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4
P
o
stp
a
rt
u
m
d
e
p
re
ss
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n
(
P
P
D)
p
re
se
n
ts
a
sig
n
ifi
c
a
n
t
m
e
n
tal
h
e
a
lt
h
c
h
a
ll
e
n
g
e
fo
r
m
o
t
h
e
rs
fo
ll
o
win
g
c
h
il
d
b
irt
h
.
Wh
il
e
t
h
e
p
re
c
ise
c
a
u
se
o
f
t
h
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ti
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v
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ti
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e
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re
s
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ts
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re
a
v
a
il
a
b
le.
T
h
is
stu
d
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a
ims
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e
m
p
l
o
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se
m
b
le
l
e
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rn
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g
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h
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e
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ti
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c
isio
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ra
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ien
t
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o
o
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n
g
tre
e
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BT),
a
n
d
e
x
trem
e
g
ra
d
ien
t
b
o
o
sti
n
g
(XG
Bo
o
st),
to
p
re
d
ict
th
e
o
c
c
u
r
re
n
c
e
s
o
f
P
P
D
in
t
h
e
Ba
n
jarm
a
sin
,
S
o
u
th
Ka
li
m
a
n
tan
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In
d
o
n
e
sia
.
T
h
e
p
re
d
ictiv
e
m
o
d
e
l
d
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v
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o
p
e
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n
c
o
m
p
a
ss
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s
a
d
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ta
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t
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m
p
risin
g
3
1
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c
o
r
d
s
g
a
th
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fro
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p
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m
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tals,
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o
m
m
u
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e
a
lt
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se
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n
d
m
id
wife
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li
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fe
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s
M
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u
rt
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t
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tu
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ti
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ti
fied
fi
v
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c
ru
c
ial
fa
c
to
rs
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ss
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c
iate
d
with
P
P
D
i
n
c
id
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n
c
e
:
m
a
rit
a
l
sta
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s,
n
u
m
b
e
r
o
f
li
v
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g
c
h
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ld
re
n
,
h
isto
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f
d
e
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re
ss
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,
fe
a
r
o
f
d
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li
v
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ry
,
a
n
d
fa
m
il
y
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lati
o
n
sh
i
p
s.
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e
u
ti
l
iza
ti
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n
se
m
b
le
lea
rn
in
g
stra
teg
ies
fo
r
P
P
D
p
re
d
icti
o
n
y
i
e
ld
s
re
li
a
b
le
o
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tc
o
m
e
s
th
a
t
c
a
n
b
e
a
p
p
li
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d
with
in
c
li
n
ica
l
se
tt
in
g
s.
E
x
p
l
o
ri
n
g
a
lt
e
rn
a
ti
v
e
e
n
se
m
b
le
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rn
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n
g
stra
teg
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su
c
h
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ra
n
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m
f
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n
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sti
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ld
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rt
h
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ti
m
ize
m
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n
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t
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ti
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tu
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a
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K
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s
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ac
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Po
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P
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CC B
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SA
li
c
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se
.
C
o
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r
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s
p
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ing
A
uth
o
r
:
B
ah
b
ib
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m
atu
llah
Facu
lty
o
f
C
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m
p
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tin
g
an
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Me
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T
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n
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lo
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y
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Su
ltan
I
d
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is
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d
u
ca
tio
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Un
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v
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s
ity
Per
ak
,
Ma
lay
s
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E
m
ail: b
ah
b
ib
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m
eta.
u
p
s
i.e
d
u
.
m
y
1.
I
NT
RO
D
UCT
I
O
N
R
ep
r
o
d
u
ctiv
e
h
ea
lth
f
r
e
q
u
en
tly
o
v
er
lo
o
k
s
m
en
tal
h
ea
lth
co
n
s
id
er
atio
n
s
[
1
]
d
esp
ite
o
n
g
o
in
g
s
u
r
v
eillan
ce
r
ev
ea
lin
g
a
s
ig
n
i
f
ican
t
p
r
ev
alen
ce
o
f
m
en
tal
h
ea
lth
is
s
u
es.
T
h
is
m
u
ltifa
ce
te
d
h
ea
lth
co
n
d
itio
n
en
co
m
p
ass
es
a
r
an
g
e
f
r
o
m
o
p
tim
al
well
-
b
ein
g
to
th
e
i
n
itiatio
n
o
f
em
o
tio
n
al
d
is
tr
ess
an
d
p
ain
.
