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To
a
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ti
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g
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ip
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p
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d
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c
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p
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m
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I
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3
m
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will
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3
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As
a
m
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m
s
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Evaluation Warning : The document was created with Spire.PDF for Python.
I
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N:
2088
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B
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(
To
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3929
m
u
tatio
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f
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ch
ar
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ter
is
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f
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tu
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m
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[
4
]
.
T
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ar
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m
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s
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5
]
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m
R
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ex
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ata
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tu
n
ities
to
an
aly
ze
c
o
m
p
le
x
b
io
lo
g
ical
p
atter
n
s
r
elate
d
t
o
b
r
ea
s
t
ca
n
ce
r
.
Ho
wev
er
,
t
h
e
h
ig
h
d
im
en
s
io
n
ality
o
f
g
en
etic
d
ata
p
o
s
es
s
u
b
s
tan
tial
ch
allen
g
es
in
a
n
aly
s
is
,
en
co
m
p
ass
in
g
ch
alle
n
g
es
s
u
ch
as
th
e
r
is
k
o
f
o
v
er
f
itti
n
g
,
ele
v
ated
co
m
p
u
tatio
n
al
d
e
m
an
d
s
,
an
d
co
m
p
lex
ities
in
in
ter
p
r
etin
g
t
h
e
r
esu
ltin
g
o
u
tp
u
ts
.
On
e
s
u
c
h
tech
n
iq
u
e
ca
p
a
b
le
o
f
h
an
d
lin
g
th
is
co
m
p
lex
ity
is
th
e
m
ac
h
in
e
lear
n
in
g
a
p
p
r
o
ac
h
[
6
]
.
Diag
n
o
s
in
g
b
r
ea
s
t
ca
n
ce
r
u
s
in
g
m
R
NA
d
ata
an
d
m
ac
h
in
e
lear
n
in
g
h
as
b
ec
o
m
e
a
k
ey
r
ese
ar
ch
f
o
cu
s
d
u
e
to
its
p
o
ten
tial f
o
r
ea
r
l
y
d
etec
tio
n
an
d
p
er
s
o
n
a
lized
tr
ea
t
m
en
t.
E
ar
ly
an
d
ac
cu
r
ate
d
ete
ctio
n
is
ess
en
tial f
o
r
im
p
r
o
v
in
g
s
u
r
v
iv
al
r
ates.
T
h
i
s
o
v
er
v
iew
ex
p
lo
r
es
v
ar
io
u
s
m
ac
h
in
e
lear
n
in
g
m
eth
o
d
s
ap
p
lied
to
m
R
NA
d
at
a
f
o
r
b
r
ea
s
t
ca
n
ce
r
d
etec
tio
n
an
d
d
iag
n
o
s
is
.
Ma
ch
in
e
lear
n
in
g
(
ML
)
tech
n
iq
u
es
h
av
e
b
ee
n
i
n
cr
ea
s
in
g
ly
u
tili
ze
d
to
o
p
tim
ize
th
e
ac
cu
r
ac
y
an
d
e
f
f
icien
cy
o
f
b
r
ea
s
t
ca
n
ce
r
d
ia
g
n
o
s
is
b
y
lev
er
ag
in
g
b
o
th
im
ag
in
g
m
o
d
alities
an
d
ad
v
an
ce
d
d
ata
an
aly
s
is
.
T
h
ese
m
eth
o
d
s
s
ig
n
if
ican
tly
im
p
r
o
v
e
th
e
id
en
tific
atio
n
o
f
d
ia
g
n
o
s
tic
an
d
p
r
o
g
n
o
s
tic
b
io
m
ar
k
e
r
s
an
d
en
ab
le
e
f
f
ec
tiv
e
class
if
icatio
n
o
f
b
r
ea
s
t
ca
n
ce
r
s
u
b
ty
p
es
[
7
]
.
T
h
e
f
ield
o
f
ML
h
as
ad
v
an
ce
d
co
n
s
id
er
ab
ly
with
th
e
d
ev
el
o
p
m
en
t
o
f
b
o
th
c
o
n
v
e
n
tio
n
al
a
n
d
h
y
b
r
id
ap
p
r
o
ac
h
es.
C
o
n
v
en
t
io
n
al
ML
m
eth
o
d
s
ty
p
ically
r
ely
o
n
s
in
g
le
alg
o
r
ith
m
s
,
wh
ile
h
y
b
r
id
m
ac
h
in
e
lear
n
in
g
s
y
s
tem
(
HM
L
S)
co
m
b
in
es
m
u
ltip
le
tech
n
iq
u
es to
lev
e
r
ag
e
th
eir
s
tr
en
g
th
s
an
d
m
itig
ate
th
e
wea
k
n
ess
es o
f
in
d
iv
id
u
al
alg
o
r
ith
m
s
[
8
]
.
Hy
b
r
id
m
ac
h
in
e
lear
n
in
g
s
y
s
tem
(
HM
L
S)
p
r
o
v
i
d
es
s
u
b
s
tan
tial
b
en
ef
its
b
y
co
m
b
in
in
g
th
e
s
tr
en
g
th
s
o
f
d
if
f
er
e
n
t
tech
n
iq
u
es.
T
h
ey
en
h
an
ce
p
e
r
f
o
r
m
an
ce
,
ac
cu
r
a
cy
,
an
d
r
o
b
u
s
tn
ess
,
m
ak
in
g
t
h
em
well
-
s
u
ited
f
o
r
tack
lin
g
co
m
p
le
x
p
r
o
b
lem
s
.
T
h
ese
m
eth
o
d
s
ar
e
p
ar
ticu
la
r
ly
ef
f
ec
tiv
e
in
ap
p
licatio
n
s
th
at
r
eq
u
ir
e
b
o
th
d
ata
-
d
r
iv
e
n
in
s
ig
h
ts
a
n
d
d
o
m
ain
k
n
o
wled
g
e.
B
y
i
n
teg
r
at
in
g
v
a
r
io
u
s
tec
h
n
iq
u
es,
h
y
b
r
i
d
m
o
d
els
n
o
t
o
n
ly
im
p
r
o
v
e
ac
c
u
r
ac
y
b
u
t
also
ex
p
an
d
th
e
a
p
p
licab
ilit
y
o
f
m
ac
h
in
e
lear
n
in
g
ac
r
o
s
s
d
iv
er
s
e
f
ield
s
.
T
h
ey
d
em
o
n
s
tr
ate
s
u
p
e
r
io
r
p
er
f
o
r
m
an
ce
in
co
m
p
le
x
task
s
,
in
clu
d
in
g
class
if
icatio
n
,
r
e
g
r
ess
io
n
,
an
d
r
ei
n
f
o
r
ce
m
en
t
lear
n
in
g
.
I
n
h
ea
lth
ca
r
e,
h
y
b
r
i
d
s
y
s
tem
s
th
at
co
m
b
in
e
p
h
y
s
ician
r
ea
s
o
n
in
g
with
ML
alg
o
r
ith
m
s
o
u
tp
e
r
f
o
r
m
tr
ad
itio
n
al
m
o
d
els b
y
lev
er
a
g
in
g
h
ig
h
-
q
u
ality
d
ata
an
d
ex
p
e
r
t
k
n
o
wled
g
e
[
9
]
.
A
d
d
itio
n
ally
,
h
y
b
r
i
d
ap
p
r
o
ac
h
es
o
p
tim
ize
f
ea
tu
r
e
s
elec
tio
n
,
e
n
h
an
ce
g
en
e
r
aliza
tio
n
,
an
d
o
f
f
er
s
ig
n
if
ican
t
ad
v
a
n
tag
es
o
v
e
r
c
o
n
v
e
n
tio
n
al
m
eth
o
d
s
[
1
0
]
.
I
n
th
is
s
tu
d
y
,
we
u
s
ed
th
r
ee
co
m
p
o
n
e
n
ts
o
f
th
e
HM
L
S: i)
Ma
ch
in
e
lear
n
in
g
class
if
i
ca
tio
n
alg
o
r
ith
m
s
,
s
elec
ted
f
o
r
th
eir
a
b
ilit
y
to
h
a
n
d
le
co
m
p
lex
an
d
in
tr
icate
d
a
ta.
T
h
ese
alg
o
r
ith
m
s
in
cl
u
d
e
r
an
d
o
m
f
o
r
est
(
R
F)
[
1
1
]
,
n
ai
v
e
b
ay
es
(
NB
)
[
1
2
]
,
k
-
n
ea
r
est
n
eig
h
b
o
r
s
(
KNN)
[
1
3
]
,
ex
tr
a
tr
ee
s
class
if
ier
(
E
T
C
)
[
1
4
]
,
an
d
lo
g
is
tic
r
eg
r
ess
io
n
(
L
R
)
[
1
5
]
.
ii)
Fea
tu
r
e
s
elec
tio
n
al
g
o
r
ith
m
s
,
d
e
s
ig
n
ed
to
id
en
tif
y
th
e
b
est
f
ea
tu
r
es
f
r
o
m
lar
g
e
d
atasets
.
