pai
et
a
l.
[
4
4
]
,
th
e
y
p
r
esen
ted
a
m
u
ltimo
d
al
d
a
taset
wh
ic
h
c
o
n
s
is
ted
o
f
p
h
y
s
ical
ac
tiv
ity
p
ar
am
eter
s
an
d
b
io
c
h
e
m
ical
p
ar
am
eter
s
f
o
r
p
r
o
v
id
in
g
a
b
etter
life
s
ty
le
f
o
r
T
y
p
e
-
2
d
iab
etic
p
atien
ts
.
T
h
eis
et
a
l.
[
4
5
]
,
h
as
co
n
s
id
er
ed
u
s
in
g
h
ea
lth
p
ar
am
eter
s
f
o
r
ev
alu
atio
n
o
f
d
iab
etics.
T
h
is
wo
r
k
co
n
s
id
er
ed
m
ed
ical
-
in
f
o
r
m
ati
o
n
-
m
ar
t
f
o
r
in
ten
s
iv
e
-
ca
r
e
(
MI
MI
C
I
I
I
)
[
4
6
]
d
ataset
f
o
r
ev
alu
atio
n
o
f
t
h
eir
wo
r
k
.
T
h
is
d
ataset
co
n
s
is
ted
o
f
v
ar
io
u
s
h
ea
lth
r
elate
d
p
a
r
am
eter
s
wh
ich
in
clu
d
e
d
clin
ical
test
s
,
clin
ica
l
m
ea
s
u
r
em
en
ts
,
d
em
o
g
r
ap
h
ics,
b
illi
n
g
,
p
h
ar
m
ac
o
t
h
er
ap
y
,
in
t
er
v
en
tio
n
m
eth
o
d
s
,
an
d
m
ed
i
ca
l
in
f
o
r
m
atio
n
o
f
an
in
d
iv
id
u
al.
T
h
is
wo
r
k
u
tili
ze
d
a
DNN
ap
p
r
o
ac
h
f
o
r
p
r
ed
ic
tio
n
o
f
d
ea
th
o
f
p
atien
ts
s
u
f
f
er
in
g
f
r
o
m
d
iab
etics.
T
h
e
r
esu
lts
f
r
o
m
th
is
wo
r
k
s
h
o
wed
th
at
th
is
ap
p
r
o
ac
h
ac
h
iev
ed
ar
ea
-
u
n
d
er
th
e
r
ec
eiv
er
-
op
er
atin
g
-
c
h
ar
ac
ter
is
tics
(
AURO
C
)
o
f
0
.
8
7
3
.
Naseem
et
a
l.
[
4
7
]
,
th
is
wo
r
k
co
n
s
id
er
e
d
u
s
in
g
h
ea
lth
p
ar
am
eter
s
f
o
r
p
r
ed
ictin
g
d
iab
etes
i
n
in
d
iv
id
u
als.
T
h
is
wo
r
k
c
o
n
s
id
er
ed
a
Kag
g
le
d
ataset
p
r
o
v
i
d
ed
b
y
Natio
n
al
-
I
n
s
titu
te
o
f
Diab
etes
ca
lled
as
PIM
A
[
4
8
]
.
T
h
is
d
a
taset
co
n
s
is
ted
o
f
v
ar
io
u
s
p
ar
am
eter
s
wh
ich
i
n
clu
d
e
in
s
u
lin
lev
el,
b
lo
o
d
-
p
r
ess
u
r
e,
p
r
eg
n
an
cies,
ag
e,
b
o
d
y
-
m
ass
-
in
d
ex
es,
g
l
u
c
o
s
e
lev
el,
an
d
s
k
in
t
h
ick
n
ess
.
Fo
r
p
r
e
d
ictio
n
,
th
is
wo
r
k
co
n
s
id
er
e
d
L
R
,
s
u
p
p
o
r
t
v
ec
to
r
m
ac
h
in
e
(
SVM)
,
L
ST
M,
C
NN
an
d
ar
tific
ial
n
eu
r
a
l
n
etwo
r
k
(
ANN)
,
an
d
r
ec
u
r
r
en
t
n
e
u
r
al
n
etwo
r
k
(
R
NN)
.
T
h
e
f
in
d
in
g
s
s
h
o
wed
th
at
am
o
n
g
all
th
ese
ap
p
r
o
ac
h
es,
th
e
R
NN
ac
h
iev
ed
b
est ac
cu
r
ac
y
,
i.e
.
,
0
.
8
1
.
Als
o
,
it wa
s
s
ee
n
th
at
ANN
ac
h
iev
ed
b
etter
r
ec
all,
i.e
.
,
0
.
5
6
.
Ah
m
ed
et
a
l.
[
4
9
]
,
p
r
ed
icted
d
iab
etics
u
s
in
g
SVM
an
d
AN
N.
Fu
r
th
er
,
th
ey
p
r
o
p
o
s
ed
an
ap
p
r
o
ac
h
ca
lled
as
f
u
s
ed
-
m
eth
o
d
f
o
r
d
i
ab
etic
p
r
ed
ictio
n
(
FMDA)
.
T
h
is
wo
r
k
co
n
s
id
er
ed
a
d
ataset
f
r
o
m
Un
iv
er
s
ity
o
f
C
alif
o
r
n
ia
-
I
r
v
in
e
(
UC
I
)
[
5
0
]
.
T
h
e
d
ataset
co
n
s
is
ted
o
f
v
ar
io
u
s
h
ea
lth
p
ar
am
eter
s
,
b
io
ch
em
ical
p
ar
am
et
er
s
,
an
d
d
em
o
g
r
a
p
h
ic
d
ata.