Me
n
tal
h
ea
lth
d
is
o
r
d
er
s
ca
n
in
f
lict
p
r
o
lo
n
g
ed
s
u
f
f
e
r
in
g
o
n
in
d
iv
id
u
als
[
2
]
,
o
cc
u
r
r
in
g
ac
r
o
s
s
v
a
r
io
u
s
en
v
ir
o
n
m
e
n
ts
an
d
af
f
ec
tin
g
an
y
o
n
e,
e
v
en
m
o
t
h
er
s
,
d
u
r
in
g
ch
ild
b
ir
th
.
T
h
e
p
o
s
tp
ar
tu
m
p
h
ase
em
er
g
es a
s
th
e
m
o
s
t p
r
ec
ar
io
u
s
tim
e
f
o
r
b
o
th
th
e
m
o
th
er
an
d
th
e
b
ab
y
,
o
f
ten
o
v
e
r
lo
o
k
e
d
a
n
d
ass
o
ciate
d
with
a
s
ig
n
if
ican
t
n
u
m
b
er
o
f
f
atalities
[
3
]
.
I
t
m
ar
k
s
a
cr
u
cial
p
er
io
d
o
f
m
ater
n
al
tr
an
s
f
o
r
m
atio
n
ch
ar
ac
ter
ized
b
y
p
h
y
s
io
lo
g
ical
an
d
p
s
y
ch
o
lo
g
ical
ch
an
g
es.
Po
s
tp
ar
tu
m
d
e
p
r
ess
io
n
(
PPD
)
s
tan
d
s
o
u
t
as
a
s
ev
er
e
m
en
tal
h
ea
lth
is
s
u
e
[
4
]
,
o
n
e
th
at
ca
n
b
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
7
,
No
.
1
,
J
an
u
ar
y
20
2
5
:
44
3
-
45
1
444
ad
d
r
ess
ed
an
d
tr
ea
ted
y
et
f
r
e
q
u
en
tly
g
o
es
u
n
n
o
ticed
an
d
u
n
d
iag
n
o
s
ed
[
5
]
.
PP
D
is
in
tr
icate
ly
lin
k
ed
to
a
co
m
b
in
atio
n
o
f
p
h
y
s
ical
an
d
e
m
o
tio
n
al
f
ac
to
r
s
.
M
a
c
h
i
n
e
l
e
a
r
n
i
n
g
(
M
L
)
h
as
b
ec
o
m
e
a
n
i
n
t
e
r
e
s
ti
n
g
t
o
p
i
c
i
n
r
es
e
a
r
c
h
r
el
a
t
e
d
t
o
e
a
r
l
y
d
e
t
e
ct
i
o
n
[
6
]
,
[
7
]
.
E
ar
ly
d
etec
tio
n
o
f
d
e
p
r
ess
iv
e
s
y
m
p
to
m
s
ca
n
s
ig
n
if
ican
tly
im
p
r
o
v
e
d
e
p
r
ess
io
n
m
an
ag
e
m
en
t
an
d
r
ed
u
ce
its
n
eg
ativ
e
im
p
ac
ts
[
8
]
.
Ho
wev
er
,
r
o
u
tin
e
s
cr
ee
n
in
g
f
o
r
PP
D
is
o
f
ten
n
eg
lecte
d
d
u
e
to
th
e
n
o
n
-
ec
o
n
o
m
ical
n
atu
r
e
o
f
tr
ad
itio
n
al
an
d
tim
e
-
co
n
s
u
m
in
g
s
elf
-
r
e
p
o
r
t
q
u
esti
o
n
n
air
es.
B
ased
o
n
a
s
y
s
tem
a
tic
liter
atu
r
e
r
ev
iew
an
d
m
eta
-
an
aly
s
is
,
th
e
p
r
ev
ale
n
ce
o
f
PP
D
was
f
o
u
n
d
to
b
e
1
7
.
2
2
%
in
th
e
wo
r
ld
p
o
p
u
latio
n
,
wh
ile
th
e
r
ate
o
f
PP
D
was
m
u
ch
lo
we
r
in
d
e
v
e
lo
p
ed
o
r
h
ig
h
-
in
co
m
e
co
u
n
tr
i
es
an
d
r
eg
io
n
s
[
9
]
.
Me
an
wh
il
e,
in
I
n
d
o
n
esia,
t
h
e
p
r
ev
alen
ce
o
f
p
er
in
atal
m
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n
ta
l
h
ea
lth
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en
ts
is
n
o
t
y
et
k
n
o
wn
[
1
0
]
.