T
h
ese
in
clu
d
e
an
aly
s
is
o
f
v
ar
ian
ce
(
ANOV
A)
[
1
6
]
,
m
u
tu
al
in
f
o
r
m
atio
n
(
MI
)
[
1
7
]
,
E
T
C
,
an
d
L
R
[
1
8
]
.
iii)
U
s
in
g
th
e
p
r
in
cip
al
co
m
p
o
n
e
n
t
an
aly
s
is
(
PC
A)
alg
o
r
ith
m
,
wh
ich
en
h
a
n
ce
s
n
o
n
lin
ea
r
d
y
n
am
ic
p
r
o
ce
s
s
m
o
n
ito
r
i
n
g
b
y
ex
tr
ac
ti
n
g
d
y
n
am
ic,
lin
ea
r
,
an
d
n
o
n
lin
ea
r
f
ea
tu
r
es f
r
o
m
p
r
o
ce
s
s
d
ata
[
1
9
]
.
T
h
is
r
esear
ch
e
m
p
lo
y
s
m
R
NA
g
en
e
ex
p
r
ess
io
n
d
ata
to
class
i
f
y
b
r
ea
s
t
ca
n
ce
r
an
d
aim
s
t
o
id
en
tify
th
e
m
o
s
t
o
p
tim
al
co
m
b
in
atio
n
o
f
HM
L
S
b
y
an
aly
zin
g
an
d
co
m
p
ar
in
g
th
e
r
esu
lts
o
f
ea
ch
ex
p
er
im
en
t
co
n
d
u
cted
.
T
h
e
HM
L
S
m
o
d
el,
d
ev
elo
p
ed
u
s
i
n
g
th
e
Py
th
o
n
p
r
o
g
r
am
m
i
n
g
lan
g
u
a
g
e,
p
r
esen
ts
a
co
m
p
r
eh
en
s
iv
e
ap
p
r
o
ac
h
th
at
co
m
b
in
es
f
ea
tu
r
e
s
elec
tio
n
,
f
ea
tu
r
e
e
x
tr
ac
tio
n
,
an
d
class
if
icatio
n
tech
n
iq
u
es,
a
d
d
r
ess
in
g
th
e
lim
itatio
n
s
o
f
tr
ad
itio
n
al
s
in
g
le
-
m
eth
o
d
p
ip
e
lin
es.
T
h
e
p
r
im
ar
y
o
b
jectiv
es o
f
th
is
s
tu
d
y
ar
e
as
f
o
llo
ws:
i)
to
d
ev
elo
p
a
r
o
b
u
s
t
an
d
p
r
e
cise
HM
L
S
m
o
d
el
f
o
r
id
en
tify
in
g
b
r
ea
s
t
ca
n
ce
r
u
s
in
g
m
R
NA
g
en
e
ex
p
r
ess
io
n
d
at
a,
ii)
to
co
m
p
ar
e
t
h
e
p
r
o
p
o
s
ed
HM
L
S
m
o
d
el
with
p
r
ev
io
u
s
m
o
d
els
f
r
o
m
p
ast
r
esear
ch
,
an
d
iii)
t
o
g
ain
n
e
w
in
s
ig
h
ts
in
to
th
e
im
p
lem
en
ta
tio
n
o
f
HM
L
S
f
o
r
b
r
ea
s
t
ca
n
ce
r
id
en
tific
atio
n
.
B
y
em
p
lo
y
in
g
a
co
m
p
ar
ativ
e
a
n
d
e
n
s
em
b
le
-
b
ased
f
ea
tu
r
e
s
elec
tio
n
ap
p
r
o
ac
h
,
th
e
s
tu
d
y
id
en
tifie
s
th
e
m
o
s
t
ef
f
ec
tiv
e
s
tr
ateg
ies
th
at
im
p
r
o
v
e
b
o
th
th
e
ac
cu
r
ac
y
an
d
r
eliab
ilit
y
o
f
class
if
icatio
n
,
c
o
n
tr
ib
u
tin
g
to
m
o
r
e
r
o
b
u
s
t
an
d
d
e
p
en
d
a
b
le
o
u
tco
m
e
s
.
Ad
d
itio
n
ally
,
th
e
in
n
o
v
ativ
e
u
s
e
o
f
th
e
ex
tr
a
t
r
ee
s
alg
o
r
ith
m
,
b
o
t
h
as
a
class
if
ier
an
d
as
a
f
ea
tu
r
e
s
elec
to
r
,
o
f
f
e
r
s
a
n
o
v
el
p
er
s
p
ec
tiv
e
o
n
alg
o
r
ith
m
v
er
s
atility
an
d
its
im
p
ac
t
o
n
m
o
d
el
p
er
f
o
r
m
an
ce
.
Fin
ally
,
th
e
i
n
teg
r
at
io
n
o
f
PC
A
with
v
ar
io
u
s
class
if
icatio
n
alg
o
r
ith
m
s
p
r
o
v
id
es
a
co
m
p
r
e
h
e
n
s
iv
e
ev
alu
atio
n
f
r
a
m
ewo
r
k
t
h
at
co
u
ld
s
er
v
e
as
a
b
en
ch
m
ar
k
f
o
r
f
u
tu
r
e
s
tu
d
i
es
in
v
o
lv
in
g
h
ig
h
ly
c
o
m
p
le
x
an
d
h
ig
h
-
d
im
e
n
s
io
n
al
b
i
o
m
ed
ical
d
atasets
.
C
o
llectiv
ely
,
th
e
f
in
d
in
g
s
c
o
n
tr
ib
u
te
to
t
h
e
a
d
v
an
ce
m
e
n
t
o
f
m
o
r
e
e
f
f
ec
tiv
e
s
tr
ateg
ies,
ef
f
icien
t,
an
d
in
ter
p
r
etab
le
m
ac
h
in
e
lear
n
i
n
g
m
o
d
els with
in
th
e
d
o
m
ain
o
f
ca
n
ce
r
g
en
o
m
ics.
Evaluation Warning : The document was created with Spire.PDF for Python.
I
SS
N
:
2
0
8
8
-
8
7
0
8
I
n
t J E
lec
&
C
o
m
p
E
n
g
,
Vo
l.
15
,
No
.
4
,
Au
g
u
s
t
20
25
:
3
9
2
8
-
3937
3930
2.
L
I
T
E
R
AT
U
RE
R
E
VI
E
W
T
h
e
s
tu
d
y
b
y
C
h
en
et
a
l.
[
2
0
]
,
d
atasets
o
b
tain
ed
f
r
o
m
Mo
lecu
lar
T
a
x
o
n
o
m
y
o
f
B
r
ea
s
t
C
an
ce
r
I
n
ter
n
atio
n
al
C
o
n
s
o
r
tiu
m
(
ME
T
AB
R
I
C
)
,
T
h
e
C
an
ce
r
Gen
o
m
e
Atlas
(
T
C
GA)
,
an
d
Gen
e
E
x
p
r
ess
io
n
Om
n
i
b
u
s
(
GE
O)
wer
e
u
s
ed
to
p
r
e
d
ict
b
r
ea
s
t
ca
n
ce
r
ac
co
r
d
in
g
to
i
m
m
u
n
e
s
u
b
ty
p
es
i
n
tr
ip
le
-
n
e
g
ativ
e
b
r
ea
s
t
ca
n
ce
r
(
T
NB
C
)
p
atien
ts
,
id
en
tify
in
g
1
1
h
u
b
g
e
n
es.
T
h
e
R
F
m
o
d
el
d
ev
elo
p
e
d
in
th
eir
s
tu
d
y
y
ield
ed
an
AUC
o
f
0
.
7
6
,
a
p
er
f
o
r
m
an
ce
co
n
s
id
er
ed
s
atis
f
ac
to
r
y
th
o
u
g
h
n
o
t
r
em
ar
k
a
b
le,
in
d
icatin
g
th
at
th
e
m
o
d
el
s
till
h
as
r
o
o
m
f
o
r
im
p
r
o
v
em
e
n
t.
Su
b
s
eq
u
en
t
r
es
ea
r
ch
b
y
E
l
-
Nab
awy
et
a
l.
[
2
1
]
ap
p
lied
s
u
p
er
v
is
ed
lear
n
in
g
to
th
e
ME
T
AB
R
I
C
d
ataset,
ac
h
iev
in
g
t
h
e
h
ig
h
est
ac
cu
r
ac
y
o
f
9
7
.
1
%
u
s
in
g
lin
ea
r
-
SVM
an
d
E
-
SVM
alg
o
r
it
h
m
s
.
Ho
wev
er
,
th
e
m
o
d
el
s
till
h
ea
v
ily
r
elies o
n
m
an
u
al
f
ea
tu
r
e
p
r
e
-
s
elec
tio
n
r
ath
er
th
an
u
tili
zin
g
m
o
r
e
s
ca
lab
l
e
au
to
m
atic
f
ea
tu
r
e
lear
n
in
g
tech
n
iq
u
es.
Fu
r
th
er
s
tu
d
ies
ar
e
n
ee
d
ed
to
ex
p
lain
t
h
e
k
e
y
f
ea
t
u
r
es
d
r
iv
in
g
th
e
p
r
ed
ictio
n
s
.
