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m
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J
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CT
)
Vo
l.
13
,
No
.
3
,
Dec
em
b
er
20
24
,
p
p
.
49
9
~
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SS
N:
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.
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Ens
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ise
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sis o
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a
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m
b
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c
k
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las
sifier
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.
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s
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ll
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d
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h
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b
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se
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m
s.
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e
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o
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th
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m
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l
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s
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t
ial
a
s
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stru
m
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n
t
fo
r
p
re
d
ictin
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d
iab
e
tes
in
in
d
iv
i
d
u
a
ls.
K
ey
w
o
r
d
s
:
Dec
is
io
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tr
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Diab
etes p
r
ed
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Ma
ch
in
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in
g
Su
p
p
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t
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to
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m
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R
an
d
o
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f
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T
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is i
s
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c
c
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ss
a
rticle
u
n
d
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r th
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CC B
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SA
li
c
e
n
se
.
C
o
r
r
e
s
p
o
nd
ing
A
uth
o
r
:
Yash
Ven
k
atesh
B
ellar
y
Dep
ar
tm
en
t o
f
E
lectr
o
n
ics an
d
T
elec
o
m
m
u
n
icatio
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E
n
g
in
ee
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Pimp
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lleg
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Sav
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Ph
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Pu
n
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Un
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s
ity
Secto
r
2
6
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Pra
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Nig
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4
1
1
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4
4
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I
n
d
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ail: b
ellar
y
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2
3
@
g
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ail.
co
m
1.
I
NT
RO
D
UCT
I
O
N
E
ac
h
y
ea
r
,
n
o
n
-
co
m
m
u
n
ica
b
le
d
is
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s
es
ar
e
ac
co
u
n
tab
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f
o
r
n
ea
r
ly
7
1
%
o
f
all
f
atalities
g
lo
b
ally
,
o
r
m
o
r
e
t
h
an
4
1
m
illi
o
n
p
r
em
at
u
r
e
d
ea
th
s
[
1]
.
I
f
n
o
n
-
co
m
m
u
n
i
ca
b
le
d
is
ea
s
es
ar
e
n
o
t
t
r
ea
ted
,
it
is
p
r
ed
icted
th
at
th
ey
will
r
esu
lt
in
5
2
m
illi
o
n
d
ea
th
s
y
ea
r
ly
b
y
2
0
3
0
[
2
]
.
Diab
etes
is
th
e
m
o
s
t
p
r
ev
alen
t
n
o
n
-
c
o
m
m
u
n
icab
le
d
is
ea
s
e,
co
n
tr
ib
u
tin
g
t
o
ap
p
r
o
x
im
ately
4
6
.
2
%
o
f
all
f
atalities
[
3
]
,
[
4
]
.
T
y
p
e
2
d
iab
e
tes
is
a
p
er
s
is
ten
t
m
etab
o
lic
d
is
o
r
d
er
ch
a
r
ac
ter
i
ze
d
b
y
elev
ated
b
lo
o
d
s
u
g
a
r
lev
els.
I
t
is
co
m
m
o
n
ly
b
r
o
u
g
h
t
o
n
b
y
th
e
b
o
d
y
’
s
in
ca
p
ac
ity
to
u
tili
ze
its
o
wn
p
r
o
d
u
ce
d
i
n
s
u
lin
[
5
]
,
[
6
]
.
Patien
ts
d
iag
n
o
s
ed
with
d
ia
b
etes
ar
e
at
an
in
cr
ea
s
e
d
r
is
k
o
f
m
o
r
tality
d
u
e
to
s
tr
o
k
e
an
d
o
th
e
r
ass
o
ciate
d
ca
u
s
es [
7]
.
Ho
wev
er
,
with
c
o
n
s
is
ten
t su
r
v
eillan
ce
o
f
b
lo
o
d
g
lu
co
s
e
lev
els,
d
iab
etic
co
m
p
l
icatio
n
s
ca
n
b
e
ef
f
ec
ti
v
ely
p
r
e
v
en
ted
o
r
m
itig
ated
[
8
]
,
[
9
]
.
Acc
o
r
d
in
g
to
p
r
o
jectio
n
s
,
th
e
n
u
m
b
er
o
f
p
e
o
p
le
liv
in
g
with
d
iab
etes
in
d
ev
elo
p
in
g
co
u
n
tr
ies
will
r
ea
ch
2
2
8
m
illi
o
n
b
y
2
0
3
0
,
i
m
p
o
s
in
g
a
s
ig
n
if
ican
t
s
tr
ain
o
n
h
ea
lth
ca
r
e
s
y
s
tem
s
[
1
0
]
.
A
n
u
m
b
e
r
o
f
r
ec
en
t
Evaluation Warning : The document was created with Spire.PDF for Python.
I
SS
N
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2
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I
n
t J I
n
f
&
C
o
m
m
u
n
T
ec
h
n
o
l
,
Vo
l.
13
,
No
.
3
,
Dec
em
b
er
20
24
:
49
9
-
50
8
500
r
esear
ch
in
v
esti
g
atio
n
s
h
av
e
u
tili
ze
d
m
ac
h
in
e
lear
n
in
g
tech
n
o
lo
g
y
to
ass
is
t
in
th
e
d
etec
tio
n
o
f
d
is
ea
s
es,
s
p
ec
if
i
ca
lly
in
th
e
p
r
ec
is
e
id
e
n
tific
atio
n
o
f
d
ia
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etes
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s
in
g
h
ea
lth
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ata
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m
a
n
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d
iv
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d
u
al.
T
h
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tr
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aid
s
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als
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im
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itio
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ly
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icac
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s
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ield
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ap
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at
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al
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ates
n
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m
er
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s
class
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m
o
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els
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s
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ck
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g
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ag
g
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g
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o
r
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o
o
s
tin
g
tech
n
iq
u
es.
I
ts
ac
cu
r
ac
y
h
as
b
ee
n
s
h
o
wn
to
b
e
s
u
p
er
io
r
to
th
e
u
tili
za
tio
n
o
f
s
o
litar
y
alg
o
r
ith
m
s
.
Pre
v
i
o
u
s
s
tu
d
ies
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av
e
s
u
cc
ess
f
u
lly
im
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lem
en
ted
th
e
en
s
em
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le
m
eth
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d
to
ass
is
t
m
ed
ical
d
ec
is
io
n
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m
ak
in
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a
n
d
p
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s
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is
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ased
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is
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ap
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.
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h
is
m
o
d
el
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tili
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s
p
ar
ticu
lar
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ed
ical
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ar
am
eter
s
an
d
h
ea
lth
m
ea
s
u
r
es
to
f
o
r
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ast
th
e
p
r
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ce
o
f
d
iab
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in
an
in
d
iv
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d
u
al.
T
h
e
m
o
d
el
is
tr
ain
ed
u
s
in
g
th
e
Pima
I
n
d
ian
Diab
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s
Data
s
et
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PID
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wh
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o
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m
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s
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t c
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R
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th
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r
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ev
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ar
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ig
h
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ted
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tio
n
2
.
I
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d
d
itio
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to
d
escr
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in
g
th
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m
eth
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d
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g
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tili
z
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cr
ea
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d
d
ev
elo
p
in
g
th
e
p
r
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p
o
s
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m
o
d
el,
s
ec
tio
n
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p
r
o
v
id
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th
e
o
r
eti
ca
l
d
etails
r
eg
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d
in
g
th
e
alg
o
r
i
th
m
s
an
d
p
r
o
ce
s
s
es.
I
n
s
ec
tio
n
4
,
th
e
o
u
tco
m
es
an
d
p
er
f
o
r
m
a
n
ce
o
f
th
e
p
r
o
p
o
s
ed
m
o
d
el
ar
e
ass
ess
ed
,
with
a
co
m
p
ar
is
o
n
m
ad
e
b
etwe
en
th
e
m
o
d
el
’
s
p
a
r
am
et
er
s
an
d
th
o
s
e
o
f
t
h
e
b
aselin
e
m
eth
o
d
.
I
n
ad
d
itio
n
to
p
r
ese
n
tin
g
a
c
o
n
clu
d
in
g
s
tatem
en
t
an
d
f
in
al
ev
alu
atio
n
o
f
th
e
r
esear
ch
f
in
d
in
g
s
,
s
ec
tio
n
5
pr
o
v
id
es
th
e
co
n
clu
d
i
n
g
s
tatem
en
t
o
f
th
e
s
tu
d
y
an
d
i
n
v
esti
g
ates p
o
s
s
ib
le
f
u
tu
r
e
a
p
p
licatio
n
s
o
f
th
e
d
e
s
ig
n
ed
m
o
d
el.
2.
L
I
T
E
R
AT
U
RE
R
E
VI
E
W
Diab
etes
is
a
s
ig
n
if
ican
t
etio
lo
g
ical
ag
en
t
in
th
e
d
ev
el
o
p
m
en
t
o
f
n
u
m
er
o
u
s
d
is
ea
s
es
an
d
h
ea
lth
co
n
d
itio
n
s
.