R
esear
ch
b
y
Fazr
an
in
g
ty
as
[
1
1
]
o
n
8
8
p
ar
ticip
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ts
wh
o
wer
e
h
o
s
p
italized
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th
e
Gen
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al
Ho
s
p
ital
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th
e
B
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jar
m
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ev
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th
at
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o
f
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o
s
tp
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tu
m
m
o
t
h
er
s
ex
p
er
ien
c
ed
s
ev
er
e
d
ep
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ess
io
n
.
T
h
is
f
ig
u
r
e
is
co
n
ce
r
n
in
g
,
co
n
s
id
er
in
g
th
er
e
is
n
o
r
eliab
le
p
r
ed
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n
to
o
l f
o
r
d
etec
tin
g
PP
D
in
cid
en
ts
,
wh
ich
im
p
ac
ts
th
e
h
an
d
lin
g
o
f
th
is
ca
s
e.
Stu
d
ies
o
n
p
r
ed
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PP
D
ca
s
es
r
em
ain
lim
ited
a
n
d
h
av
e
n
o
t
y
ield
ed
s
ig
n
i
f
ican
t
r
esu
lts
.
A
s
y
s
tem
atic
liter
atu
r
e
r
ev
ie
w
ap
p
r
o
ac
h
s
h
o
ws
th
at
o
p
ti
m
izin
g
th
e
p
er
f
o
r
m
an
ce
o
f
ML
alg
o
r
ith
m
s
v
ar
ies
d
ep
en
d
i
n
g
o
n
th
e
d
ataset
an
d
th
e
p
r
o
b
lem
to
b
e
ad
d
r
ess
ed
[
1
2
]
,
[
1
3
]
.
I
n
ad
d
itio
n
,
a
s
y
s
tem
atic
liter
atu
r
e
r
ev
iew
s
tu
d
y
r
ev
ea
led
th
at
t
h
e
r
an
d
o
m
f
o
r
est
(
R
F)
alg
o
r
ith
m
co
n
s
is
ten
tly
p
r
o
v
id
e
d
th
e
b
est
p
r
ed
ictio
n
m
o
d
el
p
er
f
o
r
m
an
ce
[
1
2
]
,
with
an
ac
cu
r
ac
y
o
f
7
9
.
1
%
in
a
r
etr
o
s
p
e
ctiv
e
co
h
o
r
t
s
tu
d
y
[
1
4
]
.
T
h
e
e
x
tr
em
e
r
a
n
d
o
m
ized
f
o
r
es
t
(
XR
T
)
[
1
5
]
an
d
lo
g
is
tic
r
eg
r
ess
io
n
(
L
R
)
[
1
6
]
m
o
d
el
s
ac
h
iev
ed
a
7
3
%
ac
cu
r
ac
y
i
n
PP
D
p
r
ed
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n
.
Desp
ite
th
ese
ef
f
o
r
ts
,
t
h
e
ef
f
e
ctiv
en
ess
o
f
PP
D
p
r
e
d
ictio
n
m
o
d
els
r
em
ain
s
s
u
b
o
p
tim
al,
s
u
g
g
esti
n
g
a
n
ee
d
f
o
r
f
u
r
th
er
r
esear
ch
.
E
n
s
em
b
le
lear
n
in
g
,
as
n
o
ted
b
y
R
az
a
et
a
l.
[
1
7
]
,
h
as
s
h
o
wn
im
p
r
o
v
e
d
p
er
f
o
r
m
an
ce
an
d
p
o
ten
tial f
o
r
p
r
ed
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g
PP
D.
T
h
is
s
tu
d
y
in
v
esti
g
ates
th
e
u
s
e
o
f
ar
tific
ial
in
tellig
en
t
(
AI
)
,
p
ar
ticu
lar
ly
ML
a
n
d
e
n
s
em
b
le
lear
n
in
g
,
to
p
r
e
d
ict
PP
D
o
cc
u
r
r
en
ce
an
d
ev
al
u
ate
k
e
y
c
o
n
tr
ib
u
tin
g
f
a
cto
r
s
.
I
t
o
p
tim
izes
th
e
ch
o
s
en
lear
n
in
g
alg
o
r
ith
m
with
v
ar
io
u
s
s
am
p
lin
g
m
eth
o
d
s
an
d
em
p
lo
y
s
f
o
r
war
d
s
elec
tio
n
an
d
b
ac
k
war
d
elim
in
atio
n
f
o
r
f
ea
tu
r
e
s
elec
tio
n
.