A
s
tu
d
y
co
n
d
u
cte
d
b
y
Z
h
a
o
et
a
l.
[
2
2
]
u
s
ed
ME
T
AB
R
I
C
d
ata
w
ith
th
e
K
-
Me
an
s
m
eth
o
d
an
d
m
ac
h
in
e
lear
n
in
g
class
if
ier
s
,
s
h
o
win
g
th
at
R
an
d
o
m
Fo
r
est
an
d
SVM
ac
h
iev
e
d
an
ac
cu
r
ac
y
o
f
7
2
.
9
%.
Ho
we
v
er
,
th
e
s
tu
d
y
d
id
n
o
t
ex
p
lain
h
o
w
m
is
s
in
g
d
ata
in
g
en
e
e
x
p
r
ess
io
n
an
d
clin
ica
l
v
ar
iab
les
wer
e
h
an
d
led
,
wh
i
ch
co
u
ld
af
f
ec
t
th
e
ac
cu
r
ac
y
a
n
d
in
tr
o
d
u
ce
b
ias.
Mo
r
e
ad
v
a
n
ce
d
d
im
en
s
io
n
alit
y
r
ed
u
ctio
n
tech
n
iq
u
es,
s
u
ch
a
s
s
u
p
er
v
is
ed
PC
A
o
r
au
to
en
co
d
er
s
,
co
u
l
d
b
e
in
te
g
r
ated
to
im
p
r
o
v
e
th
e
m
o
d
el’
s
a
b
ilit
y
to
p
r
eser
v
e
ess
en
tial
b
io
lo
g
ical
in
f
o
r
m
atio
n
,
o
f
f
er
in
g
ad
v
a
n
tag
es o
v
e
r
th
e
c
o
n
v
en
tio
n
al
K
-
Me
an
s
ap
p
r
o
ac
h
.
Pre
v
io
u
s
r
esear
ch
h
as
ex
p
lo
r
e
d
h
y
b
r
id
m
ac
h
in
e
lear
n
in
g
s
y
s
tem
s
.
Fo
r
ex
am
p
le,
Al
-
R
ajab
et
a
l.
[
2
3
]
p
r
o
p
o
s
ed
a
n
ew
h
y
b
r
id
m
ac
h
in
e
lear
n
in
g
f
ea
tu
r
e
s
elec
tio
n
m
o
d
el
to
im
p
r
o
v
e
g
en
e
class
if
icatio
n
ac
r
o
s
s
m
u
ltip
le
co
lo
n
ca
n
ce
r
d
atase
ts
.
T
h
e
s
tu
d
y
ad
d
r
ess
ed
th
e
ch
allen
g
es
o
f
h
ig
h
-
d
im
e
n
s
io
n
al
an
d
n
o
is
y
g
e
n
e
ex
p
r
ess
io
n
d
ata.
T
h
e
m
o
d
el
co
m
b
in
es
i
n
f
o
r
m
atio
n
g
ain
(
I
G)
a
n
d
g
en
etic
alg
o
r
ith
m
(
GA
)
f
o
r
f
ea
t
u
r
e
ex
tr
ac
tio
n
,
an
d
m
R
MR
with
p
ar
ticle
s
war
m
o
p
tim
izatio
n
(
PS
O)
f
o
r
g
en
e
s
elec
tio
n
.
H
ML
FS
M
im
p
r
o
v
ed
class
if
icatio
n
ac
cu
r
ac
y
b
y
id
e
n
tify
in
g
k
ey
g
e
n
es a
n
d
r
em
o
v
i
n
g
ir
r
elev
a
n
t o
n
es,
ac
h
ie
v
in
g
u
p
to
9
7
% a
cc
u
r
ac
y
.
Ho
wev
er
,
th
e
s
tu
d
y
r
e
p
o
r
te
d
o
n
ly
ac
cu
r
ac
y
,
wh
ile
o
th
er
m
et
r
ics
s
u
ch
as
AU
C
,
F1
-
s
co
r
e,
r
ec
all,
an
d
p
r
ec
is
io
n
ar
e
also
im
p
o
r
tan
t
f
o
r
d
iag
n
o
s
tic
ap
p
licatio
n
s
.
I
t
also
d
id
n
o
t
ass
ess
th
e
m
o
d
el’
s
r
o
b
u
s
tn
ess
to
n
o
is
e,
m
is
s
in
g
v
alu
es,
o
r
s
m
all
d
atasets
.
L
astl
y
,
th
e
f
ea
tu
r
e
e
x
tr
ac
tio
n
a
p
p
r
o
ac
h
co
u
ld
b
e
c
o
m
p
ar
e
d
with
o
th
er
m
et
h
o
d
s
to
b
etter
ev
alu
ate
its
ef
f
ec
tiv
en
ess
.
An
o
th
er
s
tu
d
y
o
n
h
y
b
r
id
m
ac
h
in
e
lear
n
in
g
was
co
n
d
u
cted
b
y
T
ag
h
izad
eh
et
a
l.
[
1
8
]
,
c
o
m
b
in
in
g
f
ea
tu
r
e
s
ele
ctio
n
,
f
ea
tu
r
e
ex
tr
ac
tio
n
,
a
n
d
class
if
icatio
n
to
id
en
tif
y
b
r
ea
s
t
ca
n
ce
r
.
T
h
e
s
tu
d
y
u
s
ed
R
NA
s
eq
u
en
cin
g
d
ata
f
r
o
m
th
e
T
C
GA
d
atab
ase.
T
h
e
b
est
r
esu
lts
wer
e
o
b
tain
e
d
u
s
in
g
th
e
L
GR
f
ea
tu
r
e
s
elec
tio
n
m
eth
o
d
with
a
m
u
ltil
ay
er
p
er
c
ep
tr
o
n
(
ML
P)
class
if
ier
,
ac
h
i
ev
i
n
g
a
b
alan
ce
d
ac
cu
r
ac
y
o
f
0
.
8
6
a
n
d
an
AUC
o
f
0
.
9
4
.
H
o
wev
er
,
d
u
e
to
th
e
co
m
p
lex
ity
a
n
d
n
o
is
in
ess
o
f
R
NA
-
s
eq
d
ata,
s
u
ch
h
ig
h
ac
cu
r
ac
y
r
aises
co
n
ce
r
n
s
ab
o
u
t
o
v
er
f
itti
n
g
.
A
d
d
itio
n
ally
,
th
e
s
tu
d
y
d
id
n
o
t
em
p
lo
y
k
-
f
o
ld
c
r
o
s
s
-
v
alid
atio
n
,
m
ak
in
g
th
e
r
esu
lt
s
m
o
r
e
s
u
s
ce
p
tib
le
to
b
ias
f
r
o
m
d
ata
s
p
litt
in
g
an
d
less
r
e
liab
le
f
o
r
b
r
o
ad
er
ap
p
licatio
n
.
A
s
im
ilar
s
tu
d
y
b
y
Nad
em
et
a
l.
[
1
2
]
d
em
o
n
s
tr
a
ted
th
at
co
m
b
in
in
g
ar
tific
ial
n
eu
r
al
n
etwo
r
k
s
with
tr
ad
itio
n
al
m
ac
h
in
e
lear
n
i
n
g
alg
o
r
ith
m
s
h
av
e
en
h
an
ce
d
th
e
ac
cu
r
ac
y
o
f
co
l
o
n
ca
n
ce
r
p
r
ed
ictio
n
s
b
y
u
p
to
6
.
6
7
%
an
d
1
0
.
4
3
%
co
m
p
ar
e
d
to
s
tan
d
ar
d
m
eth
o
d
s
.
T
h
e
h
ig
h
est
ac
cu
r
ac
y
was
ac
h
iev
ed
b
y
th
e
R
FNN
m
o
d
el,
r
ea
ch
in
g
8
9
.
8
1
%.
Ho
wev
er
,
t
h
e
s
tu
d
y
p
r
o
v
id
e
d
lim
ited
d
et
ails
ab
o
u
t
th
e
f
ea
tu
r
e
s
elec
tio
n
p
r
o
ce
s
s
.
I
t
d
id
n
o
t
clea
r
ly
ex
p
lai
n
wh
ich
alg
o
r
i
th
m
s
wer
e
u
s
ed
,
h
o
w
f
ea
t
u
r
es
wer
e
s
elec
ted
,
o
r
wh
eth
er
f
e
atu
r
e
s
tab
ilit
y
was
ev
alu
ated
.
Fu
r
th
er
m
o
r
e,
it
d
id
n
o
t
m
en
tio
n
wh
eth
e
r
k
-
f
o
ld
cr
o
s
s
-
v
alid
atio
n
(
s
u
ch
as
5
-
f
o
ld
o
r
1
0
-
f
o
ld
)
was
ap
p
lied
,
wh
ich
is
cr
u
cial
to
r
e
d
u
ce
b
ias f
r
o
m
r
a
n
d
o
m
d
ata
s
p
lits
.
3.