E
ar
ly
d
etec
tio
n
m
ay
en
ab
le
in
d
i
v
id
u
als
to
im
p
lem
en
t
p
r
e
v
en
tativ
e
m
ea
s
u
r
es
to
s
u
r
m
o
u
n
t
th
is
co
n
d
itio
n
.
Ma
c
h
in
e
lear
n
i
n
g
ca
n
p
r
o
d
u
ce
a
p
r
ed
ictiv
e
m
o
d
el
f
o
r
th
e
ea
r
ly
d
etec
tio
n
o
f
d
iab
etes
an
d
o
th
e
r
m
alad
ies
b
y
u
tili
zin
g
in
d
iv
id
u
al
m
ed
ical
d
ata.
Pre
d
ictin
g
o
r
p
r
ed
i
s
p
o
s
in
g
to
d
iab
etes
h
as
b
ee
n
th
e
s
u
b
ject
o
f
n
u
m
er
o
u
s
r
esear
ch
s
tu
d
ies d
e
m
o
n
s
tr
atin
g
n
o
tewo
r
th
y
o
u
tco
m
es u
s
in
g
m
ac
h
in
e
lear
n
in
g
m
o
d
els.
So
n
ar
an
d
Ma
lin
i
[
1
1
]
d
ev
is
ed
a
s
y
s
tem
th
at
ef
f
ec
tiv
ely
p
r
ed
icted
an
in
d
iv
id
u
al
’
s
d
iab
etic
r
is
k
b
y
co
m
b
in
in
g
m
u
ltip
le
a
l
g
o
r
ith
m
s
.
T
h
is
r
esear
ch
m
a
d
e
u
s
e
o
f
s
u
p
p
o
r
t
v
ec
to
r
m
ac
h
in
e
(
SVM)
,
d
ec
is
io
n
tr
ee
(
DT
)
,
a
n
d
Naiv
e
B
ay
es
(
NB
)
alg
o
r
ith
m
s
.
A
r
o
b
u
s
t
f
r
a
m
ewo
r
k
f
o
r
p
r
e
d
ictin
g
d
iab
ete
s
is
d
ev
elo
p
e
d
b
y
Hasan
et
a
l.
[
1
2
]
.
T
h
e
f
r
am
e
wo
r
k
in
co
r
p
o
r
ated
v
ar
io
u
s
m
a
ch
in
e
lear
n
in
g
(
ML
)
tech
n
iq
u
es,
in
clu
d
in
g
f
ea
tu
r
e
s
elec
tio
n
,
K
-
f
o
ld
cr
o
s
s
-
v
alid
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n
,
o
u
tlier
r
ejec
tio
n
an
d
f
illi
n
g
,
m
is
s
in
g
v
alu
e
f
illi
n
g
,
an
d
d
ata
s
tan
d
ar
d
izatio
n
.
C
o
m
b
in
in
g
t
h
ese
m
eth
o
d
s
im
p
r
o
v
e
d
th
e
ac
cu
r
ac
y
o
f
t
h
e
p
r
ed
icted
weig
h
ts
f
o
r
ca
lcu
latin
g
th
e
th
e
r
ec
eiv
e
r
o
p
er
atin
g
ch
a
r
ac
ter
is
tic
(
R
O
C
)
ar
ea
u
n
d
er
c
u
r
v
e
(
AUC)
o
f
th
e
ML
m
o
d
el.
Alan
az
i
an
d
Me
zh
er
[
1
3
]
co
n
d
u
cte
d
a
s
tu
d
y
in
wh
ic
h
th
ey
p
r
e
d
icted
d
iab
etes
u
s
in
g
a
co
m
b
in
atio
n
o
f
th
e
SVM
an
d
r
an
d
o
m
f
o
r
est
(
R
F)
alg
o
r
ith
m
s
.
T
h
e
R
OC
f
o
r
th
e
p
r
o
p
o
s
ed
m
o
d
el
is
9
9
%,
a
n
d
i
ts
ac
cu
r
ac
y
r
ate
is
9
8
%.
I
n
ter
m
s
o
f
ac
cu
r
ac
y
,
t
h
e
r
esu
lt
in
d
icate
s
th
at
th
e
RF
m
e
th
o
d
o
u
tp
e
r
f
o
r
m
s
th
e
SVM.
I
n
th
eir
s
tu
d
y
,
Su
n
g
e
et
a
l.
[
1
4
]
em
p
lo
y
ed
th
e
C
4
.
5
alg
o
r
ith
m
a
n
d
DT
m
o
d
els
to
d
eter
m
in
e
th
at
th
e
m
o
d
el
’
s
ac
c
u
r
ac
y
is
a
r
o
u
n
d
7
2
%.
K
u
m
ar
[
1
5
]
d
is
co
v
er
ed
th
at
ea
r
ly
d
iab
etes
p
r
ed
ictio
n
f
o
r
a
p
atien
t
ca
n
b
e
p
er
f
o
r
m
e
d
p
r
e
cisely
u
s
in
g
ML
’
s
R
F
m
eth
o
d
.
B
ab
aso
et
a
l.
[
1
6
]
in
v
esti
g
ated
ML
m
eth
o
d
o
lo
g
i
es,
in
clu
d
in
g
SVM,
K
-
n
ea
r
est
n
eig
h
b
o
r
(
KNN)
,
n
eu
r
al
n
etw
o
r
k
s
,
NB
,
a
n
d
d
ee
p
lear
n
in
g
alg
o
r
ith
m
s
in
th
eir
in
v
esti
g
atio
n
.
I
n
th
eir
s
tu
d
y
,
Kis
h
o
r
e
et
a
l.
[
1
7
]
in
v
esti
g
ated
t
h
e
m
etr
ics
o
f
m
is
class
if
icatio
n
an
d
ac
cu
r
ac
y
ass
o
ciate
d
with
v
ar
io
u
s
class
if
icatio
n
alg
o
r
ith
m
s
,
in
clu
d
in
g
SVM,
KNN,
DT
,
R
F,
an
d
lo
g
is
tic
r
eg
r
ess
io
n
(
L
R
)
.
R
F e
x
h
ib
its
s
u
p
er
io
r
p
er
f
o
r
m
an
ce
,
b
o
asti
n
g
a
n
ac
cu
r
ac
y
o
f
ap
p
r
o
x
im
ately
7
5
%.
T
h
e
ef
f
icac
y
o
f
NB
an
d
DT
class
if
icatio
n
alg
o
r
ith
m
s
is
ev
alu
ated
b
y
Srik
an
t
h
a
n
d
Dev
er
ap
alli
[
1
8
]
.
T
h
e
al
g
o
r
ith
m
s
ac
h
iev
e
d
ap
p
r
o
x
im
ately
7
5
%
an
d
8
0
%
p
r
ec
is
io
n
m
ea
s
u
r
es.
An
in
v
e
s
tig
atio
n
is
ca
r
r
ied
o
u
t
b
y
Ko
c
an
d
Yen
iad
[
1
9
]
,
em
p
lo
y
in
g
v
ar
io
u
s
class
if
icati
o
n
m
o
d
els,
s
u
ch
as
SVM,
R
F
,
DT
,
KNN,
L
R
,
an
d
g
r
ad
ien
t
b
o
o
s
tin
g
.
A
7
7
%
d
eg
r
ee
o
f
class
if
icatio
n
ac
cu
r
ac
y
in
Diab
etes
m
ellitu
s
is
p
r
ed
icted
b
y
J
ag
g
i
et
a
l.
[
2
0
]
u
t
ilizin
g
well
-
k
n
o
wn
ML
alg
o
r
ith
m
s
,
in
clu
d
in
g
R
F,
KNN,
DT
,
an
d
L
R
.
I
n
co
n
tr
ast
to
all
al
ter
n
ativ
e
m
ac
h
in
e
lear
n
in
g
ap
p
r
o
ac
h
es
ev
alu
ated
,
L
R
ac
h
iev
ed
a
r
e
m
ar
k
ab
le
ac
cu
r
ac
y
o
f
7
8
%
f
o
r
th
e
d
ataset.
An
en
s
em
b
le
-
b
ased
m
u
ltil
ay
er
class
if
icatio
n
alg
o
r
ith
m
was
d
ev
is
ed
b
y
Fit
r
iy
an
i
et
a
l.
[
2
1
]
,
u
tili
zin
g
SVM
an
d
DT
as
b
ase
class
if
ier
s
an
d
L
R
as
th
e
m
eta
-
class
if
ier
.
A
s
u
b
s
t
an
tial
im
p
r
o
v
em
en
t
in
th
e
ac
c
u
r
ac
y
o
f
th
e
class
if
icatio
n
alg
o
r
ith
m
s
is
o
b
s
er
v
ed
.