An
e
n
s
em
b
le
lear
n
i
n
g
m
o
d
el,
in
clu
d
i
n
g
C
4
.
5
d
ec
i
s
io
n
tr
ee
(
DT
)
,
g
r
a
d
ien
t
b
o
o
s
tin
g
tr
ee
(
GB
T
)
,
an
d
ex
tr
em
e
g
r
ad
ien
t
b
o
o
s
tin
g
(
X
GB
o
o
s
t
)
,
is
ap
p
lied
to
im
p
r
o
v
e
m
o
d
el
p
e
r
f
o
r
m
an
ce
b
y
p
r
o
v
i
d
in
g
s
tab
ilit
y
to
t
h
e
p
r
ed
ictio
n
m
o
d
el.
T
h
e
g
o
al
is
to
en
h
an
ce
h
ea
lth
ca
r
e
d
ev
el
o
p
m
en
t
an
d
p
r
o
v
id
e
ac
tio
n
ab
le
i
n
s
ig
h
ts
f
o
r
clin
ical
s
ettin
g
s
u
s
in
g
AI
.
2.
M
E
T
H
O
D
T
h
e
f
r
a
m
ewo
r
k
illu
s
tr
ated
i
n
Fig
u
r
e
1
d
elin
ea
tes
th
e
c
o
n
s
ec
u
tiv
e
p
h
ases
an
d
s
eq
u
e
n
tial
s
tep
s
em
p
lo
y
ed
in
th
is
r
esear
c
h
.
T
h
e
g
ath
e
r
ed
d
ata
u
n
d
er
g
o
es
p
r
o
ce
s
s
in
g
to
p
r
e
p
ar
e
it
f
o
r
u
tili
za
tio
n
in
M
L
alg
o
r
ith
m
s
.
T
h
e
ML
alg
o
r
i
th
m
s
u
tili
ze
d
in
th
is
s
tu
d
y
en
co
m
p
ass
C
4
.
5
DT
,
GB
T
,
an
d
XGBo
o
s
t.
T
h
e
co
n
s
tr
u
cted
p
r
e
d
ictiv
e
m
o
d
el
in
co
r
p
o
r
ates
d
ata
p
r
o
ce
s
s
in
g
,
s
eg
r
eg
atio
n
,
f
ea
t
u
r
e
s
elec
tio
n
,
an
d
p
r
e
d
ictio
n
alg
o
r
ith
m
s
[
1
8
]
.
T
h
ese
m
o
d
e
ls
ar
e
co
n
f
ig
u
r
ed
to
o
p
tim
ize
th
e
p
er
f
o
r
m
a
n
ce
o
f
ML
alg
o
r
ith
m
s
ef
f
ec
tiv
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u
tili
zin
g
r
esam
p
lin
g
a
n
d
f
ea
t
u
r
e
s
elec
tio
n
m
eth
o
d
o
lo
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ies.
Fig
u
r
e
1
.
T
h
e
f
r
a
m
ewo
r
k
o
f
th
e
s
tu
d
y
2
.
1
.
Da
t
a
c
o
llect
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n
T
h
e
d
ataset
f
o
r
th
is
s
tu
d
y
w
as
co
llected
f
r
o
m
3
1
7
p
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ar
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m
m
o
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s
at
h
o
s
p
itals
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co
m
m
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n
ity
h
ea
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er
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an
d
m
id
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clin
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in
B
an
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m
asin
c
ity
,
u
s
in
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e
s
am
p
lin
g
.
T
h
e
ed
in
b
u
r
g
h
Evaluation Warning : The document was created with Spire.PDF for Python.
I
n
d
o
n
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J
E
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&
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p
Sci
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2
5
0
2
-
4
7
52
A
p
r
ed
ictive
mo
d
el
fo
r
p
o
s
tp
a
r
tu
m
d
ep
r
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n
:
en
s
emb
le
lea
r
n
in
g
…
(
Win
d
a
A
yu
F
a
z
r
a
n
in
g
tya
s
)
445
p
o
s
tn
atal
d
ep
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s
ca
le
(
E
PDS)
an
d
th
e
p
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s
tp
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d
e
p
r
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n
r
is
k
f
ac
to
r
s
(
PP
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F
)
wer
e
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tili
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as
m
ea
s
u
r
em
en
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in
s
tr
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m
e
n
ts
.