M
E
T
H
OD
T
h
e
ev
alu
atio
n
p
h
ase
o
f
th
e
m
eth
o
d
n
ec
ess
itates
a
clea
r
ly
d
ef
in
ed
an
d
h
ig
h
l
y
ac
cu
r
ate
ap
p
r
o
ac
h
.
T
h
e
p
r
o
p
o
s
ed
m
eth
o
d
s
er
v
es
a
cr
itical
f
u
n
ctio
n
in
th
e
r
esear
ch
p
r
o
ce
s
s
,
f
ac
ilit
atin
g
th
e
ac
h
iev
em
en
t
o
f
th
e
d
esire
d
o
u
tc
o
m
es,
as
illu
s
tr
ate
d
in
Fig
u
r
e
1
.
T
h
e
d
esig
n
o
f
f
er
s
a
th
o
r
o
u
g
h
o
v
er
v
iew
o
f
th
e
r
esear
ch
p
r
o
ce
s
s
,
en
s
u
r
in
g
a
clea
r
u
n
d
er
s
tan
d
in
g
o
f
ea
ch
s
tep
in
v
o
lv
e
d
,
wh
ic
h
ar
e
o
u
tlin
ed
s
y
s
tem
atica
lly
.
T
h
e
s
tep
s
in
clu
d
e:
i)
co
llectin
g
d
ata
f
r
o
m
ME
T
AB
R
I
C
,
ii)
d
ata
in
s
p
ec
tio
n
,
ii
i)
p
r
e
p
r
o
ce
s
s
in
g
th
e
d
ata
an
d
p
er
f
o
r
m
in
g
1
0
-
f
o
ld
cr
o
s
s
-
v
alid
atio
n
,
iii)
f
ea
tu
r
e
s
elec
tio
n
an
d
f
ea
tu
r
e
ex
t
r
ac
tio
n
,
iv
)
ap
p
ly
i
n
g
m
ac
h
in
e
lear
n
in
g
alg
o
r
ith
m
s
an
d
s
elec
tin
g
th
e
b
est
-
p
er
f
o
r
m
in
g
m
o
d
el,
v
)
e
v
alu
atin
g
an
d
v
alid
atin
g
th
e
r
esu
lts
,
an
d
v
i)
g
e
n
er
atin
g
a
p
er
f
o
r
m
an
ce
r
e
p
o
r
t.
T
h
is
m
et
h
o
d
o
lo
g
y
en
s
u
r
es tr
a
n
s
p
ar
en
cy
an
d
r
ep
r
o
d
u
cib
ilit
y
,
wh
ich
ar
e
ess
en
tial p
illar
s
o
f
r
o
b
u
s
t scien
tific
in
q
u
ir
y
.
3
.
1
.
B
re
a
s
t
c
a
ncer
m
RNA
d
a
t
a
s
et
(
M
E
T
AB
RIC)
T
h
is
s
tu
d
y
le
v
er
ag
es
th
e
b
r
ea
s
t
ca
n
ce
r
m
R
NA
d
ataset
p
r
o
v
i
d
ed
b
y
t
h
e
ME
T
AB
R
I
C
.
R
ec
o
g
n
ized
f
o
r
its
ex
ten
s
iv
e
v
alid
atio
n
a
n
d
b
r
o
ad
citatio
n
with
in
th
e
b
r
ea
s
t
ca
n
ce
r
r
esear
ch
co
m
m
u
n
ity
,
th
e
ME
T
AB
R
I
C
d
ataset
in
teg
r
ates
d
etailed
clin
ical,
p
ath
o
lo
g
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an
d
m
o
l
ec
u
lar
p
r
o
f
iles
f
r
o
m
a
d
iv
er
s
e
ar
r
ay
o
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tu
m
o
r
s
p
ec
im
en
s
,
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er
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y
o
f
f
er
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g
a
r
o
b
u
s
t
f
o
u
n
d
atio
n
f
o
r
co
m
p
r
eh
en
s
iv
e
an
aly
s
es.
T
h
e
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ataset
in
clu
d
es
a
b
r
o
ad
s
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ec
tr
u
m
o
f
g
en
o
m
ic
d
ata,
en
co
m
p
ass
in
g
g
e
n
etic
m
u
tati
o
n
s
,
g
en
e
e
x
p
r
ess
io
n
p
r
o
f
ile
s
,
an
d
ep
ig
e
n
etic
m
o
d
if
ica
tio
n
s
,
alo
n
g
s
id
e
clin
i
ca
l
v
ar
iab
les
an
d
o
th
er
p
er
tin
en
t
r
is
k
f
ac
to
r
s
.
I
t
h
as
b
ee
n
wid
ely
em
p
lo
y
e
d
in
Evaluation Warning : The document was created with Spire.PDF for Python.
I
n
t J E
lec
&
C
o
m
p
E
n
g
I
SS
N:
2088
-
8
7
0
8
B
r
ea
s
t c
a
n
ce
r
id
en
tifi
ca
tio
n
u
s
in
g
a
h
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ch
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(
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n
i A
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3931
n
u
m
er
o
u
s
s
tu
d
ies
to
in
v
esti
g
at
e
th
e
h
eter
o
g
en
eity
o
f
b
r
ea
s
t
ca
n
ce
r
an
d
to
id
en
tif
y
p
o
ten
tial
b
io
m
ar
k
er
s
f
o
r
its
ea
r
ly
d
etec
tio
n
an
d
d
iag
n
o
s
is
[
2
4
]
.
I
n
th
e
p
r
esen
t
s
tu
d
y
,
t
h
e
d
ataset
co
m
p
r
is
es
6
9
2
attr
ib
u
tes
ac
r
o
s
s
1
,
9
0
4
s
am
p
les,
p
r
o
v
id
in
g
a
r
o
b
u
s
t a
n
d
r
eliab
l
e
f
o
u
n
d
atio
n
f
o
r
an
aly
tical
m
o
d
elin
g
an
d
p
r
ed
ictiv
e
an
aly
s
is
.
Fig
u
r
e
1
.
Pro
p
o
s
ed
m
et
h
o
d
h
y
b
r
id
m
ac
h
i
n
e
lear
n
in
g
s
y
s
tem
3
.
2
.
Da
t
a
ins
pect
io
n
I
n
s
p
ec
tio
n
a
n
d
e
v
alu
atio
n
o
f
d
atasets
in
m
ac
h
in
e
lear
n
in
g
en
tails
a
co
m
p
r
e
h
en
s
iv
e
ev
al
u
atio
n
an
d
u
n
d
er
s
tan
d
i
n
g
o
f
t
h
e
d
ataset
p
r
io
r
to
im
p
lem
en
tin
g
p
r
ep
r
o
ce
s
s
in
g
s
tep
s
o
r
b
u
ild
in
g
m
o
d
els.
T
h
is
p
r
o
ce
s
s
is
cr
itical
f
o
r
g
u
id
in
g
in
f
o
r
m
ed
d
ec
is
io
n
s
r
eg
ar
d
in
g
d
a
ta
p
r
e
p
r
o
ce
s
s
in
g
an
d
f
ea
tu
r
e
s
elec
tio
n
,
b
o
th
o
f
wh
ich
ar
e
f
u
n
d
am
e
n
tal
to
en
h
an
ci
n
g
t
h
e
ac
cu
r
ac
y
an
d
p
er
f
o
r
m
an
c
e
o
f
m
ac
h
in
e
lear
n
i
n
g
m
o
d
els.
As
an
in
teg
r
a
l
co
m
p
o
n
en
t
o
f
d
ata
an
aly
s
is
,
d
ata
in
s
p
ec
tio
n
in
v
o
lv
es
s
cr
u
tin
izin
g
th
e
d
ataset
to
id
en
tify
th
e
m
o
s
t
ap
p
r
o
p
r
iate
p
r
o
ce
s
s
in
g
tech
n
iq
u
es
to
en
h
a
n
ce
m
o
d
el
p
e
r
f
o
r
m
an
ce
[
2
5
]
.
I
n
th
is
s
tu
d
y
,
d
ata
in
s
p
ec
tio
n
wa
s
p
er
f
o
r
m
ed
at
th
e
o
u
ts
et,
in
v
o
lv
in
g
a
n
in
-
d
e
p
th
an
aly
s
is
o
f
th
e
d
ata.
T
h
e
o
b
jectiv
e
was
to
ev
alu
ate
th
e
d
ata’
s
ch
ar
ac
ter
is
tics
an
d
q
u
ality
,
allo
win
g
f
o
r
th
e
id
en
tific
atio
n
o
f
th
e
ap
p
r
o
p
r
iat
e
p
r
ep
r
o
ce
s
s
in
g
tech
n
iq
u
es to
b
e
u
tili
ze
d
.
3
.
3
.
P
re
pro
ce
s
s
ing
da
t
a
a
nd
cr
o
s
s
v
a
lid
a
t
io
n
Data
p
r
ep
r
o
ce
s
s
in
g
,
p
ar
ticu
la
r
ly
d
ata
tr
an
s
f
o
r
m
atio
n
,
is
a
cr
itical
s
tep
in
m
ac
h
in
e
lear
n
in
g
.