T
h
e
r
esu
lts
s
h
o
wed
th
at
SVM,
A
NN
,
an
d
FMDA
ac
h
iev
ed
ac
cu
r
ac
y
o
f
8
9
.
1
0
%,
9
2
.
3
1
%
,
an
d
9
4
.
8
7
%
d
u
r
i
n
g
t
esti
n
g
.
J
ia
et
a
l.
[
5
1
]
,
co
n
s
id
er
ed
to
class
if
y
d
iab
etes
u
s
in
g
P
I
MA
d
ataset
wh
ich
co
n
s
is
ts
o
f
v
ar
io
u
s
h
ea
lth
p
ar
am
eter
s
.
Fo
r
class
if
icatio
n
,
th
is
wo
r
k
p
r
o
p
o
s
ed
an
ap
p
r
o
ac
h
ca
lled
as
p
r
o
b
a
b
ilis
tic
-
en
s
em
b
le
class
if
icatio
n
ap
p
r
o
ac
h
f
o
r
d
iab
etic
in
d
iv
id
u
al
(
PE
-
DI
M)
.
T
h
eir
m
ain
aim
was
to
h
an
d
le
th
e
class
im
b
alan
ce
i
s
s
u
e.
T
h
is
wo
r
k
was
b
u
ilt
b
y
co
m
b
in
in
g
lo
ca
l
m
e
d
ian
-
b
a
s
ed
(
L
M)
g
au
s
s
ian
N
aïv
e
-
B
ay
es
(
NB
)
(
L
Me
GNB)
an
d
k
-
m
ea
n
s
s
y
n
t
h
etic
-
m
in
o
r
ity
-
o
v
er
-
s
am
p
lin
g
tech
n
iq
u
e
(
SMOT
E
)
.
T
h
e
PE
-
DI
M
ac
h
iev
ed
ac
c
u
r
ac
y
o
f
0
.
9
4
5
3
f
o
r
th
e
PIM
A
d
ataset
d
u
r
i
n
g
test
in
g
.
Fu
r
th
e
r
,
t
h
ey
ev
alu
ated
th
eir
wo
r
k
o
n
o
th
er
d
atasets
,
i.e
.
,
o
n
ty
p
e
-
2
d
ataset
ca
lled
as
R
SM
H
[
5
2
]
an
d
T
ab
r
iz
[
5
3
]
.
T
h
e
R
SMH
d
ataset
co
n
s
is
ted
o
f
d
em
o
g
r
a
p
h
ic,
b
io
ch
e
m
ical
an
d
h
ea
lth
p
ar
a
m
eter
s
,
wh
er
ea
s
th
e
T
ab
r
iz
d
ataset
co
n
s
is
ted
o
f
d
em
o
g
r
a
p
h
ic
a
n
d
b
io
ch
em
ical
h
is
to
r
y
an
d
h
ea
lt
h
p
ar
am
ete
r
s
.
T
h
e
PE
-
D
I
M
a
ch
iev
ed
b
etter
r
esu
lts
f
o
r
b
o
th
th
e
d
ataset
,
i.e
.
,
AUC o
f
0
.
9
9
1
7
an
d
0
.
9
9
8
2
f
o
r
R
SMH
an
d
T
ab
r
iz
d
ataset,
r
e
s
p
ec
tiv
ely
.
Yad
av
et
a
l.
[
5
4
]
,
f
o
cu
s
was o
n
d
etec
tin
g
th
e
h
y
p
er
-
p
ar
am
et
er
s
wh
ich
ca
n
h
elp
p
r
e
d
ictio
n
o
f
d
iab
etes
b
y
s
elec
tin
g
o
p
tim
al
f
ea
tu
r
es.
Fo
r
ev
alu
atio
n
o
f
th
is
wo
r
k
,
th
ey
u
s
ed
th
ey
PIM
A
,
an
d
t
wo
I
n
d
ia
n
d
atasets
wh
ich
wer
e
f
o
cu
s
ed
o
n
m
ellitu
s
d
iab
etes
ca
lled
as
FH
D
an
d
ADRC
d
ata
s
ets.
T
h
e
FHD
d
ataset
co
n
s
is
ted
o
f
4
0
0
r
ec
o
r
d
s
h
av
i
n
g
7
attr
ib
u
te
s
wh
er
e
1
5
0
wer
e
d
iab
etic
p
atien
ts
an
d
2
5
0
wer
e
n
o
n
-
d
iab
eti
c
p
atien
ts
.
Fu
r
th
er
,
th
e
ADRC
d
ata
s
et
c
o
n
s
is
ted
o
f
5
8
3
r
ec
o
r
d
s
h
a
v
in
g
7
attr
i
b
u
t
es wh
er
e
1
6
7
wer
e
d
ia
b
etic
p
a
tien
ts
an
d
4
1
6
n
o
n
-
d
iab
etic
p
atien
ts
.
T
h
e
PIM
A
d
ataset
in
th
is
wo
r
k
co
n
s
is
ted
o
f
7
6
8
r
ec
o
r
d
s
co
n
s
is
tin
g
o
f
9
a
ttrib
u
tes wh
er
e
2
6
8
wer
e
d
iab
etic
an
d
5
0
0
wer
e
n
o
n
-
d
iab
etic.
All
th
e
d
ataset
s
h
ad
h
ea
lth
p
ar
am
eter
s
a
n
d
b
io
c
h
em
ical
p
ar
am
ete
r
s
.