T
h
e
in
d
iv
id
u
al
class
if
icatio
n
alg
o
r
ith
m
s
ex
h
ib
it
an
ap
p
r
o
x
im
ate
m
ea
n
ac
cu
r
ac
y
o
f
7
4
%,
wh
er
ea
s
th
e
en
s
em
b
le
-
b
ased
class
if
icatio
n
alg
o
r
ith
m
ex
h
ib
its
an
a
p
p
r
o
x
im
ately
8
3
%
m
ea
n
ac
c
u
r
ac
y
.
T
h
is
d
em
o
n
s
tr
ated
th
at
en
s
em
b
le
lear
n
in
g
is
th
e
p
r
ed
o
m
in
an
t
m
ac
h
i
n
e
lear
n
in
g
m
et
h
o
d
th
at
e
n
h
an
ce
d
th
e
m
o
d
el
’
s
p
r
ed
ictiv
e
p
er
f
o
r
m
an
ce
a
n
d
p
r
ec
is
io
n
.
An
en
s
em
b
le
-
b
ased
m
u
ltil
ay
er
s
tack
in
g
class
if
icatio
n
alg
o
r
it
h
m
is
im
p
lem
e
n
ted
b
y
Kala
b
ar
i
g
e
et
a
l.
[
2
2
]
.
T
h
i
s
alg
o
r
ith
m
co
m
p
r
is
ed
two
lay
er
s
o
f
b
ase
class
if
ier
s
an
d
a
co
n
clu
d
in
g
lay
e
r
o
f
m
eta
-
class
if
ier
s
.
Fu
r
th
er
m
o
r
e,
th
e
r
esear
ch
d
em
o
n
s
tr
ated
t
h
at
alg
o
r
ith
m
ic
ac
cu
r
ac
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is
co
m
p
r
o
m
is
ed
wh
en
co
m
p
ar
in
g
u
n
b
alan
ce
d
an
d
b
a
lan
ce
d
d
atasets
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T
h
e
f
in
d
in
g
s
in
d
icate
th
at
th
e
m
u
ltil
ay
er
s
ta
ck
in
g
class
if
icatio
n
alg
o
r
ith
m
ac
h
iev
es
an
ap
p
r
o
x
i
m
ate
av
er
ag
e
ac
cu
r
a
cy
o
f
9
5
%.
B
au
er
an
d
K
o
h
a
v
i
[
2
3
]
e
m
p
ir
ically
co
n
t
r
asted
Evaluation Warning : The document was created with Spire.PDF for Python.
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2252
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8
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7
6
E
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ta
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h
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ka
r
)
501
th
r
ee
en
s
em
b
le
lear
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in
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s
tr
ateg
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in
clu
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in
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b
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eth
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I
n
th
eir
s
em
in
al
wo
r
k
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J
ian
g
e
t
a
l.
[
2
4
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u
n
v
eiled
SS
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M,
an
i
n
n
o
v
ativ
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m
eth
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d
f
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en
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em
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les.
T
h
e
r
esear
c
h
e
s
[
2
5
]
,
[
2
6
]
e
x
a
m
in
e
th
e
ef
f
icac
y
o
f
en
s
em
b
le
lear
n
in
g
tech
n
iq
u
es
in
th
e
co
n
tex
t
o
f
m
ac
h
in
e
lear
n
in
g
.
B
ased
o
n
th
e
J
4
8
an
d
C
4
.
5
class
if
ier
s
,
Ksh
atr
i
et
a
l.
[
2
7
]
p
r
o
p
o
s
ed
a
m
o
d
if
ied
en
s
e
m
b
le
s
tack
in
g
class
if
icatio
n
al
g
o
r
ith
m
.
T
h
e
ac
c
u
r
ac
y
o
f
th
is
r
ec
en
tly
d
ev
elo
p
ed
alg
o
r
ith
m
is
s
u
p
er
io
r
to
th
at
o
f
th
e
n
o
r
m
alize
d
en
s
em
b
le
s
tack
class
if
ier
.
Xu
an
d
W
an
g
[
2
8
]
ass
er
ted
th
at
th
e
ac
cu
r
ac
y
o
f
th
e
class
if
icatio
n
alg
o
r
ith
m
s
is
s
ig
n
if
ican
tly
i
m
p
ac
ted
b
y
d
ata
p
r
e
p
r
o
ce
s
s
in
g
.
T
h
e
PID
D
s
et
is
u
tili
ze
d
.
T
h
e
p
er
f
o
r
m
an
ce
ca
p
ab
ilit
y
o
f
a
KNN
class
if
ier
i
s
s
h
o
wn
to
b
e
en
h
a
n
ce
d
th
r
o
u
g
h
f
ea
tu
r
e
s
elec
tio
n
an
d
d
ata
n
o
r
m
aliza
tio
n
,
as
d
e
m
o
n
s
tr
ated
b
y
Gu
p
ta
a
n
d
Go
e
l
[
2
9
]
.
On
th
e
F1
-
s
ca
le,
th
e
K
NN
class
if
ier
s
co
r
ed
7
8
.
1
0
%.
I
t
ex
h
i
b
ited
th
e
f
o
ll
o
win
g
m
etr
ics:
ac
cu
r
ac
y
o
f
8
5
.
0
6
%,
r
ec
all
o
f
7
7
.
3
6
%,
p
r
ec
is
io
n
o
f
7
8
.
8
5
%,
s
p
ec
if
icity
o
f
8
9
.
1
1
%,
a
n
d
er
r
o
r
r
ate
o
f
1
4
.
9
4
%.
Z
ian
et
a
l.
[
3
0
]
s
h
o
wca
s
ed
s
ix
teen
ad
d
itio
n
al
clas
s
if
icatio
n
alg
o
r
ith
m
s
,
in
clu
d
i
n
g
L
R
,
NB
,
an
d
XGBo
o
s
t,
im
p
lem
en
ted
as
m
eta
-
class
if
ier
s
with
in
an
en
s
em
b
le
-
b
ased
s
tack
in
g
class
if
icatio
n
m
o
d
el.
T
h
e
s
tu
d
y
co
m
p
ar
ed
th
e
ac
cu
r
ac
y
v
ar
iatio
n
am
o
n
g
m
o
d
els
ac
co
r
d
in
g
to
th
e
m
eta
-
class
if
ier
im
p
lem
en
ted
in
ea
ch
m
o
d
el
.
Ad
d
itio
n
ally
,
a
n
o
v
el
m
eta
-
class
if
ier
is
cr
e
ated
,
ex
h
ib
itin
g
en
h
an
ce
d
ef
f
icac
y
co
m
p
ar
e
d
to
co
n
v
en
tio
n
al
m
eta
-
class
if
ier
s
.
I
n
co
m
p
a
r
is
o
n
to
o
th
e
r
co
n
v
en
tio
n
al
m
eta
-
class
if
ier
s
,
th
e
LR
m
eta
-
class
if
ie
r
p
r
o
d
u
ce
d
th
e
m
o
s
t p
r
ec
is
e
o
u
t
co
m
es,
ac
co
r
d
i
n
g
to
t
h
e
f
i
n
d
i
n
g
s
o
f
th
is
s
tu
d
y
.
3.
RE
S
E
ARCH
M
E
T
H
O
D
T
h
is
s
ec
tio
n
p
r
o
v
id
es
a
d
etailed
ex
p
lan
atio
n
o
f
t
h
e
d
esig
n
an
d
d
ev
el
o
p
m
en
t
s
tep
s
th
at
ar
e
u
s
ed
f
o
r
d
iab
etes
p
r
ed
ictio
n
.
T
h
e
p
r
o
p
o
s
ed
s
tack
ed
class
if
ier
m
o
d
el
is
d
escr
ib
ed
alo
n
g
with
its
b
lo
ck
d
iag
r
am
.
T
h
e
d
etails
o
f
th
e
d
ataset
ar
e
also
d
is
cu
s
s
ed
h
er
ewith
.
T
h
e
p
ar
am
eter
s
f
o
r
th
e
p
er
f
o
r
m
a
n
ce
ass
ess
m
en
t
ar
e
th
en
th
o
r
o
u
g
h
ly
d
is
cu
s
s
ed
.
3
.
1
.
Da
t
a
s
et
c
ha
ra
c
t
er
is
t
ics
T
h
e
PID
D
[
1
1
]
is
u
s
ed
in
th
is
wo
r
k
.
T
a
b
le
1
s
h
o
ws
th
e
h
ea
l
th
p
ar
a
m
eter
s
u
s
ed
as
th
e
m
o
d
el
’
s
in
p
u
t
attr
ib
u
tes.
T
h
e
d
ataset
co
n
tain
s
a
s
am
p
le
s
p
ac
e
o
f
7
6
8
p
atien
ts
.
T
h
e
d
ataset
’
s
tar
g
et
v
ar
iab
le
is
th
e
9
th
at
tr
ib
u
te
f
r
o
m
T
a
b
le
1
,
t
h
e
‘o
u
tco
m
e
’
v
ar
iab
le.