I
t
in
v
o
lv
es
co
n
v
er
tin
g
r
aw
d
ata
i
n
to
a
f
o
r
m
at
th
at
is
m
o
r
e
s
u
ita
b
le
f
o
r
a
n
aly
s
is
an
d
m
o
d
el
tr
ai
n
in
g
.
T
h
is
p
r
o
ce
s
s
ca
n
s
ig
n
if
ican
tly
im
p
ac
t
th
e
q
u
ality
an
d
ef
f
icien
c
y
o
f
th
e
r
e
s
u
ltin
g
m
o
d
els.
I
n
th
is
r
esear
ch
,
th
e
f
ir
s
t
s
tag
e
o
f
th
e
d
ata
p
r
ep
r
o
ce
s
s
in
g
tech
n
iq
u
e
is
d
ata
tr
an
s
f
o
r
m
atio
n
(
d
is
cr
etiza
tio
n
)
,
f
o
llo
wed
b
y
th
e
s
ec
o
n
d
s
tag
e,
wh
ich
in
v
o
lv
es
r
e
p
lacin
g
m
is
s
in
g
v
al
u
es.
T
h
ese
tech
n
iq
u
es
wer
e
ch
o
s
en
b
ec
au
s
e
th
ey
o
f
f
er
s
ev
er
a
l
ad
v
an
ta
g
es,
s
u
ch
as c
o
n
v
er
tin
g
d
ata
in
to
a
f
o
r
m
at
s
u
itab
le
f
o
r
lear
n
in
g
alg
o
r
ith
m
s
an
d
im
p
r
o
v
in
g
th
e
ac
cu
r
a
cy
an
d
ef
f
icien
cy
o
f
m
in
in
g
alg
o
r
ith
m
s
.
On
ce
th
i
s
s
tag
e
is
co
m
p
leted
,
th
e
n
e
x
t
s
tep
is
th
e
im
p
lem
en
tatio
n
o
f
1
0
-
fo
ld
cr
o
s
s
-
v
alid
atio
n
f
o
r
s
p
litt
in
g
th
e
tr
ain
in
g
an
d
test
in
g
d
ata.
An
ad
d
i
tio
n
al
o
b
jectiv
e
o
f
em
p
lo
y
i
n
g
cr
o
s
s
-
v
alid
atio
n
is
to
o
b
tain
a
m
o
r
e
ac
cu
r
ate
ev
al
u
atio
n
,
e
n
s
u
r
in
g
th
at
t
h
e
m
o
d
el
n
o
t
o
n
ly
ac
q
u
ir
es
k
n
o
wled
g
e
f
r
o
m
th
e
tr
ain
in
g
d
ata
b
u
t
also
g
e
n
er
alize
s
ef
f
ec
tiv
ely
to
n
ew,
u
n
s
ee
n
d
ata
[
2
6
]
.
3
.
4
.
F
e
a
t
ure
s
elec
t
io
n
a
nd
f
e
a
t
ure
ex
t
ra
c
t
io
n
Featu
r
e
s
elec
tio
n
an
d
f
ea
tu
r
e
ex
tr
ac
tio
n
a
r
e
ess
en
tial
m
eth
o
d
s
in
m
ac
h
in
e
lea
r
n
in
g
,
esp
ec
ially
wh
en
d
ea
lin
g
with
h
ig
h
-
d
im
e
n
s
io
n
al
d
ata.
T
h
ese
tech
n
iq
u
es
f
o
cu
s
o
n
r
ed
u
cin
g
d
ata
d
im
en
s
io
n
ality
to
en
h
a
n
ce
Evaluation Warning : The document was created with Spire.PDF for Python.
I
SS
N
:
2
0
8
8
-
8
7
0
8
I
n
t J E
lec
&
C
o
m
p
E
n
g
,
Vo
l.
15
,
No
.
4
,
Au
g
u
s
t
20
25
:
3
9
2
8
-
3937
3932
m
o
d
el
p
e
r
f
o
r
m
an
ce
,
l
o
wer
c
o
m
p
u
tatio
n
al
co
s
ts
,
an
d
im
p
r
o
v
e
in
ter
p
r
etab
ilit
y
[
2
5
]
.
T
h
e
f
ea
tu
r
e
s
elec
tio
n
alg
o
r
ith
m
s
u
s
ed
in
th
is
s
tu
d
y
i
n
clu
d
e
ANOV
A,
MI
,
L
R
,
an
d
E
T
C
.
T
h
ese
alg
o
r
ith
m
s
o
f
f
er
s
ev
er
al
ad
v
a
n
tag
es,
s
u
ch
as
r
ed
u
cin
g
co
m
p
u
tatio
n
tim
e
an
d
m
o
d
el
co
m
p
le
x
ity
,
i
m
p
r
o
v
i
n
g
lear
n
in
g
ac
cu
r
ac
y
,
a
n
d
h
el
p
in
g
to
a
v
o
i
d
o
v
er
f
itti
n
g
.
PC
A
is
u
tili
ze
d
f
o
r
f
ea
tu
r
e
ex
tr
ac
tio
n
in
t
h
is
s
tu
d
y
.
T
h
is
tech
n
iq
u
e
p
r
o
v
id
es
th
e
b
en
ef
it
o
f
r
ed
u
cin
g
h
i
g
h
-
d
im
en
s
io
n
al
d
a
ta
to
a
lo
wer
-
d
im
en
s
io
n
al
r
ep
r
esen
tatio
n
wh
ile
r
etain
in
g
m
o
s
t
o
f
th
e
o
r
ig
in
al
v
ar
ian
ce
,
th
e
r
eb
y
f
ac
ilit
atin
g
m
o
r
e
ef
f
icien
t
d
ata
an
aly
s
is
with
o
u
t sig
n
if
ican
t lo
s
s
o
f
c
r
itical
in
f
o
r
m
atio
n
[
2
7
]
.
3
.
5
.
M
a
chine
lea
rning
a
lg
o
ri
t
hm
Ma
ch
in
e
lear
n
in
g
(
ML
)
is
a
r
ap
id
ly
ad
v
an
cin
g
d
is
cip
lin
e
t
h
at
lies
at
th
e
in
ter
s
ec
tio
n
o
f
co
m
p
u
ter
s
cien
ce
an
d
s
tatis
tics
,
f
o
cu
s
ed
o
n
th
e
d
ev
elo
p
m
en
t
o
f
alg
o
r
it
h
m
s
th
at
en
a
b
le
co
m
p
u
ter
s
to
lear
n
f
r
o
m
d
ata
an
d
g
en
er
ate
ac
cu
r
ate
p
r
ed
ictio
n
s
o
r
d
ec
is
io
n
s
b
ased
o
n
th
at
i
n
f
o
r
m
atio
n
.
I
n
th
is
s
tu
d
y
,
th
e
m
ac
h
in
e
lear
n
in
g
alg
o
r
ith
m
s
u
s
ed
f
o
r
tr
ain
in
g
a
r
e
ca
p
a
b
le
o
f
h
an
d
lin
g
lar
g
e
a
n
d
c
o
m
p
lex
d
atasets
[
1
2
]
,
in
c
lu
d
in
g
SVMs
,
RF
,
NB
,
KNN
,
ETC
,
an
d
LR
.
So
m
e
o
f
th
e
alg
o
r
ith
m
s
u
s
ed
f
o
r
f
ea
tu
r
e
s
elec
tio
n
,
s
u
ch
as
L
R
an
d
ETC
,
ar
e
also
ap
p
lied
in
th
e
class
if
icatio
n
p
r
o
ce
s
s
.
T
h
ese
alg
o
r
ith
m
s
we
r
e
ch
o
s
en
b
ec
au
s
e
th
e
y
h
a
v
e
m
ad
e
a
s
ig
n
if
ican
t
im
p
ac
t
in
v
ar
io
u
s
f
ield
s
,
p
a
r
ticu
lar
ly
h
ea
lth
ca
r
e,
b
y
allo
win
g
co
m
p
u
ter
s
to
lear
n
f
r
o
m
d
a
ta
an
d
m
ak
e
d
ata
-
d
r
iv
en
d
ec
is
io
n
s
[
2
8
]
.
3
.
6
.
E
v
a
lua
t
i
o
n
a
nd
v
a
lid
a
t
i
o
n
T
h
is
p
h
ase
is
d
ed
icate
d
to
e
v
alu
atin
g
th
e
p
er
f
o
r
m
a
n
ce
o
f
th
e
d
ev
elo
p
ed
m
o
d
el.
I
n
th
i
s
s
tu
d
y
,
a
class
if
icatio
n
r
ep
o
r
t
is
em
p
lo
y
ed
as
th
e
ev
alu
atio
n
m
eth
o
d
,
p
r
o
v
id
i
n
g
a
d
etailed
s
u
m
m
a
r
y
o
f
th
e
p
er
f
o
r
m
a
n
ce
m
etr
ics
ass
o
ciate
d
with
ea
ch
ap
p
lied
tech
n
iq
u
e.