T
h
e
FHD
an
d
AD
R
C
d
ata
s
et
ad
d
itio
n
ally
co
n
s
is
ted
o
f
d
em
o
g
r
ap
h
ic
in
f
o
r
m
atio
n
.
Fo
r
b
al
an
cin
g
th
e
f
ea
tu
r
es,
th
e
SMOT
E
was
u
tili
ze
d
.
Fo
r
s
elec
tin
g
o
p
tim
al
f
ea
t
u
r
es,
th
ey
u
tili
ze
d
wr
ap
p
e
r
a
n
d
f
ilter
i
n
g
m
eth
o
d
.
Fu
r
t
h
er
f
o
r
tu
n
in
g
t
h
e
h
y
p
er
p
ar
am
eter
,
B
ay
esian
o
p
tim
izin
g
a
p
p
r
o
a
ch
,
g
r
i
d
an
d
r
an
d
o
m
s
ea
r
ch
w
as
u
tili
ze
d
.
Fin
ally
,
th
ey
p
r
o
p
o
s
ed
an
alg
o
r
ith
m
ca
lled
as
g
r
ey
-
wo
lf
-
o
p
tim
i
za
tio
n
(
GW
O
)
.
Fu
r
th
e
r
,
th
e
y
u
s
ed
o
th
er
ML
ap
p
r
o
ac
h
es
lik
e
r
a
n
d
o
m
f
o
r
est
(
R
F),
m
u
lti
-
lay
e
r
-
p
er
ce
p
tr
o
n
(
ML
P),
d
ec
is
io
n
-
tr
ee
(
DT
)
,
k
-
n
ea
r
est
-
n
eig
h
b
o
u
r
(
KNN)
,
NB
,
L
R
,
A
NN,
SV
M
,
an
d
C
NN.
T
h
e
r
esu
lts
s
h
o
wed
th
at
th
e
p
r
o
p
o
s
ed
ap
p
r
o
ac
h
u
s
in
g
GW
O
ac
h
iev
ed
0
.
9
8
,
0
.
9
7
3
,
a
n
d
0
.
9
6
2
F
-
s
co
r
e
f
o
r
PIM
A,
ADRC
an
d
FHD
d
atasets
r
esp
ec
tiv
ely
.
Him
i
et
a
l.
[
5
5
]
,
co
n
s
id
er
ed
d
em
o
g
r
ap
h
ic
in
f
o
r
m
atio
n
,
b
io
ch
em
ical
p
ar
a
m
ete
r
s
,
s
leep
p
atter
n
s
,
an
d
s
tr
es
s
l
ev
els
f
o
r
p
r
ed
ictin
g
1
2
k
in
d
o
f
d
is
ea
s
es
wh
ich
also
in
clu
d
ed
d
iab
etes,
ca
lled
a
s
m
ed
ical
AI
(
Me
d
Ai)
.
T
h
is
wo
r
k
co
n
s
tr
u
cte
d
a
d
ataset
wh
ich
co
n
s
is
ted
o
f
v
a
r
io
u
s
d
em
o
g
r
ap
h
i
c
in
f
o
r
m
atio
n
,
s
leep
in
g
p
atter
n
s
,
b
i
o
ch
em
i
ca
l
p
ar
am
eter
s
an
d
s
tr
ess
lev
el
u
s
in
g
d
ata
co
llect
ed
f
r
o
m
a
s
m
ar
t
watc
h
f
r
o
m
1
5
0
s
u
b
jects.
T
h
is
wo
r
k
c
o
n
s
id
er
ed
e
v
alu
atin
g
th
e
d
ataset
u
s
in
g
v
ar
i
o
u
s
ML
a
p
p
r
o
ac
h
es
wh
ic
h
in
clu
d
ed
g
r
a
d
ien
t
b
o
o
s
tin
g
(
GB
)
,
s
u
p
p
o
r
t
-
v
ec
t
or
-
r
eg
r
ess
io
n
(
SVR
)
,
SVM,
KNN,
R
F,
XGB
,
L
R
,
an
d
L
STM
.
An
n
u
zz
i
et
a
l.
[
5
6
]
,
co
n
s
id
er
ed
ev
alu
atin
g
th
eir
m
o
d
el
b
y
c
o
n
s
id
er
in
g
h
ea
lth
p
ar
a
m
eter
s
an
d
b
io
ch
em
ical
p
ar
am
eter
s
.
T
h
is
wo
r
k
co
n
s
id
er
ed
two
d
atase
ts
,
i.e
.
,
Dir
ec
tNet
[
5
7
]
,
an
d
Ai4
p
g
[
5
8
]
d
ataset.
I
n
th
is
wo
r
k
,
th
e
y
p
r
o
p
o
s
ed
a
n
ML
ap
p
r
o
ac
h
,
i.e
.
,
f
ee
d
-
f
o
r
war
d
-
n
eu
r
al
-
n
et
wo
r
k
(
FF
NN)
.
T
h
e
ev
alu
atio
n
was
d
o
n
e
o
n
in
ter
-
s
u
b
jectiv
e
an
aly
s
is
an
d
in
tr
a
-
s
u
b
jectiv
e
an
aly
s
is
.
T
h
e
r
o
o
t
m
ea
n
s
q
u
ar
ed
er
r
o
r
(
R
MSE
)
m
etr
ic
was
co
n
s
id
er
ed
f
o
r
ev
al
u
atio
n
.
Als
o
,
th
e
test
s
wer
e
co
n
d
u
cte
d
f
o
r
1
5
,
3
0
,
4
5
,
a
n
d
6
0
m
in
s
an
d
th
e
R
MSE
was
Evaluation Warning : The document was created with Spire.PDF for Python.