T
h
is
b
in
ar
y
class
v
ar
i
ab
le
d
is
p
lay
s
th
e
r
esu
lt
as
a
0
o
r
1
,
d
ep
en
d
in
g
o
n
wh
eth
er
th
e
p
atien
t
is
d
i
ab
etic
o
r
n
o
n
-
d
iab
etic.
T
h
e
d
ataset
h
as
n
o
n
u
ll
v
alu
es.
T
h
e
d
ataset
p
r
esen
ts
a
b
in
a
r
y
class
if
icatio
n
p
r
o
b
lem
th
at
ca
n
b
e
tack
led
u
s
in
g
class
if
icatio
n
m
eth
o
d
o
lo
g
y
.
T
ab
le
1
.
Data
s
et
attr
ib
u
tes
S
r
N
o
.
A
t
t
r
i
b
u
t
e
s
1
P
r
e
g
n
a
n
c
y
2
G
l
u
c
o
s
e
(
mg
/
d
L)
3
B
l
o
o
d
p
r
e
ss
u
r
e
(
mm
H
g
)
4
S
k
i
n
t
h
i
c
k
n
e
ss (m
m)
5
I
n
su
l
i
n
6
B
M
I
(
b
o
d
y
m
a
ss
i
n
d
e
x
)
7
D
i
a
b
e
t
e
s
p
e
d
i
g
r
e
e
f
u
n
c
t
i
o
n
8
A
g
e
9
O
u
t
c
o
m
e
(
0
o
r
1
)
3
.
2
.
Co
rr
el
a
t
io
n
m
a
t
rix
T
h
e
co
r
r
elatio
n
b
etwe
en
ev
er
y
attr
ib
u
te
in
th
e
d
ataset
is
c
o
m
p
ar
ed
in
Fig
u
r
e
1
.
As
s
h
o
wn
b
y
th
e
g
en
er
ated
p
l
o
t,
th
er
e
is
n
o
s
tr
o
n
g
co
r
r
elatio
n
b
etwe
en
a
n
y
attr
ib
u
te
an
d
th
e
o
b
jectiv
e
v
ar
iab
le.
T
h
e
s
o
le
p
ar
am
eter
,
d
en
o
ted
as
‘
g
lu
co
s
e
’
,
co
r
r
elate
s
with
th
e
‘
o
u
tco
m
e
’
v
ar
ia
b
le
co
n
s
id
er
ed
s
atis
f
ac
to
r
y
.
T
h
e
co
r
r
elatio
n
s
co
r
e
b
etwe
e
n
th
e
‘
g
lu
c
o
s
e
’
an
d
th
e
‘
o
u
tc
o
m
e
’
v
ar
ia
b
les
is
0
.
4
7
.
Oth
er
th
an
th
at,
s
p
ec
if
ic
ch
ar
ac
ter
is
tics
co
r
r
elate
p
o
s
itiv
ely
o
r
n
eg
ativ
ely
with
t
h
e
o
u
t
p
u
t v
ar
iab
le,
b
u
t th
e
co
r
r
elatio
n
is
in
s
ig
n
if
ican
t.
3
.
3
.
Dis
t
ributio
n
o
f
dia
bet
ic
pa
t
ient
s
in t
he
da
t
a
s
et
T
h
e
d
ataset
is
co
n
s
id
er
ab
ly
u
n
ev
en
ly
d
is
tr
ib
u
ted
,
as
s
h
o
wn
in
Fig
u
r
e
2
.
Ap
p
r
o
x
im
ately
5
0
0
class
es
ar
e
lab
eled
as
0
,
r
e
p
r
esen
tin
g
n
eg
ativ
e
o
r
n
o
n
-
d
iab
etic
p
atien
ts
,
wh
ile
2
6
8
class
es
ar
e
la
b
eled
as
1
,
r
ep
r
esen
tin
g
p
o
s
itiv
e
o
r
d
iab
et
ic
p
atien
ts
.
T
o
en
h
an
ce
t
h
e
ac
cu
r
ac
y
o
f
t
h
e
ML
m
o
d
els,
th
is
im
b
alan
ce
d
d
ataset
m
u
s
t b
e
tr
an
s
f
o
r
m
ed
in
to
a
b
al
an
ce
d
o
n
e
[
2
2
]
.
Evaluation Warning : The document was created with Spire.PDF for Python.
I
SS
N
:
2
2
5
2
-
8
7
7
6
I
n
t J I
n
f
&
C
o
m
m
u
n
T
ec
h
n
o
l
,
Vo
l.
13
,
No
.
3
,
Dec
em
b
er
20
24
:
49
9
-
50
8
502
Fig
u
r
e
1
.
Plo
t o
f
th
e
c
o
r
r
elatio
n
m
atr
ix
f
o
r
a
g
iv
en
d
ataset
Fig
u
r
e
2
.
Sp
r
ea
d
o
f
d
iab
etic
p
atien
ts
an
d
n
o
n
-
d
iab
etic
p
atien
ts
3
.
4
.
F
l
o
wcha
rt
T
h
e
m
eth
o
d
o
lo
g
y
im
p
lem
en
t
ed
to
d
e
v
elo
p
th
e
en
s
em
b
le
s
tack
in
g
class
if
ier
m
o
d
el
is
illu
s
tr
ated
th
r
o
u
g
h
a
f
lo
wch
ar
t,
as
s
h
o
wn
in
Fig
u
r
e
3
.
Un
d
e
r
s
tan
d
in
g
th
e
d
ataset,
g
ath
er
i
n
g
a
lis
t
o
f
al
l
its
ch
ar
ac
ter
is
tics
,
an
d
an
aly
zin
g
th
eir
v
a
r
io
u
s
s
tatis
tical
m
ea
s
u
r
es
an
d
attr
ib
u
tes
d
ef
in
ed
th
e
in
itial
s
tep
.
T
h
e
d
ata
im
b
alan
ce
d
ep
icted
in
Fig
u
r
e
2
is
r
ec
tifie
d
d
u
r
in
g
th
e
d
ata
p
r
ep
r
o
ce
s
s
in
g
p
h
ase
to
p
r
o
d
u
ce
a
b
alan
ce
d
d
ataset
co
n
s
is
tin
g
o
f
5
0
0
d
ata
p
o
in
ts
lab
eled
0
an
d
5
0
0
d
ata
p
o
in
ts
lab
eled
1
[
2
2
]
.
Data
n
o
r
m
aliza
tio
n
an
d
s
tan
d
ar
d
izatio
n
p
r
o
ce
s
s
es a
r
e
ex
ec
u
ted
[
2
9
]
,
[
3
0
]
.
Fo
llo
win
g
th
ese
p
r
o
ce
d
u
r
es e
n
s
u
r
es th
at
ev
er
y
o
u
tlier
v
alu
e
in
th
e
d
ataset
is
m
o
d
if
ied
with
its
co
r
r
esp
o
n
d
in
g
n
o
r
m
alize
d
v
alu
es,
th
er
eb
y
p
r
e
v
en
tin
g
an
y
m
o
d
el
f
ailu
r
es
o
r
m
is
class
if
icatio
n
s
.
Du
r
in
g
th
is
s
tag
e,
th
e
d
ataset
is
s
p
lit
in
to
two
p
o
r
tio
n
s
,
with
8
0
% a
llo
ca
ted
f
o
r
tr
ain
in
g
an
d
2
0
%
f
o
r
test
in
g
.
Fo
llo
win
g
th
i
s
,
th
e
d
ataset
is
d
is
p
lay
ed
u
s
in
g
s
tatis
tical
ch
ar
ts
an
d
g
r
ap
h
s
,
co
n
tr
ib
u
tin
g
t
o
th
e
ML
m
o
d
el
’
s
d
ev
elo
p
m
e
n
t.
I
n
o
r
d
er
to
d
e
v
elo
p
th
e
p
r
o
p
o
s
ed
m
o
d
el,
a
liter
atu
r
e
r
ev
iew
is
co
n
d
u
cted
[
1
1
]
–
[
2
0
]
.
T
h
e
s
u
itab
le
ML
alg
o
r
ith
m
s
,
in
clu
d
in
g
KNN,
SVM,
DT
,
an
d
R
F,
wer
e
ch
o
s
en
b
ased
o
n
th
e
f
in
d
i
n
g
s
o
f
th
is
s
tu
d
y
.
Fu
r
th
er
m
o
r
e
,
th
e
en
s
em
b
le
-
b
ased
s
ta
ck
in
g
class
if
icat
io
n
m
o
d
el
[
2
1
]
,
[
2
2
]
is
s
u
g
g
ested
to
en
h
an
ce
th
e
Evaluation Warning : The document was created with Spire.PDF for Python.
I
n
t J I
n
f
&
C
o
m
m
u
n
T
ec
h
n
o
l
I
SS
N:
2252
-
8
7
7
6
E
n
s
emb
le
s
ta
ck
in
g
cla
s
s
ifier
mo
d
el
fo
r
p
r
ed
ictio
n
o
f
d
ia
b
et
es
(
Mr
u
n
a
lin
i B
h
a
n
d
a
r
ka
r
)
503
ac
cu
r
ac
y
o
f
d
ata
p
r
ed
ictio
n
an
d
class
if
icatio
n
.