T
h
is
q
u
esti
o
n
n
air
e
is
av
ailab
le
in
I
n
d
o
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esian
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with
co
n
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tr
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s
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n
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ir
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ato
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is
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o
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5
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co
m
p
ar
ativ
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f
it
in
d
e
x
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v
alu
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o
f
0
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9
6
3
,
an
d
an
n
o
r
m
ed
f
it
in
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e
x
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o
f
0
.
9
4
2
[
1
9
]
.
T
h
e
E
PDS
is
a
1
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ite
m
q
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n
n
air
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at
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ad
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in
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ter
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cr
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ac
h
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s
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f
r
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m
0
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r
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d
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f
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m
0
to
3
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w
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ile
f
o
r
q
u
esti
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n
s
3
,
5
,
6
,
7
,
8
,
9
,
a
n
d
1
0
,
th
e
s
co
r
in
g
is
f
r
o
m
3
to
0
.
T
h
e
m
ax
im
u
m
s
co
r
e
o
n
th
is
q
u
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n
n
air
e
is
3
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.
Acc
o
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d
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ev
is
et
a
l.
[
2
0
]
,
a
th
r
es
h
o
ld
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alu
e
o
f
1
1
o
r
h
ig
h
er
m
a
y
in
d
icate
wo
m
en
w
h
o
ar
e
at
r
is
k
o
f
PP
D.
2
.
2
.
Da
t
a
prepro
ce
s
s
ing
:
re
s
a
m
pli
ng
m
et
ho
ds
T
h
e
r
esam
p
lin
g
tech
n
i
q
u
e
a
d
d
r
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im
b
alan
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lear
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d
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ta
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y
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if
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in
g
it
to
ac
h
iev
e
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b
alan
ce
d
d
is
tr
ib
u
tio
n
[
2
1
]
.
T
h
is
r
esear
ch
em
p
lo
y
ed
r
a
n
d
o
m
o
v
e
r
s
am
p
lin
g
,
in
cl
u
d
in
g
b
o
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tr
ap
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g
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d
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m
eth
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s
.
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o
o
ts
tr
ap
p
in
g
in
v
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l
v
es selectin
g
n
o
b
s
er
v
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n
s
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o
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ig
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ize
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ep
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en
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e
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itted
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h
e
p
r
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ce
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s
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m
ath
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atica
lly
d
etailed
in
th
e
p
r
o
v
id
ed
:
∗
=
ℎ
{
1
,
2
,
.
.
.
,
}
(
1
)
wh
er
e
∗
r
ep
r
esen
ts
ea
ch
elem
en
t in
th
e
b
o
o
ts
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ap
s
am
p
le
th
at
i
s
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ep
licated
f
r
o
m
th
e
o
r
i
g
in
al
d
ataset.
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E
is
a
d
ata
p
r
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r
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ce
s
s
i
n
g
ap
p
r
o
ac
h
to
tack
le
class
im
b
alan
ce
with
in
a
d
ataset.
T
h
is
tech
n
iq
u
e
in
v
o
lv
es
g
en
e
r
atin
g
n
ew
s
y
n
th
etic
o
b
s
er
v
atio
n
s
d
e
r
iv
ed
f
r
o
m
th
e
ex
is
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g
s
am
p
les
o
f
t
h
e
m
in
o
r
ity
class
.
Gen
er
atin
g
th
ese
s
y
n
th
etic
tr
ain
in
g
r
ec
o
r
d
s
in
v
o
lv
es
r
an
d
o
m
ly
s
elec
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g
o
n
e
o
r
m
o
r
e
K
-
n
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r
est
n
eig
h
b
o
u
r
s
f
o
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ea
ch
in
s
tan
ce
in
th
e
m
in
o
r
ity
class
.
T
h
e
(
2
)
elu
cid
at
es
th
e
f
o
r
m
u
latio
n
o
f
th
is
m
eth
o
d
f
o
r
cr
ea
tin
g
s
y
n
th
etic
s
am
p
les:
∗
=
+
.
(
−
)
(
2
)
T
h
e
f
ac
to
r
λ
,
r
an
g
i
n
g
b
etwe
en
0
an
d
1
,
p
lay
s
a
p
iv
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tal
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le
i
n
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m
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n
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th
e
d
is
tan
ce
b
et
wee
n
an
d
f
o
r
th
e
c
r
ea
tio
n
o
f
s
y
n
th
etic
s
a
m
p
les
.
I
n
th
is
s
tu
d
y
,
t
h
e
n
u
m
b
er
o
f
n
eig
h
b
o
u
r
s
was 5
.
2
.
3
.