I
n
d
o
n
esian
J
E
lec
E
n
g
&
C
o
m
p
Sci
I
SS
N:
2
5
0
2
-
4
7
52
Dia
b
etes d
etec
tio
n
a
n
d
p
r
ed
ic
tio
n
th
r
o
u
g
h
a
mu
ltimo
d
a
l
…
(
Gu
r
u
r
a
j N.
K
u
lka
r
n
i
)
463
ev
alu
ated
.
T
h
e
FF
NN
ap
p
r
o
a
ch
ac
h
iev
e
d
R
MSE
o
f
4
.
1
4
,
8
.
3
0
,
1
3
.
7
2
an
d
1
6
.
6
9
f
o
r
1
5
,
3
0
,
4
5
an
d
6
0
m
i
n
s
r
esp
ec
tiv
ely
f
o
r
Dir
ec
tNet
d
at
aset
an
d
f
o
r
Ai4
p
g
,
th
e
r
esu
lts
wer
e
ev
alu
ated
b
y
c
o
n
s
id
er
i
n
g
v
a
r
io
u
s
f
ea
tu
r
es
an
d
its
R
MSE
s
co
r
e.
T
h
eir
m
ain
aim
was
to
s
elec
t
o
p
ti
m
al
f
ea
tu
r
es
f
o
r
p
r
ed
ictin
g
th
e
g
lu
co
s
e
lev
el
o
f
a
p
atien
t.
De
Pao
la
et
a
l.
[
5
9
]
,
f
o
cu
s
ed
o
n
ev
alu
atin
g
th
e
r
o
le
o
f
p
h
y
s
ical
ac
tiv
ity
f
o
r
p
r
e
d
ictio
n
o
f
g
lu
co
s
e
lev
els
in
T
y
p
e
-
2
d
iab
etic
p
at
ien
ts
.
T
h
is
wo
r
k
was
a
s
im
u
l
atio
n
ap
p
r
o
ac
h
w
h
er
e
th
e
y
f
o
cu
s
ed
o
n
h
o
w
t
h
e
in
ter
leu
k
in
-
6
wh
e
n
in
jecte
d
c
an
h
elp
to
in
cr
ea
s
e
th
e
in
s
u
li
n
lev
el
i
n
a
n
in
d
iv
id
u
al
b
o
d
y
d
u
r
i
n
g
a
p
h
y
s
ical
ac
tiv
ity
.
T
h
e
f
in
d
in
g
s
s
h
o
w
t
h
at
th
is
ap
p
r
o
ac
h
estab
lis
h
ed
a
b
ase
f
o
r
u
s
in
g
p
h
y
s
ical
ac
tiv
ity
p
ar
am
eter
s
f
o
r
d
etec
tio
n
an
d
p
r
ed
ictio
n
o
f
d
ia
b
etes.
3.
F
I
NDING
S
T
h
e
r
esu
lts
an
d
f
in
d
in
g
s
f
r
o
m
th
e
ab
o
v
e
liter
atu
r
e
s
u
r
v
e
y
ar
e
g
iv
e
n
in
T
a
b
le
1
in
Ap
p
en
d
ix
.
I
n
T
ab
le
1
,
th
e
d
atasets
,
m
eth
o
d
s
,
p
ar
am
eter
s
u
s
ed
,
a
n
d
th
e
f
i
n
d
in
g
s
ar
e
d
is
cu
s
s
ed
in
b
r
ief
.
3
.
1
.
L
im
it
a
t
io
ns
T
h
e
liter
atu
r
e
s
u
r
v
ey
r
ev
ea
ls
s
ev
er
al
l
im
itatio
n
s
in
t
h
e
ex
is
tin
g
r
esear
ch
r
elate
d
to
th
e
d
ete
ctio
n
an
d
ea
r
ly
id
en
tific
atio
n
o
f
d
iab
ete
s
u
s
in
g
m
u
ltimo
d
al
d
atasets
an
d
a
co
m
p
r
e
h
en
s
iv
e
s
et
o
f
p
ar
am
eter
s
.
Firstl
y
,
th
er
e
is
a
n
o
tab
le
s
ca
r
city
o
f
s
tu
d
ies
th
at
h
av
e
ex
te
n
s
iv
ely
u
t
ilized
m
u
ltimo
d
al
d
at
asets
en
c
o
m
p
ass
in
g
v
ar
io
u
s
d
ata
ty
p
es
s
u
ch
as
h
ea
lth
p
ar
a
m
eter
s
,
s
leep
p
atter
n
s
,
s
tr
ess
lev
els,
p
h
y
s
ical
ac
tiv
ity
,
b
io
ch
em
ical
m
ar
k
e
r
s
,
an
d
o
p
h
th
alm
ic
p
a
r
am
eter
s
.
T
h
e
la
ck
o
f
in
teg
r
atio
n
an
d
a
n
aly
s
is
o
f
m
u
ltip
le
d
ata
s
o
u
r
ce
s
af
f
ec
t
s
th
e
d
ev
elo
p
m
en
t
o
f
h
o
li
s
tic
an
d
ac
cu
r
ate
d
iag
n
o
s
tic
m
o
d
els
f
o
r
d
iab
etes.
T
h
is
lim
itatio
n
s
u
g
g
ests
a
g
ap
i
n
r
esear
ch
th
at
c
o
u
ld
p
o
ten
tially
y
ield
m
o
r
e
r
o
b
u
s
t
an
d
co
m
p
r
e
h
en
s
iv
e
ap
p
r
o
ac
h
es
f
o
r
d
iab
etes
d
etec
tio
n
.