An
ev
alu
atio
n
is
co
n
d
u
cted
o
n
th
e
p
er
f
o
r
m
an
ce
p
ar
am
eter
s
o
f
ea
ch
alg
o
r
ith
m
im
p
lem
en
te
d
in
d
iv
id
u
ally
to
th
e
d
ataset.
T
h
e
p
r
e
v
io
u
s
ly
m
en
tio
n
ed
alg
o
r
ith
m
s
ar
e
im
p
lem
en
ted
in
t
h
e
s
tack
class
if
ier
,
wh
ich
co
m
p
r
is
es
th
e
b
ase
class
if
ier
lay
er
,
an
d
th
e
L
R
alg
o
r
ith
m
is
th
e
m
eta
class
if
ier
[
3
0
]
.
T
h
ese
d
esig
n
s
tag
es
ar
e
th
en
co
m
p
le
ted
f
o
r
t
h
e
en
s
em
b
le
s
tack
cl
ass
if
icatio
n
m
o
d
el.
Op
tim
al
p
er
f
o
r
m
an
c
e
f
o
r
d
ata
class
if
icatio
n
an
d
p
r
ec
is
e
p
r
ed
ictio
n
s
is
ac
h
iev
ed
th
r
o
u
g
h
iter
ativ
e
m
o
d
if
icatio
n
an
d
e
n
h
an
ce
m
e
n
t
o
f
th
e
d
es
ig
n
ed
m
o
d
el.
T
h
e
s
co
r
es
p
r
o
d
u
ce
d
b
y
s
u
itab
le
p
er
f
o
r
m
a
n
ce
p
ar
a
m
eter
s
ar
e
u
tili
ze
d
to
ass
ess
b
o
th
th
e
en
s
em
b
le
s
tack
class
if
icatio
n
m
o
d
el
an
d
th
e
o
u
tco
m
es
o
f
th
e
s
tan
d
ar
d
alg
o
r
ith
m
s
.
T
h
er
ef
o
r
e,
in
f
er
en
ce
s
ca
n
b
e
m
ad
e
r
eg
ar
d
in
g
th
e
ac
cu
r
ac
y
o
f
p
r
ed
ictio
n
an
d
class
if
ic
atio
n
o
f
th
e
ch
o
s
en
alg
o
r
ith
m
b
ased
o
n
t
h
ese
o
u
tc
o
m
es.
Fig
u
r
e
3
.
Flo
wch
ar
t
3
.
5
.
M
a
chine
lea
rning
a
lg
o
ri
t
hm
s
us
ed
T
h
e
f
o
llo
win
g
s
ec
tio
n
d
is
cu
s
s
es
th
e
th
eo
r
y
u
n
d
er
ly
i
n
g
ea
ch
m
ac
h
in
e
lear
n
in
g
alg
o
r
ith
m
u
tili
ze
d
in
th
e
d
esig
n
an
d
d
ev
elo
p
m
en
t
o
f
th
e
p
r
o
p
o
s
ed
wo
r
k
.
I
t
is
n
ec
ess
ar
y
to
u
n
d
er
s
tan
d
th
e
o
p
er
atio
n
an
d
ap
p
licatio
n
s
o
f
ea
ch
o
f
th
ese
alg
o
r
ith
m
s
to
co
n
d
u
ct
a
n
ex
h
au
s
tiv
e
an
aly
s
is
.
T
h
e
Sci
-
k
it
l
ea
r
n
f
r
am
ew
o
r
k
,
a
n
o
p
en
-
s
o
u
r
ce
lib
r
ar
y
f
o
r
Py
th
o
n
,
is
u
tili
ze
d
to
im
p
lem
en
t
th
e
p
r
o
g
r
am
m
in
g
l
o
g
ic
o
f
ea
c
h
o
f
th
ese
alg
o
r
ith
m
s
.
T
h
e
v
alu
es o
f
attr
ib
u
tes in
th
e
s
e
f
u
n
ctio
n
s
ar
e
m
o
d
if
ied
as
n
ec
ess
ar
y
to
alig
n
with
th
e
m
o
d
el
’
s
s
p
ec
i
f
icatio
n
s
.
3
.
5
.
1
.
K
-
nea
re
s
t
neig
hb
o
rs
T
h
e
KNN
alg
o
r
ith
m
lo
ca
tes
th
e
n
ea
r
est
d
ata
p
o
in
ts
in
th
e
tr
ain
in
g
d
ata
s
et,
also
k
n
o
wn
as
its
n
ea
r
est
n
eig
h
b
o
r
s
,
to
p
r
e
d
ict
a
n
ew
d
ata
p
o
in
t
[
1
0
]
.
T
h
is
d
is
tan
ce
is
co
m
p
u
ted
u
s
in
g
m
etr
ics
lik
e
E
u
clid
ea
n
,
Ma
n
h
attan
,
o
r
Min
k
o
wsk
i
d
is
tan
ce
s
.
B
ased
o
n
th
e
r
esu
lts
f
r
o
m
th
e
d
is
tan
ce
m
etr
ics,
th
e
clo
s
est
n
eig
h
b
o
r
s
ar
e
d
esig
n
ated
b
y
th
e
co
n
s
tan
t
p
o
s
itiv
e
in
teg
er
K.
T
h
e
clas
s
s
et
is
u
s
ed
to
s
elec
t
K
’
s
v
alu
e.
T
h
u
s
,
a
h
ig
h
er
v
alu
e
o
f
K
wo
u
ld
b
e
s
u
itab
le
f
o
r
a
d
ataset
with
m
o
r
e
o
u
tlier
s
o
r
n
o
is
e.
3
.
5
.
2
.
Su
pp
o
rt
v
ec
t
o
r
m
a
chi
ne
A
h
y
p
er
p
lan
e
is
cr
ea
ted
u
s
i
n
g
SVM,
ca
teg
o
r
izin
g
th
e
d
ata
p
o
in
ts
in
to
m
u
ltip
le
g
r
o
u
p
s
.
I
t
ca
n
p
r
o
d
u
ce
a
s
in
g
le
h
y
p
er
p
lan
e
o
r
a
s
tr
in
g
o
f
h
y
p
er
p
lan
es
in
h
ig
h
-
d
im
e
n
s
io
n
al
s
p
ac
e.
R
eg
r
ess
io
n
an
d
class
if
icatio
n
b
o
th
em
p
lo
y
t
h
ese
h
y
p
er
p
lan
es.
SVM
ca
n
ca
teg
o
r
ize
th
e
en
titi
es
an
d
s
ep
ar
ate
th
em
in
to
d
esig
n
ated
class
es.
Evaluation Warning : The document was created with Spire.PDF for Python.
I
SS
N
:
2
2
5
2
-
8
7
7
6
I
n
t J I
n
f
&
C
o
m
m
u
n
T
ec
h
n
o
l
,
Vo
l.
13
,
No
.
3
,
Dec
em
b
er
20
24
:
49
9
-
50
8
504
3
.
5
.
3
.
Dec
is
io
n
t
ree
T
h
is
alg
o
r
ith
m
is
u
s
ed
wh
en
th
e
o
u
tp
u
t
v
ar
iab
le
h
as
a
d
ef
in
ite
n
atu
r
e
[
1
6
]
.
A
m
o
d
el
with
a
tr
ee
-
lik
e
s
tr
u
ctu
r
e
in
v
o
lv
e
d
in
th
e
clas
s
if
icatio
n
p
r
o
ce
s
s
b
ased
o
n
in
p
u
t
f
ea
tu
r
es
is
ca
lled
a
d
ec
is
i
o
n
tr
ee
.
An
y
in
p
u
t
v
ar
iab
le
ty
p
e
m
ay
b
e
u
s
ed
,
in
c
lu
d
in
g
c
o
n
tin
u
o
u
s
,
d
is
cr
ete,
an
d
g
r
ap
h
v
ar
ia
b
les.
3
.
5
.
4
.
Sta
c
k
ed
cla
s
s
if
ier
m
o
del
T
h
e
en
s
em
b
le
s
tack
class
if
icatio
n
m
o
d
el
ca
n
b
e
s
ee
n
as
a
b
l
o
ck
d
iag
r
am
in
Fig
u
r
e
4
.
T
h
e
f
ir
s
t
lay
er
in
v
o
lv
es
a
s
tack
class
if
ier
b
u
ilt
u
s
in
g
KNN,
SVM,
DT
,
an
d
R
F
as
th
e
b
ase
class
if
ier
s
.
T
h
e
in
p
u
t
d
ata
is
f
ed
to
ea
ch
m
eth
o
d
in
d
i
v
id
u
ally
.