T
h
is
s
tep
is
f
u
n
d
am
en
tal
f
o
r
estab
lis
h
in
g
th
e
r
o
b
u
s
tn
ess
,
ef
f
ec
tiv
en
ess
,
an
d
r
ep
r
o
d
u
cib
ilit
y
o
f
th
e
class
if
icatio
n
m
o
d
el,
p
ar
ticu
lar
ly
in
h
ea
lth
ca
r
e
ap
p
licatio
n
s
wh
er
e
ac
cu
r
ac
y
is
cr
itical.
A
co
m
p
r
eh
en
s
iv
e
s
et
o
f
ev
alu
atio
n
te
ch
n
iq
u
es
is
s
y
s
tem
atica
lly
ap
p
lied
to
r
ig
o
r
o
u
s
ly
ass
es
s
b
o
th
th
e
p
r
ed
ictiv
e
ac
cu
r
ac
y
,
g
en
e
r
aliza
b
ilit
y
,
an
d
r
o
b
u
s
tn
ess
o
f
th
e
m
ac
h
in
e
lear
n
in
g
m
o
d
el.
Du
r
in
g
th
e
test
in
g
p
h
ase,
q
u
a
n
titativ
e
m
etr
ics
-
in
clu
d
in
g
ac
c
u
r
ac
y
,
p
r
ec
is
io
n
,
r
ec
all,
an
d
F1
-
s
co
r
e
-
ar
e
s
y
s
tem
atica
lly
ap
p
lied
to
en
s
u
r
e
a
th
o
r
o
u
g
h
an
d
s
tatis
tically
s
o
u
n
d
ev
alu
atio
n
o
f
t
h
e
m
o
d
el’
s
p
r
e
d
ictiv
e
ca
p
a
b
ilit
ies.
Acc
u
r
ac
y
r
ef
lects
th
e
o
v
er
all
co
r
r
ec
tn
ess
o
f
th
e
m
o
d
el’
s
p
r
ed
ictio
n
s
,
wh
ile
r
ec
all
m
ea
s
u
r
es
its
ab
ilit
y
to
co
r
r
ec
tly
id
e
n
tify
all
r
ele
v
an
t
p
o
s
itiv
e
in
s
tan
ce
s
.
Pre
cisi
o
n
ev
alu
ates
th
e
r
atio
o
f
tr
u
e
p
o
s
it
iv
es
to
all
in
s
tan
ce
s
pr
ed
icted
as
p
o
s
itiv
e.
T
h
e
F1
-
s
co
r
e,
ca
lcu
lated
as
th
e
h
ar
m
o
n
ic
m
ea
n
o
f
p
r
ec
is
io
n
a
n
d
r
ec
all,
s
er
v
es
as
a
r
o
b
u
s
t
an
d
in
teg
r
ativ
e
m
et
r
ic
f
o
r
ass
ess
in
g
th
e
class
if
icatio
n
ef
f
ec
tiv
en
ess
o
f
th
e
m
o
d
el.
T
a
k
en
to
g
eth
e
r
,
th
ese
m
etr
ics p
r
o
v
id
e
a
th
o
r
o
u
g
h
an
d
r
o
b
u
s
t a
s
s
ess
m
en
t o
f
th
e
m
o
d
el’
s
p
r
ed
ictiv
e
p
e
r
f
o
r
m
an
ce
[
6
]
.
4.
RE
SU
L
T
S AN
D
D
I
SCU
SS
I
O
N
Up
o
n
co
m
p
letio
n
o
f
all
ex
p
e
r
im
en
tal
p
h
ases
,
th
e
m
o
d
el
was
r
ig
o
r
o
u
s
ly
ev
alu
ate
d
u
s
in
g
1
0
-
f
o
ld
cr
o
s
s
-
v
alid
atio
n
an
d
f
u
r
th
er
a
n
aly
ze
d
th
r
o
u
g
h
a
c
o
n
f
u
s
io
n
m
atr
ix
,
wh
ich
r
ep
o
r
ts
k
ey
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
F1
-
s
co
r
e.
T
h
e
r
esu
lts
d
em
o
n
s
tr
ate
th
at
th
e
SVM
co
n
s
is
ten
tly
o
u
tp
er
f
o
r
m
s
o
th
er
al
g
o
r
ith
m
s
,
ex
h
ib
itin
g
s
u
p
e
r
io
r
class
if
icatio
n
p
er
f
o
r
m
an
ce
.
As
p
r
es
en
ted
in
T
ab
le
1
,
Fig
u
r
e
2
an
d
Fig
u
r
e
3
(
a
)
-
3
(
d
)
,
th
e
SVM
co
m
b
in
ed
with
MI
-
b
ased
f
ea
tu
r
e
s
elec
tio
n
ac
h
iev
es
th
e
h
ig
h
est
p
er
f
o
r
m
an
ce
,
with
a
n
ac
c
u
r
ac
y
o
f
9
9
.
4
%
an
d
id
en
tical
p
r
ec
is
io
n
,
r
ec
all,
a
n
d
F1
-
s
co
r
e
v
al
u
es
o
f
0
.
9
9
4
0
.
T
h
is
s
tr
o
n
g
p
er
f
o
r
m
a
n
ce
ca
n
b
e
p
r
im
ar
ily
attr
ib
u
ted
to
th
e
c
o
m
p
r
eh
en
s
iv
e
d
ata
p
r
e
p
r
o
ce
s
s
in
g
co
n
d
u
cted
in
th
e
ea
r
ly
s
tag
es,
wh
ich
ef
f
ec
tiv
el
y
r
ef
in
e
d
th
e
in
p
u
t
d
ata
an
d
en
ab
led
th
e
SVM
t
o
m
an
ag
e
an
d
s
im
p
lify
a
n
o
th
er
wis
e
co
m
p
lex
class
if
icatio
n
task
.
Fu
r
th
e
r
m
o
r
e,
th
e
ap
p
licatio
n
o
f
f
ea
t
u
r
e
s
elec
tio
n
co
n
tr
ib
u
ted
to
a
m
o
r
e
ef
f
icien
t
m
o
d
el
b
y
r
ed
u
ci
n
g
d
im
en
s
io
n
ality
an
d
r
etain
in
g
th
e
m
o
s
t
r
elev
an
t
f
ea
tu
r
es,
th
er
eb
y
en
h
a
n
cin
g
th
e
alg
o
r
ith
m
’
s
p
r
ed
ictiv
e
ca
p
ab
il
ity
an
d
co
m
p
u
tat
io
n
al
ef
f
icien
cy
.
T
h
e
s
ec
o
n
d
an
d
th
ir
d
p
o
s
i
tio
n
s
ar
e
also
h
eld
b
y
SVM
m
o
d
els
b
u
t
with
d
if
f
er
en
t
f
ea
tu
r
e
s
elec
tio
n
m
eth
o
d
s
:
LR
an
d
ETC
,
b
o
th
ac
h
iev
in
g
an
ac
c
u
r
ac
y
o
f
9
9
.
2
2
%.
T
h
eir
p
r
ec
is
io
n
,
r
ec
al
l,
an
d
ac
cu
r
ac
y
v
alu
es
a
r
e
clo
s
ely
m
atch
ed
at
0
.
9
9
2
1
an
d
0
.
9
9
2
2
,
alth
o
u
g
h
th
e
F1
-
s
co
r
e
d
if
f
e
r
s
s
lig
h
tly
,
with
th
e
ETC
y
ield
in
g
0
.
9
2
2
1
.
I
n
f
o
u
r
th
p
lace
,
t
h
e
SVM
with
ANOV
A
ac
h
iev
es
a
s
lig
h
tly
lo
wer
ac
cu
r
ac
y
o
f
9
9
.
1
7
%,
a
p
r
ec
is
io
n
o
f
0
.
9
9
1
8
,
a
r
ec
all
o
f
0
.
9
9
2
,
an
d
an
F1
-
s
co
r
e
o
f
0
.
9
9
1
9
.
C
o
n
v
er
s
ely
,
th
e
SVM
with
f
e
atu
r
e
ex
tr
ac
tio
n
u
s
in
g
PC
A
ex
h
ib
its
th
e
lo
west
p
er
f
o
r
m
an
c
e,
with
an
ac
cu
r
ac
y
o
f
2
9
.
6
8
%,
a
p
r
ec
is
io
n
o
f
0
.
2
2
0
4
,
a
r
ec
all
o
f
0
.
3
0
1
4
,
an
d
a
n
F1
-
s
co
r
e
o
f
0
.
2
2
6
3
.
T
h
ese
r
esu
lts
h
ig
h
lig
h
t
th
e
s
ig
n
if
ican
tly
p
o
o
r
p
e
r
f
o
r
m
an
c
e
o
f
SVM
+
PC
A
co
m
p
ar
ed
to
o
th
er
alg
o
r
ith
m
s
,
a
tr
en
d
co
n
s
is
ten
t
with
o
th
er
m
ac
h
in
e
lear
n
i
n
g
m
o
d
els
test
ed
with
PC
A,
all
o
f
wh
ich
p
r
o
d
u
ce
d
s
im
ilar
ly
lo
w
v
alu
e
s
,
as
illu
s
tr
ated
in
Fig
u
r
e
2
.