S
ec
o
n
d
ly
,
with
in
th
e
lim
ited
s
tu
d
ies
th
at
d
o
c
o
n
s
id
er
m
u
ltimo
d
al
d
ata
s
ets,
th
er
e
i
s
a
g
ap
o
f
wo
r
k
s
p
ec
if
ically
f
o
cu
s
ed
o
n
th
e
ea
r
ly
id
en
tific
atio
n
o
f
d
iab
etes.
E
ar
ly
d
etec
tio
n
is
cr
u
cial
f
o
r
tim
ely
in
ter
v
en
tio
n
an
d
m
an
ag
e
m
en
t,
y
et
th
e
cu
r
r
en
t
liter
atu
r
e
lack
s
s
u
f
f
icien
t
em
p
h
asis
o
n
lev
er
ag
in
g
d
iv
e
r
s
e
d
ata
m
o
d
alities
f
o
r
ea
r
ly
d
iag
n
o
s
is
.
T
h
is
g
ap
is
s
ig
n
if
ican
t
as
ea
r
ly
id
en
tific
ati
o
n
ca
n
lead
t
o
b
etter
o
u
tco
m
e
s
f
o
r
in
d
iv
id
u
als
at
r
is
k
o
f
d
e
v
elo
p
in
g
d
ia
b
etes
o
r
th
o
s
e
in
th
e
ea
r
ly
s
tag
es o
f
th
e
d
is
ea
s
e.
Mo
r
eo
v
er
,
th
e
liter
atu
r
e
also
h
ig
h
lig
h
ts
a
lack
o
f
co
m
p
r
eh
en
s
iv
e
s
tu
d
ies t
h
at
s
im
u
ltan
eo
u
s
ly
co
n
s
id
er
d
em
o
g
r
a
p
h
ics,
h
ea
lth
p
ar
am
et
er
s
,
b
io
ch
e
m
ical
p
a
r
am
eter
s
,
p
h
y
s
ical
ac
tiv
ity
,
an
d
o
p
h
th
al
m
ic
p
ar
am
ete
r
s
f
o
r
id
en
tify
in
g
d
iab
etes.
Diab
etes
is
a
co
m
p
lex
m
etab
o
lic
d
is
o
r
d
er
in
f
lu
e
n
ce
d
b
y
v
ar
io
u
s
f
ac
t
o
r
s
,
in
clu
d
in
g
ag
e,
g
en
d
er
,
life
s
ty
le,
an
d
p
h
y
s
io
l
o
g
ical
m
ar
k
e
r
s
.
Ho
wev
er
,
th
e
ex
is
tin
g
r
esear
ch
o
f
ten
o
v
er
l
o
o
k
s
th
e
s
y
n
e
r
g
is
tic
ef
f
ec
ts
o
f
th
ese
d
iv
e
r
s
e
p
ar
a
m
eter
s
,
lead
in
g
to
i
n
co
m
p
let
e
ass
es
s
m
en
ts
o
f
d
iab
etes
r
is
k
an
d
p
r
o
g
r
ess
io
n
.
T
h
e
lim
itatio
n
s
id
en
tifie
d
in
th
e
liter
atu
r
e
s
u
r
v
ey
u
n
d
er
s
c
o
r
e
th
e
n
ee
d
f
o
r
m
o
r
e
ex
ten
s
iv
e
an
d
in
teg
r
ated
r
esear
ch
ef
f
o
r
ts
th
at
lev
er
ag
e
m
u
ltimo
d
al
d
atasets
,
p
r
io
r
itize
ea
r
ly
id
en
tific
atio
n
s
tr
ateg
ies,
an
d
co
n
s
id
er
a
co
m
p
r
eh
e
n
s
iv
e
r
an
g
e
o
f
d
em
o
g
r
ap
h
ic
an
d
p
h
y
s
io
lo
g
ical
p
ar
am
eter
s
.
Ad
d
r
ess
in
g
th
ese
g
ap
s
ca
n
s
i
g
n
if
ican
tly
en
h
an
ce
th
e
ac
cu
r
ac
y
,
tim
elin
ess
,
an
d
ef
f
ec
tiv
en
ess
o
f
d
iab
e
tes d
etec
tio
n
an
d
m
a
n
ag
em
e
n
t a
p
p
r
o
ac
h
es.
3
.
2
.
G
a
ps
,
is
s
ues
,
a
nd
c
ha
lle
ng
es
T
h
e
g
ap
s
,
is
s
u
es a
n
d
ch
allen
g
es id
en
tifie
d
f
r
o
m
th
e
ab
o
v
e
s
ec
tio
n
is
as
;
a.
L
im
ited
u
s
e
o
f
m
u
ltimo
d
al
d
at
asets
−
I
n
s
u
f
f
icien
t
ex
p
lo
r
atio
n
an
d
u
tili
za
tio
n
o
f
m
u
ltimo
d
al
d
at
asets
co
m
b
in
in
g
d
iv
er
s
e
d
ata
ty
p
es
s
u
ch
as
h
ea
lth
p
ar
am
eter
s
,
s
leep
p
atte
r
n
s
,
s
tr
ess
lev
els,
p
h
y
s
ical
ac
t
iv
ity
,
b
io
ch
em
ical
m
ar
k
er
s
,
a
n
d
o
p
h
th
alm
ic
p
ar
am
eter
s
.