T
h
e
co
m
b
in
ed
o
u
tp
u
t
o
f
ea
c
h
b
a
s
e
clas
s
if
ier
is
th
en
f
ed
to
a
m
eta
-
class
if
ier
,
wh
ich
in
teg
r
ates
th
e
p
r
ed
ictio
n
s
o
f
m
u
ltip
le
b
ase
class
if
ier
s
.
Her
e,
th
e
R
F,
DT
,
KNN,
an
d
SV
M
o
u
tp
u
ts
f
r
o
m
th
e
b
ase
class
if
ier
s
ar
e
u
s
ed
as
in
p
u
t
to
th
e
m
eta
-
class
if
ier
,
wh
ic
h
is
th
e
L
R
class
if
ier
.
T
o
p
r
o
d
u
ce
th
e
b
est
o
v
er
all
p
r
ed
ictio
n
,
t
h
e
m
e
ta
-
class
if
ier
m
u
s
t
lear
n
h
o
w
to
b
alan
ce
th
e
p
r
ed
ictio
n
s
o
f
ea
ch
o
f
t
h
e
in
d
iv
id
u
al
b
ase
class
if
ier
s
.
Fig
u
r
e
4
.
B
lo
ck
d
iag
r
am
o
f
th
e
s
tack
ed
class
if
ier
m
o
d
el
3
.
6
.
P
er
f
o
rma
nce
pa
ra
met
er
s
us
ed
f
o
r
ev
a
lua
t
ing
t
he
a
lg
o
rit
hm
s
Mu
ltip
le
p
er
f
o
r
m
an
ce
p
a
r
am
eter
s
ar
e
em
p
lo
y
ed
to
ass
ess
an
d
co
m
p
ar
e
th
e
ML
alg
o
r
ith
m
s
’
o
u
tco
m
es.
T
h
e
o
u
tp
u
t
s
co
r
e
o
f
ea
ch
p
ar
a
m
eter
f
o
r
th
e
r
esp
ec
tiv
e
alg
o
r
ith
m
is
a
n
aly
ze
d
,
an
d
th
e
r
esu
lts
an
d
co
n
clu
s
io
n
s
ar
e
d
r
awn
f
r
o
m
th
ese
v
alu
es.
Par
am
eter
s
li
k
e
ac
cu
r
ac
y
,
r
ec
al
l,
F1
-
s
co
r
e,
an
d
Ma
tth
ew
’
s
co
r
r
elatio
n
co
ef
f
icien
t
(
MCC
)
ar
e
u
s
ed
to
an
aly
ze
th
e
p
er
f
o
r
m
an
ce
o
f
in
d
iv
i
d
u
al
alg
o
r
ith
m
s
u
s
in
g
th
e
s
tack
ed
class
if
ier
m
o
d
el.
4.
RE
SU
L
T
S
AND
D
I
SCU
SS
I
O
N
A
co
m
p
ar
is
o
n
tab
le
is
d
ev
el
o
p
ed
to
e
v
alu
ate
th
e
p
e
r
f
o
r
m
an
ce
o
f
b
o
th
th
e
tr
ai
n
in
g
an
d
test
in
g
d
atasets
.
T
h
is
tab
le
in
clu
d
es
th
e
class
if
icatio
n
p
er
f
o
r
m
a
n
c
e
o
f
ea
ch
alg
o
r
ith
m
.
Ad
d
itio
n
ally
,
b
a
r
p
lo
ts
ar
e
g
en
er
ated
to
s
h
o
wca
s
e
th
e
c
o
m
p
ar
is
o
n
o
f
o
u
tp
u
t
v
al
u
es
o
f
ea
ch
alg
o
r
ith
m
c
o
n
ce
r
n
in
g
d
if
f
er
en
t
p
er
f
o
r
m
an
ce
p
ar
am
eter
s
.
4
.
1
.
T
ra
ini
ng
perf
o
rma
nce
o
f
a
ll a
lg
o
ri
t
hm
s
T
h
e
r
esu
lts
o
f
th
e
class
if
icatio
n
p
r
o
b
lem
s
f
o
r
ea
ch
al
g
o
r
ith
m
ar
e
p
r
esen
ted
in
T
ab
le
2
.
T
h
e
s
tack
ed
class
if
ier
m
o
d
el
ex
h
ib
its
th
e
h
ig
h
est
ac
cu
r
ac
y
r
e
g
ar
d
i
n
g
p
e
r
f
o
r
m
a
n
ce
p
ar
am
eter
s
co
r
es,
f
o
llo
wed
b
y
th
e
RF
alg
o
r
ith
m
.
C
o
n
ce
r
n
s
ar
e
ex
p
r
e
s
s
ed
,
h
o
wev
er
,
r
e
g
ar
d
i
n
g
th
e
p
o
s
s
ib
ilit
y
o
f
o
v
er
f
itti
n
g
.
Evaluation Warning : The document was created with Spire.PDF for Python.
I
n
t J I
n
f
&
C
o
m
m
u
n
T
ec
h
n
o
l
I
SS
N:
2252
-
8
7
7
6
E
n
s
emb
le
s
ta
ck
in
g
cla
s
s
ifier
mo
d
el
fo
r
p
r
ed
ictio
n
o
f
d
ia
b
et
es
(
Mr
u
n
a
lin
i B
h
a
n
d
a
r
ka
r
)
505
4
.
2
.
T
esting
perf
o
rma
nce
o
f
a
ll a
lg
o
rit
hm
s
T
h
e
ef
f
icac
y
o
f
ea
c
h
alg
o
r
ith
m
,
m
ea
s
u
r
e
d
b
y
th
e
p
r
o
v
id
e
d
p
er
f
o
r
m
an
ce
p
a
r
am
eter
s
,
is
d
etailed
in
T
ab
le
3
.
T
h
is
ass
es
s
m
en
t
ex
am
in
es
th
e
alg
o
r
ith
m
s
’
p
r
ed
ict
iv
e
ca
p
ab
ilit
y
.
W
ith
th
e
m
o
s
t
s
ig
n
if
ican
t
av
er
ag
e
p
er
f
o
r
m
an
ce
s
co
r
e
am
o
n
g
all
alg
o
r
ith
m
s
,
th
e
s
tack
ed
class
if
ier
m
o
d
el
r
ec
eiv
es
th
e
h
ig
h
e
s
t
p
o
s
s
ib
le
s
co
r
e
in
ev
er
y
p
er
f
o
r
m
an
ce
p
ar
am
ete
r
.
T
h
e
f
in
d
i
n
g
s
o
f
th
is
s
tu
d
y
m
itig
ate
t
h
e
ass
er
tio
n
s
o
f
o
v
er
f
itti
n
g
an
d
d
em
o
n
s
tr
ate
th
e
r
o
b
u
s
tn
ess
o
f
th
e
m
o
d
el.
T
ab
le
2
.
E
v
alu
atio
n
o
f
tr
ain
in
g
p
er
f
o
r
m
an
ce
o
f
all
alg
o
r
ith
m
s
Tr
a
i
n
i
n
g
d
a
t
a
A
c
c
u
r
a
c
y
M
C
C
F1
-
sc
o
r
e
R
e
c
a
l
l
A
v
e
r
a
g
e
(
%
)
K
NN
8
5
.
5
%
7
3
.
4
5
%
8
6
.
3
3
%
9
2
.
8
8
%
8
4
.
5
4
%
S
V
M
1
0
0
%
1
0
0
%
1
0
0
%
1
0
0
%
1
0
0
%
DT
8
1
.
5
%
6
9
.
7
4
%
8
5
%
9
0
.
0
8
%
8
1
.
5
8
%
RF
9
9
.
1
2
5
%
9
8
.
5
%
9
9
.
2
5
%
9
9
.
2
4
%
9
9
.
0
2
%
S
t
a
c
k
e
d
c
l
a
ssi
f
i
e
r
1
0
0
%
1
0
0
%
1
0
0
%
1
0
0
%
1
0
0
%
T
ab
le
3
.
E
v
alu
atio
n
o
f
test
in
g
p
er
f
o
r
m
an
ce
o
f
all
alg
o
r
it
h
m
s
Te
st
i
n
g
d
a
t
a
A
c
c
u
r
a
c
y
M
C
C
F1
-
sc
o
r
e
R
e
c
a
l
l
A
v
e
r
a
g
e
(
%
)
K
NN
7
1
.
5
%
4
4
.
6
1
%
7
2
.
2
5
%
8
0
.
3
7
%
6
7
.
1
8
%
S
V
M
8
7
%
6
6
.
1
7
%
7
9
.
3
%
6
2
.
6
2
%
7
3
.
7
7
%
DT
7
1
.
5
%
5
5
.
7
%
7
7
.
9
3
%
8
2
.
2
4
%
7
1
.
8
4
%
RF
8
4
.
5
%
7
0
%
8
4
.
9
1
%
8
4
.
1
1
%
8
0
.
8
8
%
S
t
a
c
k
e
d
c
l
a
ssi
f
i
e
r
8
8
.