T
h
is
s
tu
d
y
i
n
clu
d
es
c
o
m
p
ar
is
o
n
s
with
s
im
ilar
r
esear
ch
.
So
m
e
o
f
th
e
s
tu
d
ies
r
ef
er
e
n
ce
d
a
r
e
Al
-
R
ajab
et
a
l.
[
2
3
]
,
wh
o
p
r
o
p
o
s
ed
a
H
ML
S
b
y
i
n
teg
r
atin
g
I
G
with
GA
an
d
c
o
u
p
lin
g
m
i
n
im
u
m
r
e
d
u
n
d
a
n
cy
m
ax
im
u
m
r
elev
an
ce
(
m
R
MR)
with
PS
O
,
as
well
as
T
ag
h
i
za
d
eh
et
a
l.
[
1
8
]
,
w
h
o
im
p
lem
e
n
ted
HM
L
S
u
s
in
g
th
e
L
GR
f
ea
tu
r
e
s
elec
tio
n
m
et
h
o
d
co
u
p
led
with
an
ML
P
class
if
ier
to
ac
h
iev
e
h
ig
h
ac
cu
r
ac
y
.
T
h
e
r
esu
lts
o
f
t
h
ese
co
m
p
ar
is
o
n
s
ar
e
p
r
esen
ted
in
T
ab
le
2
.
Evaluation Warning : The document was created with Spire.PDF for Python.
I
n
t J E
lec
&
C
o
m
p
E
n
g
I
SS
N:
2088
-
8
7
0
8
B
r
ea
s
t c
a
n
ce
r
id
en
tifi
ca
tio
n
u
s
in
g
a
h
y
b
r
id
ma
ch
in
e
…
(
To
n
i A
r
ifin
)
3933
Fig
u
r
e
2
.
Acc
u
r
ac
y
h
ea
tm
ap
o
f
th
e
f
ea
tu
r
e
s
elec
tio
n
,
f
ea
tu
r
e
ex
tr
ac
tio
n
,
an
d
class
if
icatio
n
p
r
o
ce
d
u
r
es
T
ab
le
1
.
T
h
e
ev
alu
atio
n
r
esu
lt
s
with
th
e
SVM
,
f
ea
tu
r
e
s
elec
tio
n
,
an
d
f
ea
tu
r
e
ex
tr
ac
tio
n
S
u
p
p
o
r
t
v
e
c
t
o
r
ma
c
h
i
n
e
A
l
g
o
r
i
t
h
m
A
c
c
u
r
a
c
y
P
r
e
c
i
s
i
o
n
R
e
c
a
l
l
F1
-
s
c
o
r
e
M
u
t
u
a
l
i
n
f
o
r
m
a
t
i
o
n
99
.
4
0
%
0
.
9
9
4
0
0
.
9
9
4
0
0
,
9
9
4
0
Lo
g
i
s
t
i
c
r
e
g
r
e
ssi
o
n
99
.
2
0
%
0
.
9
9
2
1
0
.
9
9
2
2
0
.
9
9
2
1
Ex
t
r
a
t
r
e
e
c
l
a
ssi
f
i
e
r
99
.
2
0
%
0
.
9
9
2
1
0
.
9
9
2
2
0
.
9
2
2
1
A
n
a
l
y
s
i
s
o
f
v
a
r
i
a
n
c
e
99
.
1
7
%
0
.
9
9
1
8
0
.
9
9
2
0
.
9
9
1
9
P
r
i
n
c
i
p
a
l
c
o
mp
o
n
e
n
t
a
n
a
l
y
s
i
s
29
.
6
8
%
0
.
2
2
0
4
0
.
3
0
1
4
0
.
2
2
6
3
(
a)
(
b
)
(
c)
(
d
)
Fig
u
r
e
3
.
Dep
icts
th
e
e
v
alu
atio
n
o
u
tc
o
m
es b
ased
o
n
m
u
t
u
al
in
f
o
r
m
atio
n
,
in
clu
d
in
g
(
a
)
ac
c
u
r
ac
y
,
(
b
)
p
r
ec
is
io
n
,
(
c)
r
ec
all,
an
d
(
d
)
F1
-
s
co
r
e
T
ab
le
2
s
h
o
ws
th
at
th
e
p
r
o
p
o
s
ed
m
o
d
el,
p
ar
ticu
la
r
ly
th
e
SVM
+
MI
alg
o
r
ith
m
,
o
u
tp
e
r
f
o
r
m
s
p
r
ev
io
u
s
s
tu
d
ies
th
at
also
u
ti
lized
h
y
b
r
i
d
m
ac
h
in
e
lear
n
in
g
tech
n
iq
u
e
s
an
d
co
m
p
lex
d
atasets
.
An
o
th
er
n
o
tab
le
f
in
d
in
g
in
th
is
s
tu
d
y
is
th
at
th
e
im
p
lem
en
tatio
n
o
f
f
ea
tu
r
e
ex
tr
ac
tio
n
with
PC
A,
as
ex
p
lain
ed
in
T
ab
le
1
,
y
ield
ed
t
h
e
lo
west
r
esu
lts
co
m
p
ar
ed
t
o
th
e
f
ea
tu
r
e
s
elec
tio
n
alg
o
r
ith
m
s
ap
p
lied
.
T
h
is
h
ig
h
lig
h
ts
a
lim
itatio
n
o
f
o
u
r
s
tu
d
y
:
Evaluation Warning : The document was created with Spire.PDF for Python.
I
SS
N
:
2
0
8
8
-
8
7
0
8
I
n
t J E
lec
&
C
o
m
p
E
n
g
,
Vo
l.
15
,
No
.
4
,
Au
g
u
s
t
20
25
:
3
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we
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ith
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ith
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ith
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f
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m
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p
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S
.
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G
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H
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F
AUTH
O
RS
To
n
i
Ar
ifi
n
He
is
a
m
e
m
b
e
r
o
f
th
e
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a
c
u
lt
y
o
f
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n
g
in
e
e
rin
g
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m
a
jo
ri
n
g
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n
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fo
rm
a
ti
c
s
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n
g
i
n
e
e
rin
g
,
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d
h
iraj
a
sa
Re
sw
a
ra
S
a
n
jay
a
(ARS)
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i
v
e
rsity
,
a
n
d
re
se
a
rc
h
e
r
AR
S
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it
a
l
Re
se
a
rc
h
&
In
n
o
v
a
ti
o
n
(AD
RI).
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re
c
e
iv
e
d
h
is
b
a
c
h
e
lo
r’s
d
e
g
re
e
in
i
n
fo
rm
a
ti
c
s
e
n
g
in
e
e
rin
g
fro
m
Bin
a
S
a
ra
n
a
In
fo
rm
a
ti
k
a
Un
iv
e
rsit
y
in
2
0
1
3
a
n
d
g
ra
d
u
a
ted
fro
m
th
e
c
o
m
p
u
ter
sc
ien
c
e
m
a
ste
r’s
p
ro
g
ra
m
at
Nu
sa
M
a
n
d
iri
Un
iv
e
rsit
y
Ja
k
a
rta
in
2
0
1
5
.
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h
a
s
a
u
th
o
re
d
o
r
c
o
a
u
t
h
o
re
d
m
o
re
t
h
a
n
7
3
p
u
b
li
c
a
ti
o
n
s:
4
p
r
o
c
e
e
d
in
g
s
a
n
d
6
9
jo
u
rn
a
ls,
wit
h
1
4
h
-
in
d
e
x
a
n
d
m
o
re
t
h
a
n
6
3
0
c
it
a
ti
o
n
s.
Re
se
a
rc
h
in
tere
sts
in
c
l
u
d
e
m
a
c
h
in
e
lea
rn
in
g
,
ima
g
e
p
ro
c
e
ss
in
g
a
n
d
d
e
e
p
lea
rn
i
n
g
.
He
c
a
n
b
e
c
o
n
tac
ted
a
t
e
m
a
il
:
to
n
i.
a
ri
fin
@a
rs.
a
c
.
id
.
Ig
n
a
ti
u
s
Wise
to
Pra
se
ty
o
A
g
u
n
g
Afte
r
re
ti
re
d
fro
m
P
T.
Telk
o
m
In
d
o
n
e
sia
,
h
e
is
n
o
w
d
e
d
ica
ted
h
is
ti
m
e
in
th
e
ARS
(Ad
h
iraja
sa
Re
sw
a
r
a
S
a
n
jay
a
)
Un
iv
e
rsit
y
Ba
n
d
u
n
g
,
In
d
o
n
e
sia
,
a
s
a
lec
tu
re
r
a
n
d
Vic
e
Re
c
to
r
fo
r
Co
ll
a
b
o
ra
ti
o
n
&
In
n
o
v
a
ti
o
n
,
sin
c
e
Oc
to
b
e
r
2
0
1
9
.
In
Tel
k
o
m
I
n
d
o
n
e
sia
,
h
e
w
o
rk
e
d
sin
c
e
1
9
8
8
in
v
a
ri
o
u
s
d
i
v
isio
n
s
e
.
g
.