−
L
ac
k
o
f
in
teg
r
ate
d
an
aly
s
is
ac
r
o
s
s
m
u
ltip
le
d
ata
s
o
u
r
ce
s
,
af
f
ec
tin
g
th
e
d
e
v
elo
p
m
e
n
t
o
f
c
o
m
p
r
eh
e
n
s
iv
e
d
iag
n
o
s
tic
m
o
d
els f
o
r
d
ia
b
etes.
b.
Neg
lig
en
ce
o
f
ea
r
ly
id
e
n
tific
atio
n
−
Scar
city
o
f
r
esear
ch
f
o
cu
s
ed
s
p
ec
if
ically
o
n
ea
r
ly
id
en
tific
at
io
n
o
f
d
iab
etes,
wh
ich
is
cr
u
cial
f
o
r
tim
ely
in
ter
v
en
tio
n
a
n
d
im
p
r
o
v
ed
p
at
ien
t o
u
tco
m
es.
−
Miss
ed
o
p
p
o
r
tu
n
ities
to
lev
e
r
a
g
e
d
iv
er
s
e
d
ata
m
o
d
alities
f
o
r
ea
r
ly
d
etec
tio
n
a
n
d
in
ter
v
en
tio
n
s
tr
ateg
ies.
c.
I
n
ad
eq
u
ate
co
n
s
id
er
atio
n
o
f
d
em
o
g
r
ap
h
ics an
d
o
th
er
p
a
r
am
eter
s
−
L
im
ited
s
tu
d
ies
th
at
s
im
u
lt
an
eo
u
s
ly
c
o
n
s
id
e
r
d
em
o
g
r
a
p
h
ics
(
ag
e,
g
e
n
d
er
)
,
h
e
alth
p
ar
am
eter
s
,
b
io
ch
em
ical
p
ar
am
eter
s
,
p
h
y
s
ical
ac
tiv
ity
,
an
d
o
p
h
th
alm
ic
p
ar
am
eter
s
f
o
r
d
iab
etes id
en
tifi
ca
tio
n
.
−
Failu
r
e
to
ad
d
r
ess
th
e
s
y
n
er
g
is
tic
ef
f
ec
ts
o
f
th
ese
d
iv
er
s
e
p
ar
am
eter
s
,
lead
in
g
to
in
c
o
m
p
let
e
ass
es
s
m
en
ts
o
f
d
iab
etes r
is
k
an
d
p
r
o
g
r
ess
io
n
.
d.
Nee
d
f
o
r
a
d
v
an
ce
d
an
aly
tical
t
ec
h
n
iq
u
es
−
L
ac
k
o
f
ad
o
p
tio
n
o
f
a
d
v
an
ce
d
an
aly
tical
tech
n
iq
u
es
s
u
ch
as
ML
alg
o
r
ith
m
s
,
DL
m
o
d
el
s
,
an
d
AI
f
o
r
p
r
o
ce
s
s
in
g
an
d
in
ter
p
r
etin
g
m
u
ltimo
d
al
d
ata
in
d
iab
etes r
esear
ch
.
−
C
h
allen
g
es
in
d
e
v
elo
p
in
g
ac
cu
r
ate
an
d
s
ca
lab
le
p
r
e
d
ictiv
e
m
o
d
els
d
u
e
to
d
ata
co
m
p
le
x
ity
,
h
eter
o
g
en
eity
,
an
d
v
o
lu
m
e.
Evaluation Warning : The document was created with Spire.PDF for Python.
I
SS
N
:
2
5
0
2
-
4
7
52
In
d
o
n
esian
J
E
lec
E
n
g
&
C
o
m
p
Sci
,
Vo
l.
3
8
,
No
.
1
,
Ap
r
il
20
2
5
:
4
5
9
-
4
6
8
464
Ad
d
r
ess
in
g
th
ese
g
a
p
s
,
is
s
u
es,
an
d
c
h
allen
g
es
is
ess
en
tial
f
o
r
ad
v
a
n
cin
g
th
e
f
iel
d
o
f
d
iab
etes
d
etec
tio
n
,
p
r
o
m
o
tin
g
ea
r
ly
id
en
tific
atio
n
s
tr
ateg
ies,
an
d
im
p
r
o
v
in
g
p
atien
t
o
u
tco
m
es
th
r
o
u
g
h
p
e
r
s
o
n
alize
d
an
d
d
ata
-
d
r
iv
en
h
ea
lth
ca
r
e
in
ter
v
e
n
tio
n
s
.
Hen
ce
,
in
th
e
n
ex
t
s
ec
tio
n
,
a
p
o
s
s
ib
le
s
o
lu
tio
n
to
a
d
d
r
ess
th
ese
g
ap
s
,
is
s
u
es
an
d
ch
allen
g
es is
p
r
esen
ted
.
4.
M
UL
T
I
M
O
DA
L
F
RA
M
E
W
O
RK
I
n
th
is
s
ec
tio
n
,
a
n
o
v
el
m
u
lti
m
o
d
al
f
r
am
ewo
r
k
is
in
tr
o
d
u
c
ed
f
o
r
th
e
d
etec
tio
n
a
n
d
p
r
e
d
ictio
n
o
f
d
iab
etes,
as
p
r
esen
ted
i
n
Fig
u
r
e
2
.
T
h
e
f
r
am
ewo
r
k
b
eg
in
s
b
y
in
co
r
p
o
r
atin
g
a
co
m
p
r
e
h
en
s
iv
e
m
u
ltimo
d
al
d
ataset
th
at
en
c
o
m
p
ass
es
a
wid
e
r
an
g
e
o
f
p
ar
am
eter
s
c
r
u
cial
f
o
r
d
ia
b
etes
ass
ess
m
en
t
.