5
%
7
0
.
5
2
%
8
5
.
5
%
7
9
.
4
4
%
8
0
.
9
9
%
4
.
3
.
Co
m
pa
riso
n o
f
t
ra
ini
ng
perf
o
rm
a
nce
o
f
a
ll a
l
g
o
rit
h
m
s
Fig
u
r
e
5
p
r
esen
ts
a
co
m
p
r
eh
en
s
iv
e
co
m
p
ar
is
o
n
o
f
th
e
tr
ai
n
in
g
p
er
f
o
r
m
a
n
ce
o
f
all
alg
o
r
ith
m
s
f
o
r
ea
ch
ev
alu
atio
n
p
ar
a
m
eter
.
T
h
e
s
tack
ed
class
if
ier
m
o
d
el
p
e
r
f
o
r
m
s
s
im
ilar
ly
to
th
e
b
aselin
e
alg
o
r
ith
m
s
d
u
r
in
g
tr
ain
in
g
.
T
h
e
af
o
r
e
m
en
tio
n
e
d
in
d
icate
s
th
at
th
e
s
tac
k
ed
cl
ass
if
ier
m
o
d
el
is
lear
n
in
g
at
a
s
im
ilar
r
ate
as
th
e
o
th
er
m
o
d
els,
co
r
r
ec
tly
id
en
ti
f
y
in
g
t
h
e
ap
p
r
o
p
r
iate
class
(
r
ec
all)
,
p
r
o
d
u
cin
g
a
s
ig
n
if
ican
t
n
u
m
b
er
o
f
ac
cu
r
ate
p
r
ed
ictio
n
s
(
a
cc
u
r
ac
y
)
,
an
d
d
e
m
o
n
s
tr
atin
g
s
tr
o
n
g
p
er
f
o
r
m
a
n
ce
o
n
b
i
n
ar
y
class
if
icatio
n
s
MCC
.
Ho
wev
er
,
it
is
also
im
p
o
r
tan
t
to
n
o
te
th
at
it
in
co
r
r
ec
tly
class
if
ies
a
s
im
ilar
n
u
m
b
er
o
f
ca
s
es
(
F1
)
as
th
e
o
th
er
m
o
d
els
d
u
r
i
n
g
tr
ai
n
in
g
.
T
h
e
s
tack
ed
class
if
ier
m
o
d
el
’
s
co
m
p
a
r
ab
le
tr
ain
in
g
p
er
f
o
r
m
an
ce
r
aises
co
n
ce
r
n
s
a
b
o
u
t
t
h
e
p
o
ten
tial
f
o
r
o
v
er
f
itti
n
g
.
T
h
is
o
b
s
er
v
atio
n
u
n
d
er
s
co
r
es
th
e
i
m
p
o
r
ta
n
ce
o
f
m
o
d
el
v
alid
atio
n
an
d
th
e
n
ee
d
f
o
r
f
u
r
th
er
i
n
v
esti
g
atio
n
in
to
o
p
tim
izin
g
th
e
s
tack
ed
class
if
ier
m
o
d
el’
s
lear
n
in
g
ef
f
icien
cy
.
Fig
u
r
e
5
.
C
o
m
p
a
r
is
o
n
p
lo
t
o
f
t
r
ain
in
g
p
e
r
f
o
r
m
an
ce
o
f
all
alg
o
r
ith
m
s
4
.
4
.
Co
m
pa
riso
n o
f
t
esting
perf
o
rma
nce
o
f
a
ll
a
lg
o
rit
hm
s
A
co
m
p
r
e
h
en
s
iv
e
c
o
m
p
a
r
is
o
n
o
f
th
e
ef
f
icac
y
o
f
all
alg
o
r
ith
m
s
f
o
r
ea
ch
ev
alu
atio
n
p
ar
am
eter
is
p
r
esen
ted
in
Fig
u
r
e
6
.
T
h
e
s
tack
ed
class
if
ier
m
o
d
el
co
n
s
is
ten
tly
ex
h
ib
ited
s
u
p
er
io
r
p
e
r
f
o
r
m
an
ce
in
e
v
er
y
p
er
f
o
r
m
an
ce
m
etr
ic
co
m
p
a
r
e
d
to
th
e
b
aselin
e
alg
o
r
ith
m
s
.
T
h
is
in
d
icate
s
th
at
th
e
s
tack
ed
class
if
ier
m
o
d
el
Evaluation Warning : The document was created with Spire.PDF for Python.
I
SS
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2
2
5
2
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8
7
7
6
I
n
t J I
n
f
&
C
o
m
m
u
n
T
ec
h
n
o
l
,
Vo
l.
13
,
No
.
3
,
Dec
em
b
er
20
24
:
49
9
-
50
8
506
ex
h
ib
its
s
tr
o
n
g
p
er
f
o
r
m
an
ce
o
n
b
in
ar
y
class
if
icatio
n
s
MCC
,
co
r
r
ec
tly
id
en
tifie
s
th
e
ap
p
r
o
p
r
iate
class
(
r
ec
all)
,
an
d
p
r
o
d
u
ce
s
a
s
ig
n
if
ican
t
n
u
m
b
er
o
f
ac
cu
r
ate
p
r
ed
ictio
n
s
(
a
cc
u
r
ac
y
)
.
Fu
r
th
er
m
o
r
e,
it
in
co
r
r
ec
tl
y
class
if
ies
f
ewe
r
ca
s
es
(
F1
)
,
attesti
n
g
t
o
i
ts
r
o
b
u
s
tn
ess
.
I
m
p
o
r
ta
n
tly
,
t
h
e
co
n
s
is
ten
t
p
e
r
f
o
r
m
an
ce
o
f
th
e
s
tack
ed
class
if
ier
m
o
d
el
in
b
o
th
th
e
tr
ain
in
g
a
n
d
test
in
g
p
h
ases
ef
f
ec
tiv
ely
ad
d
r
ess
es
an
y
co
n
c
er
n
s
r
eg
ar
d
in
g
o
v
er
f
itti
n
g
.
T
h
is
co
n
s
is
ten
cy
en
s
u
r
es
th
at
th
e
m
o
d
el
is
n
o
t
m
er
ely
m
e
m
o
r
izin
g
th
e
tr
ain
in
g
d
ata
b
u
t
ca
n
g
en
er
alize
to
u
n
s
ee
n
d
ata,
th
e
r
eb
y
p
r
o
v
id
in
g
r
eliab
le
an
d
r
o
b
u
s
t p
r
ed
ictio
n
s
.
Fig
u
r
e
6
.
C
o
m
p
a
r
is
o
n
p
lo
t
o
f
t
esti
n
g
p
er
f
o
r
m
an
ce
o
f
all
alg
o
r
ith
m
s
5.
CO
NCLU
SI
O
N
AND
F
U
T
U
RE
SCO
P
E
T
h
is
wo
r
k
ev
alu
ated
h
o
w
m
ac
h
in
e
lear
n
in
g
alg
o
r
ith
m
s
ca
n
p
r
ed
ict
d
iab
etes a
n
d
attem
p
ted
t
o
d
ev
elo
p
a
m
o
d
el
th
at
ca
n
p
r
e
d
ict
d
iab
etes
in
a
p
atien
t
with
ac
cu
r
ac
y
an
d
p
r
ec
is
io
n
.
T
h
e
d
ev
el
o
p
ed
s
tack
ed
class
if
ier
m
o
d
el,
a
m
ix
o
f
m
eth
o
d
s
s
u
ch
as
SVM,
DT
,
KNN,
an
d
R
F
u
s
in
g
en
s
em
b
le
m
eth
o
d
o
lo
g
y
,
s
h
o
ws
p
r
o
m
is
in
g
r
esu
lts
.
Fo
r
th
e
test
in
g
d
ata,
i.
e.
,
f
o
r
d
ia
b
etes
p
r
ed
ictio
n
,
t
h
e
en
s
em
b
le
s
tack
in
g
class
if
ier
m
o
d
el
s
h
o
we
d
th
e
h
ig
h
est
ac
cu
r
ac
y
o
f
8
8
.
5
%,
f
o
llo
wed
clo
s
ely
b
y
th
e
SVM
at
8
7
%.
T
h
e
o
v
e
r
all
av
e
r
ag
e
p
e
r
f
o
r
m
an
ce
o
f
all
th
e
ev
alu
atio
n
p
ar
am
eter
s
f
o
r
t
h
e
d
ev
elo
p
e
d
s
tack
ed
class
if
ier
m
o
d
el
is
also
b
etter
th
an
th
e
i
n
d
iv
id
u
al
alg
o
r
ith
m
’
s
av
er
ag
e
s
co
r
e.