,
sa
telli
te
d
e
v
e
l
o
p
m
e
n
t,
n
e
two
rk
o
p
e
ra
ti
o
n
,
R&
D,
a
n
d
Di
g
it
a
l
Bu
sin
e
ss
.
He
re
c
e
iv
e
d
th
e
sa
rjan
a
(
b
a
c
h
e
lo
r’s
d
e
g
re
e
)
in
Tele
c
o
m
m
u
n
ica
ti
o
n
fro
m
I
n
stit
u
t
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n
o
l
o
g
i
Ba
n
d
u
n
g
,
In
d
o
n
e
sia
in
1
9
8
7
.
He
a
lso
g
ra
d
u
a
ted
fro
m
th
e
Un
iv
e
rsit
y
o
f
S
u
rre
y
,
UK
a
n
d
re
c
e
iv
e
d
th
e
M
S
c
in
Te
lem
a
ti
c
s
(1
9
9
4
)
a
n
d
P
h
D
i
n
M
u
lt
ime
d
ia
C
o
m
m
u
n
ica
ti
o
n
(
2
0
0
2
).
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wa
s
a
lso
i
n
c
h
a
rg
e
in
se
v
e
ra
l
p
ro
fe
ss
io
n
a
l
fo
r
u
m
s,
fo
r
in
sta
n
c
e
th
e
As
ia
P
a
c
ifi
c
Tele
c
o
m
m
u
n
it
y
Wi
re
les
s
F
o
r
u
m
(AWF
)
a
s
Co
n
v
e
rg
e
n
c
e
Wo
r
k
in
g
G
ro
u
p
Ch
a
irma
n
(2
0
0
8
-
2
0
1
1
)
;
in
ITU
-
D as
Vic
e
R
a
p
p
o
rteu
r
(
2
0
0
7
-
2
0
0
9
);
a
s Ch
a
irma
n
(2
0
2
0
,
2
0
2
1
)
a
n
d
Vic
e
Ch
a
ir
(2
0
1
8
-
2
0
1
9
)
o
f
IEE
E
C
o
m
m
u
n
ica
ti
o
n
s
S
o
c
iety
In
d
o
n
e
sia
Ch
a
p
ter;
a
n
d
a
s
Ge
n
e
ra
l
Ch
a
ir
o
f
se
v
e
ra
l
IEE
E
C
o
n
fe
re
n
c
e
s.
He
c
a
n
b
e
c
o
n
tac
ted
a
t
e
m
a
il
:
wise
to
.
a
g
u
n
g
@a
rs.ac
.
id
.
Er
fia
n
J
u
n
ia
n
to
He
is
a
m
e
m
b
e
r
o
f
th
e
F
a
c
u
lt
y
o
f
E
n
g
in
e
e
rin
g
,
m
a
jo
ri
n
g
in
In
fo
rm
a
ti
c
s
En
g
i
n
e
e
rin
g
,
a
t
Ad
h
ir
a
jas
a
Re
sw
a
ra
S
a
n
jay
a
(ARS)
Un
iv
e
rsity
,
a
n
d
a
re
se
a
rc
h
e
r
a
t
ARS
Dig
i
tal
Re
se
a
rc
h
&
I
n
n
o
v
a
ti
o
n
(AD
RI).
He
g
ra
d
u
a
ted
f
ro
m
th
e
c
o
m
p
u
ter
sc
ien
c
e
m
a
ste
r'
s
p
ro
g
ra
m
a
t
Nu
sa
M
a
n
d
iri
Un
iv
e
rsity
Ja
k
a
rta
in
2
0
1
4
.
He
h
a
s
a
u
th
o
re
d
o
r
c
o
-
a
u
t
h
o
re
d
m
o
r
e
th
a
n
3
8
p
u
b
li
c
a
ti
o
n
s,
in
c
l
u
d
i
n
g
2
p
ro
c
e
e
d
in
g
s
a
n
d
3
6
jo
u
rn
a
ls,
wit
h
a
n
h
-
i
n
d
e
x
o
f
1
0
a
n
d
m
o
re
th
a
n
4
5
0
c
it
a
ti
o
n
s.
His
re
se
a
rc
h
in
tere
sts
in
c
l
u
d
e
te
x
t
m
in
i
n
g
,
a
rti
ficia
l
in
telli
g
e
n
c
e
,
a
n
d
c
las
sifica
ti
o
n
.
He
c
a
n
b
e
c
o
n
tac
ted
a
t
e
m
a
il
:
e
rfian
.
e
jn
@a
rs.ac
.
id
.
Evaluation Warning : The document was created with Spire.PDF for Python.
I
n
t J E
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h
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l
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f
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ti
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n
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ste
m
s,
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t
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h
iraja
sa
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sw
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ra
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n
jay
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iv
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n
d
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s
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se
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s
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n
t
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t
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Dig
it
a
l
Re
se
a
rc
h
&
In
n
o
v
a
ti
o
n
(AD
RI).
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re
v
io
u
sly
,
sh
e
p
a
rti
c
ip
a
ted
in
re
se
a
rc
h
u
si
n
g
m
a
c
h
in
e
lea
rn
in
g
m
e
th
o
d
s
a
n
d
th
e
P
y
t
h
o
n
p
r
o
g
ra
m
m
in
g
lan
g
u
a
g
e
.
S
h
e
c
a
n
b
e
c
o
n
tac
ted
v
i
a
e
m
a
il
:
1
6
2
1
3
0
5
6
@a
rs.ac
.
id
.
Ilh
a
m
Ra
c
h
m
a
t
Wib
o
wo
He
is
a
b
a
c
h
e
l
o
r
st
u
d
e
n
t
i
n
t
h
e
F
a
c
u
l
ty
o
f
E
n
g
in
e
e
rin
g
,
m
a
jo
rin
g
i
n
In
f
o
rm
a
ti
o
n
S
y
ste
m
s,
a
t
Ad
h
ira
jas
a
Re
sw
a
ra
S
a
n
jay
a
(ARS)
Un
iv
e
rsity
,
a
n
d
w
o
rk
s
a
s
a
re
se
a
rc
h
a
ss
istan
t
a
t
ARS
Dig
it
a
l
Re
se
a
rc
h
&
In
n
o
v
a
t
io
n
(AD
RI).
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h
a
s
p
a
rti
c
i
p
a
ted
i
n
re
se
a
rc
h
fo
c
u
se
d
o
n
id
e
n
ti
fy
i
n
g
c
a
n
c
e
r
u
sin
g
m
a
c
h
in
e
lea
rn
in
g
m
e
th
o
d
s,
u
ti
li
z
in
g
th
e
P
y
t
h
o
n
p
ro
g
ra
m
m
in
g
lan
g
u
a
g
e
.
H
e
c
a
n
b
e
c
o
n
tac
ted
v
ia em
a
il
:
ih
a
m
wib
o
wo
1
2
5
@g
m
a
il
.
c
o
m
.
Riz
a
l
Ra
c
h
m
a
n
He
stu
d
ied
u
n
d
e
rg
ra
d
u
a
te
a
t
P
a
d
ja
d
jara
n
Un
i
v
e
rsity
fr
o
m
2
0
0
0
t
o
2
0
0
5
,
m
a
jo
ri
n
g
in
M
a
t
h
e
m
a
ti
c
s
with
a
C
o
m
p
u
ter
S
c
ien
c
e
st
u
d
y
p
r
o
g
ra
m
.
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p
u
rsu
e
d
a
m
a
ste
r'
s
in
m
a
n
a
g
e
m
e
n
t
a
t
Bi
n
a
S
a
ra
n
a
In
f
o
rm
a
ti
k
a
Un
iv
e
rsity
fro
m
2
0
1
3
t
o
2
0
1
5
a
n
d
a
m
a
ste
r’s
in
in
f
o
rm
a
ti
o
n
sy
ste
m
s
a
t
S
T
M
IK
LIKM
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Ba
n
d
u
n
g
fr
o
m
2
0
1
9
t
o
2
0
2
1
.
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h
a
s
a
u
th
o
re
d
o
r
c
o
-
a
u
t
h
o
re
d
m
o
re
th
a
n
7
9
p
u
b
li
c
a
ti
o
n
s,
i
n
c
lu
d
i
n
g
2
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ro
c
e
e
d
in
g
s
a
n
d
3
6
j
o
u
rn
a
ls
,
with
a
n
h
-
in
d
e
x
o
f
1
0
a
n
d
m
o
re
t
h
a
n
9
8
6
c
it
a
ti
o
n
s.
His
re
se
a
rc
h
in
tere
sts
in
c
lu
d
e
d
a
ta
m
in
i
n
g
,
a
rti
ficia
l
in
tell
ig
e
n
c
e
,
a
n
d
i
n
f
o
rm
a
ti
o
n
sy
ste
m
s.
He
c
a
n
b
e
c
o
n
tac
ted
v
ia
e
m
a
il
:
riza
lrac
h
m
a
n
@a
rs.ac
.
id
.
Evaluation Warning : The document was created with Spire.PDF for Python.