T
h
ese
p
ar
am
eter
s
in
clu
d
e
d
e
m
o
g
r
ap
h
ic
in
f
o
r
m
at
io
n
s
u
ch
as
ag
e,
g
en
d
er
,
an
d
e
th
n
icity
,
h
ea
lth
m
etr
ics
lik
e
g
l
u
co
s
e
lev
els,
b
lo
o
d
p
r
ess
u
r
e,
an
d
h
ea
r
t
r
ate,
b
i
o
ch
em
ical
m
ar
k
er
s
s
u
ch
as
lip
id
p
r
o
f
iles
an
d
in
s
u
lin
lev
els,
p
h
y
s
ical
ac
tiv
ity
d
ata
in
clu
d
in
g
ex
e
r
cise
f
r
eq
u
en
c
y
an
d
in
ten
s
ity
,
o
p
h
t
h
alm
ic
p
ar
am
eter
s
lik
e
r
etin
al
ch
an
g
es,
an
d
d
ata
o
n
s
leep
p
atter
n
s
an
d
s
tr
ess
lev
els.
On
ce
th
e
m
u
ltimo
d
al
d
ataset
is
s
tr
u
ctu
r
ed
,
a
ML
ap
p
r
o
ac
h
will
b
e
em
p
lo
y
e
d
f
o
r
th
e
task
o
f
d
etec
tin
g
an
d
p
r
ed
icti
n
g
d
iab
etics.
ML
alg
o
r
ith
m
s
ar
e
p
ar
ticu
lar
ly
well
-
s
u
ited
f
o
r
h
an
d
lin
g
co
m
p
le
x
an
d
m
u
ltid
im
e
n
s
io
n
al
d
ata,
m
ak
in
g
th
em
an
id
ea
l
c
h
o
ice
f
o
r
an
aly
zin
g
th
e
d
iv
e
r
s
e
p
ar
am
eter
s
p
r
esen
t
in
th
e
m
u
ltimo
d
al
d
ataset.
T
ec
h
n
i
q
u
es
s
u
ch
as
s
u
p
e
r
v
is
ed
lear
n
in
g
,
en
s
em
b
le
m
et
h
o
d
s
,
a
n
d
DL
m
ay
b
e
u
tili
ze
d
with
in
th
e
ML
f
r
am
ewo
r
k
to
ex
tr
ac
t
p
atter
n
s
,
c
o
r
r
elatio
n
s
,
an
d
p
r
ed
ictiv
e
in
s
ig
h
ts
f
r
o
m
th
e
d
ata.
Fo
llo
win
g
th
e
ap
p
licatio
n
o
f
ML
alg
o
r
ith
m
s
,
th
e
f
r
am
ewo
r
k
p
r
o
ce
e
d
s
to
ev
alu
ate
th
e
p
e
r
f
o
r
m
an
ce
o
f
th
e
d
ia
b
etes
d
etec
tio
n
an
d
p
r
ed
ictio
n
m
o
d
els.
T
h
is
ev
alu
atio
n
is
co
n
d
u
cted
u
s
in
g
a
r
an
g
e
o
f
p
e
r
f
o
r
m
a
n
ce
m
etr
ics,
in
clu
d
in
g
ac
cu
r
ac
y
,
p
r
ec
is
io
n
,
r
ec
all,
an
d
F
-
s
co
r
e,
am
o
n
g
o
th
er
s
.
T
h
ese
m
etr
ics p
r
o
v
id
e
q
u
a
n
titativ
e
m
ea
s
u
r
es
o
f
th
e
m
o
d
el
’
s
ef
f
ec
tiv
en
ess
in
co
r
r
ec
tly
id
e
n
tify
in
g
d
ia
b
e
tic
in
d
iv
id
u
als,
ca
p
tu
r
i
n
g
t
r
u
e
p
o
s
itiv
e
a
n
d
tr
u
e
n
eg
ativ
e
r
ates,
an
d
m
in
im
izin
g
f
alse
p
o
s
itiv
es
an
d
f
alse
n
e
g
ativ
es.
Ad
d
itio
n
ally
,
th
e
ev
a
lu
atio
n
p
r
o
ce
s
s
ca
n
co
n
s
id
er
o
th
er
r
elev
an
t
p
ar
a
m
eter
s
an
d
asp
ec
ts
,
s
u
c
h
as
m
o
d
el
r
o
b
u
s
tn
ess
,
s
ca
lab
ilit
y
,
in
ter
p
r
eta
b
ilit
y
,
an
d
co
m
p
u
tatio
n
al
ef
f
icien
cy
.
B
y
ex
am
in
in
g
th
ese
m
etr
ics
co
m
p
r
eh
en
s
iv
ely
,
th
e
f
r
am
ewo
r
k
aim
s
to
ass
ess
th
e
o
v
er
all
ef
f
ec
tiv
en
ess
an
d
r
eli
ab
ilit
y
o
f
th
e
ML
-
b
ased
ap
p
r
o
ac
h
in
d
iab
etes
d
etec
tio
n
an
d
p
r
ed
ic
tio
n
task
s
.