T
h
e
av
er
a
g
e
te
s
tin
g
p
er
f
o
r
m
an
ce
p
ar
a
m
eter
s
co
r
e
is
ab
o
u
t
8
1
%,
wh
ich
s
ig
n
if
ies
th
at
th
e
m
o
d
el
m
ak
es
b
e
tter
p
r
ed
ictio
n
s
,
b
ett
er
class
if
icatio
n
s
,
an
d
s
u
b
s
tan
tially
b
etter
co
v
er
ag
e
o
f
th
e
d
ataset
th
an
all
o
th
er
b
aselin
e
class
if
icatio
n
alg
o
r
ith
m
s
.
T
h
e
KNN
an
d
DT
alg
o
r
ith
m
s
b
o
th
s
h
o
wed
t
h
e
lo
west a
cc
u
r
ac
ies o
f
7
1
.
5
%.
T
h
ese
f
in
d
in
g
s
im
p
ly
th
at
th
e
p
r
ed
ictio
n
ac
cu
r
ac
y
o
f
in
d
i
v
id
u
al
class
if
ier
alg
o
r
ith
m
s
is
en
h
an
ce
d
wh
en
co
m
b
in
ed
,
as
s
h
o
w
n
in
th
e
en
s
em
b
le
s
tack
in
g
class
i
f
ier
m
o
d
el.
T
h
is
in
d
icate
s
th
a
t
m
ac
h
in
e
lear
n
i
n
g
alg
o
r
ith
m
s
ca
n
b
e
u
s
ed
as
p
r
ac
tical
to
o
ls
f
o
r
f
o
r
ec
asti
n
g
d
iab
etes
an
d
h
elp
in
th
e
tim
ely
d
iag
n
o
s
is
an
d
p
r
ed
ictio
n
o
f
d
ia
b
etes in
a
p
ati
en
t.
Ma
ch
in
e
lear
n
in
g
alg
o
r
ith
m
s
ca
n
an
aly
ze
v
ast d
atasets
an
d
u
n
co
v
er
p
atter
n
s
h
u
m
an
s
m
ig
h
t
o
v
er
lo
o
k
.
T
h
ese
m
o
d
els
ca
n
b
ec
o
m
e
m
o
r
e
ac
cu
r
ate
an
d
v
alu
a
b
le
if
m
ed
ical
r
ec
o
r
d
s
a
n
d
o
th
er
h
ea
lth
d
ata
ar
e
d
ec
en
tr
alize
d
.
An
o
th
e
r
p
r
o
m
is
in
g
ar
ea
f
o
r
r
esear
ch
is
u
s
in
g
th
e
d
ata
co
llected
f
r
o
m
wea
r
a
b
le
tech
n
o
lo
g
ies
o
r
s
en
s
o
r
s
in
d
iab
etes
p
r
ed
ictio
n
m
o
d
els
f
o
r
r
ea
l
-
tim
e
d
etec
tio
n
.
Ma
ch
in
e
lear
n
in
g
alg
o
r
ith
m
s
ca
n
d
eliv
er
m
o
r
e
ac
cu
r
ate
an
d
f
ast
p
r
e
d
ictio
n
s
o
f
d
iab
etic
r
is
k
b
y
g
ath
er
i
n
g
r
e
al
-
tim
e
d
ata
o
n
p
a
r
am
eter
s
s
u
ch
as
b
lo
o
d
g
lu
co
s
e
lev
els,
p
h
y
s
ical
ac
tiv
ity
,
an
d
s
leep
h
ab
its
.
Fu
r
th
er
m
o
r
e
,
d
iab
etes
p
r
ed
ict
io
n
m
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ti
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rti
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lt
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re
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a
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larly
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ictin
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a
n
d
m
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n
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ro
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ise
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se
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u
n
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lev
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tec
h
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r
so
c
ieta
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e
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fit
.
H
e
c
a
n
b
e
c
o
n
tac
ted
a
t
e
m
a
il
:
b
e
ll
a
r
y
y
a
sh
2
3
@g
m
a
il
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c
o
m
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j
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ira
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o
le
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s
b
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m
b
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r
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.
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h
a
il
s
fro
m
Na
v
i
M
u
m
b
a
i,
M
a
h
a
ra
sh
tra
sta
te,
In
d
ia.
He
re
c
e
iv
e
d
h
is
Ba
c
h
e
lo
r
o
f
Tec
h
n
o
lo
g
y
d
e
g
re
e
in
El
e
c
tro
n
ics
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n
d
Tele
c
o
m
m
u
n
ic
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ti
o
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n
g
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ri
Ch
in
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h
wa
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l
leg
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o
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En
g
i
n
e
e
rin
g
,
P
u
n
e
,
I
n
d
ia,
in
2
0
2
4
.
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i
n
tern
e
d
in
C
u
sto
m
e
r
Re
latio
n
sh
i
p
M
a
n
a
g
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m
e
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t
(CRM
)
a
n
d
lev
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ra
g
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d
it
to
id
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n
ti
fy
sa
les
o
p
p
o
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tu
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it
ies
,
re
c
u
rri
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g
p
ro
b
lem
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a
n
d
se
rv
ice
issu
e
s.
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p
u
b
li
sh
e
d
a
p
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p
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h
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rg
in
g
c
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r
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tt
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ry
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m
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l
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icle
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lso
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k
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b
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se
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g
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tri
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ted
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ries
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tr
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v
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d
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h
a
n
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se
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rit
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tec
ti
n
g
we
a
p
o
n
s o
r
il
l
icit
su
b
sta
n
c
e
s
in
m
o
n
it
o
re
d
e
n
v
ir
o
n
m
e
n
ts.
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is
c
u
rre
n
tl
y
p
u
rsu
i
n
g
h
is
ma
ste
r’s
in
c
o
m
p
u
ter
sc
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e
,
f
o
c
u
si
n
g
o
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re
a
s
o
f
m
a
c
h
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lea
rn
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n
g
,
d
a
ta
a
n
a
ly
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n
d
d
a
ta
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ien
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e
to
d
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ti
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ti
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s
t
h
a
t
imp
r
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v
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u
sin
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ss
o
p
e
ra
ti
o
n
s.
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a
ims
t
o
u
ti
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z
e
h
is
sk
il
ls
t
o
c
re
a
te
c
o
st
-
e
ff
e
c
ti
v
e
,
h
ig
h
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p
e
rfo
rm
a
n
c
e
a
p
p
li
c
a
ti
o
n
s.
He
c
a
n
b
e
c
o
n
tac
ted
a
t
e
m
a
il
:
a
n
u
j.
b
h
o
le@
g
m
a
il
.
c
o
m
.
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ish
e
k
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s
a
h
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b
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a
d
a
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g
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wa
s
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rn
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n
F
e
b
ru
a
r
y
1
9
,
2
0
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,
i
n
S
o
lap
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r
,
M
a
h
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ra
sh
tra,
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d
ia.
I
n
2
0
2
4
,
h
e
g
ra
d
u
a
ted
fro
m
P
imp
r
i
Ch
i
n
c
h
wa
d
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ll
e
g
e
o
f
E
n
g
i
n
e
e
rin
g
i
n
P
u
n
e
,
In
d
ia,
with
a
Ba
c
h
e
l
o
r
o
f
Tec
h
n
o
l
o
g
y
in
El
e
c
tro
n
ics
a
n
d
Tele
c
o
m
m
u
n
ica
ti
o
n
s
En
g
i
n
e
e
rin
g
.
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wa
s
p
a
rt
o
f
a
su
m
m
e
r
in
ter
n
sh
i
p
p
ro
g
ra
m
o
n
m
a
c
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in
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lea
rn
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n
g
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n
d
lev
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ra
g
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d
it
to
lea
rn
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b
o
u
t
v
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rio
u
s M
L
a
lg
o
rit
h
m
s,
a
lo
n
g
with
t
h
e
ir
p
ra
c
ti
c
a
l
imp
lem
e
n
tatio
n
s
a
n
d
a
p
p
l
ica
ti
o
n
s.
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h
a
s
p
u
b
li
s
h
e
d
a
p
a
p
e
r
o
n
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si
n
g
wi
n
d
e
n
e
rg
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to
re
c
h
a
rg
e
li
t
h
iu
m
-
i
o
n
b
a
tt
e
ries
in
e
lec
tri
c
v
e
h
icle
s.
He
h
a
s
a
lso
c
o
n
tri
b
u
ted
t
o
th
e
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o
n
t
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t
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a
se
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ima
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re
tri
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d
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c
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d
a
u
to
m
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ted
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ste
m
th
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t
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se
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tex
t
q
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e
ries
to
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tri
e
v
e
ima
g
e
s
fro
m
v
id
e
o
fra
m
e
s
to
imp
ro
v
e
se
c
u
rit
y
b
y
d
e
te
c
ti
n
g
we
a
p
o
n
s
o
r
il
leg
a
l
p
ro
d
u
c
ts
in
m
o
n
it
o
re
d
e
n
v
iro
n
m
e
n
ts
.
He
c
a
n
b
e
c
o
n
tac
ted
a
t
e
m
a
il
:
a
b
h
ish
e
k
b
h
a
d
a
n
g
e
4
5
@g
m
a
il
.
c
o
m
.
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