T
h
e
p
r
esen
ted
m
u
ltimo
d
al
f
r
a
m
ewo
r
k
i
n
teg
r
ates
d
iv
e
r
s
e
d
a
ta
s
o
u
r
ce
s
,
lev
e
r
ag
es
ad
v
an
ce
d
ML
tec
h
n
iq
u
es,
an
d
em
p
lo
y
s
r
i
g
o
r
o
u
s
ev
alu
ati
o
n
m
ea
s
u
r
es
t
o
en
h
an
ce
th
e
ac
cu
r
ac
y
,
r
eliab
ilit
y
,
a
n
d
clin
ical
u
tili
ty
o
f
d
iab
etes
d
etec
tio
n
an
d
p
r
e
d
ictio
n
m
o
d
e
ls
.
T
h
is
h
o
lis
tic
ap
p
r
o
ac
h
n
o
t
o
n
ly
ad
d
r
ess
es
th
e
co
m
p
lex
i
ty
o
f
d
iab
etes
as
a
m
u
ltifa
cto
r
ial
co
n
d
itio
n
b
u
t a
ls
o
p
r
o
v
id
es a
s
y
s
tem
atic
an
d
d
ata
-
d
r
iv
en
m
eth
o
d
o
lo
g
y
f
o
r
im
p
r
o
v
i
n
g
h
ea
lth
ca
r
e
o
u
tco
m
es in
d
ia
b
etic
ca
r
e
an
d
m
an
ag
em
en
t.
Fig
u
r
e
2
.
Mu
ltim
o
d
al
f
r
a
m
ewo
r
k
f
o
r
d
etec
tio
n
an
d
p
r
e
d
ictio
n
o
f
d
iab
etics
5.
CO
NCLU
SI
O
N
T
h
is
wo
r
k
h
as
d
elv
ed
in
to
th
e
in
tr
icate
lan
d
s
ca
p
e
o
f
d
iab
e
tes
d
etec
tio
n
an
d
p
r
ed
ictio
n
,
p
ar
ticu
lar
ly
f
o
cu
s
in
g
o
n
th
e
u
tili
za
tio
n
o
f
AI
tech
n
iq
u
es
an
d
m
u
ltimo
d
a
l
d
atasets
.
Diab
etes
i
s
a
cr
itic
a
l
h
ea
lth
is
s
u
e
with
r
is
in
g
p
r
ev
alen
ce
wo
r
ld
wid
e,
m
ak
in
g
th
e
d
ev
elo
p
m
en
t
o
f
ac
cu
r
ate
d
etec
tio
n
a
n
d
p
r
ed
ic
tio
n
m
eth
o
d
o
lo
g
ies
ess
en
tial
f
o
r
im
p
r
o
v
in
g
p
atien
t
o
u
tco
m
es.
T
h
e
s
tu
d
y
’
s
co
m
p
r
eh
en
s
iv
e
r
ev
iew
o
f
ex
is
tin
g
liter
atu
r
e
p
r
o
v
id
e
d
v
alu
ab
le
in
s
ig
h
ts
in
to
th
e
d
i
v
er
s
e
p
ar
am
eter
s
an
d
m
eth
o
d
o
l
o
g
ies
em
p
lo
y
ed
i
n
p
r
e
v
io
u
s
d
iab
etes
ass
e
s
s
m
en
t
s
tu
d
ies.
T
h
e
im
p
o
r
tan
ce
o
f
c
o
n
s
id
er
in
g
m
u
lti
-
p
ar
am
eter
s
,
s
u
ch
as
d
em
o
g
r
a
p
h
ic,
h
ea
lth
,
b
io
ch
em
ical,
p
h
y
s
ical
ac
tiv
ity
,
o
p
h
th
alm
ic,
s
leep
,
an
d
s
tr
ess
f
ac
to
r
s
,
wa
s
h
ig
h
lig
h
ted
,
alo
n
g
with
th
e
s
ig
n
if
ican
ce
o
f
ad
v
an
ce
d
AI
tech
n
iq
u
es
lik
e
ML
an
d
DL
in
an
aly
zin
g
c
o
m
p
lex
d
a
tasets
an
d
im
p
r
o
v
in
g
p
r
ed
ic
tiv
e
ac
cu
r
ac
y
.
T
h
e
Evaluation Warning : The document was created with Spire.PDF for Python.
I
n
d
o
n
esian
J
E
lec
E
n
g
&
C
o
m
p
Sci
I
SS
N:
2
5
0
2
-
4
7
52
Dia
b
etes d
etec
tio
n
a
n
d
p
r
ed
ic
tio
n
th
r
o
u
g
h
a
mu
ltimo
d
a
l
…
(
Gu
r
u
r
a
j N.
K
u
lka
r
n
i
)
465
lim
itatio
n
s
an
d
ch
allen
g
es
id
en
tifie
d
in
ex
is
tin
g
r
esear
c
h
,
s
u
ch
as
th
e
n
ee
d
f
o
r
b
e
tter
in
teg
r
atio
n
o
f
m
u
ltimo
d
al
d
ata,
e
ar
ly
d
etec
ti
o
n
s
tr
ateg
ies,
an
d
co
m
p
r
eh
e
n
s
iv
e
m
o
d
el
ev
alu
atio
n
,
u
n
d
er
s
co
r
ed
cr
itical
ar
ea
s
f
o
r
im
p
r
o
v
em
en
t.
T
o
ad
d
r
ess
th
ese
g
a
p
s
,
th
e
s
tu
d
y
p
r
o
p
o
s
ed
a
n
o
v
el
m
u
ltimo
d
al
f
r
am
ewo
r
k
f
o
r
d
iab
etes
d
etec
tio
n
an
d
p
r
e
d
ictio
n
,
em
p
h
asizin
g
th
e
in
teg
r
atio
n
o
f
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P
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[
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P
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In
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