F
e
a
t
ure
s
elec
t
io
n
Featu
r
e
s
elec
tio
n
is
a
tech
n
iq
u
e
to
d
i
m
in
is
h
in
p
u
t
v
a
r
iab
les
i
n
a
p
r
ed
ictio
n
m
o
d
el.
I
t
ac
h
ie
v
es
th
is
b
y
m
ax
im
izin
g
th
e
in
clu
s
io
n
o
f
p
er
tin
en
t
d
ata
wh
ile
elim
in
atin
g
n
o
is
e
f
r
o
m
th
e
d
ataset.
T
h
is
au
to
m
atic
p
r
o
ce
s
s
o
p
tim
izes r
elev
an
t f
ea
tu
r
es to
en
h
an
ce
m
o
d
el
p
er
f
o
r
m
a
n
ce
[
2
1
]
.
Fo
r
war
d
s
elec
tio
n
(
FS
)
is
an
iter
ativ
e
p
r
o
ce
s
s
th
at
co
m
m
en
ce
s
b
y
id
en
tify
i
n
g
th
e
f
ea
tu
r
e
with
th
e
h
ig
h
est
p
er
f
o
r
m
an
ce
co
n
ce
r
n
in
g
th
e
tar
g
et
f
ea
tu
r
e.
I
n
s
elec
tin
g
f
ea
tu
r
e
s
u
b
s
ets,
s
t
r
atif
icatio
n
is
im
p
er
ativ
e
to
e
n
s
u
r
e
ad
eq
u
ate
r
ep
r
esen
tatio
n
o
f
ea
ch
class
[
2
2
]
.
B
ac
k
war
d
e
lim
in
atio
n
(
B
E
)
i
s
a
v
alu
ab
le
to
o
l
f
o
r
s
elec
tin
g
p
er
tin
en
t
f
ea
tu
r
es
p
r
io
r
to
en
ter
in
g
th
e
m
o
d
e
l
test
in
g
p
h
ase.
T
h
is
alg
o
r
ith
m
in
itiates
b
y
test
in
g
all
f
ea
tu
r
es
an
d
p
r
o
g
r
ess
iv
ely
elim
i
n
a
tes
n
o
n
-
s
ig
n
if
ica
n
t
f
ea
tu
r
es b
y
c
o
m
p
ar
i
n
g
th
e
r
esu
lts
d
er
iv
ed
f
r
o
m
ea
c
h
co
m
b
in
atio
n
o
f
th
ese
f
ea
t
u
r
es [
2
3
]
,
[
2
4
]
.
2
.
4
.
Cro
s
s
v
a
lid
a
t
i
o
n
C
r
o
s
s
-
v
alid
atio
n
(
C
V)
is
a
m
o
d
el
tr
ain
in
g
ap
p
r
o
ac
h
th
a
t
ev
alu
ates
p
r
ed
ictio
n
ac
cu
r
ac
y
.
T
h
is
tech
n
iq
u
e
is
p
ar
ticu
lar
ly
ad
v
an
tag
eo
u
s
f
o
r
esti
m
atin
g
lo
w
-
b
ias
m
o
d
els,
m
ak
in
g
it
wid
ely
ad
o
p
ted
in
ML
alg
o
r
ith
m
s
[
2
5
]
.
T
h
e
p
r
esen
t
r
esear
ch
em
p
lo
y
s
a
k
-
f
o
ld
C
V
s
tr
ateg
y
,
d
iv
i
d
in
g
th
e
d
at
aset
in
to
k
s
u
b
s
ets.
I
n
itially
,
th
e
s
am
p
le
is
r
an
d
o
m
ly
p
ar
titi
o
n
ed
i
n
to
k
s
u
b
s
a
m
p
les
o
f
eq
u
al
s
ize.
E
ac
h
o
f
th
e
k
s
u
b
s
am
p
les
f
u
n
ctio
n
s
as
v
alid
atio
n
d
ata
e
x
ac
tly
o
n
ce
af
ter
th
e
cr
o
s
s
-
v
a
lid
atio
n
tech
n
i
q
u
e
is
iter
ativ
e
ly
ap
p
lied
k
tim
es
[
2
6
]
.
Fo
r
th
is
s
tu
d
y
,
a
1
0
-
f
o
ld
C
V
was im
p
lem
en
ted
with
s
tr
atif
ied
s
am
p
lin
g
.
2
.
5
.
M
a
chine
lea
rning
a
lg
o
ri
t
hm
s
T
h
e
f
u
n
d
am
e
n
tal
co
n
ce
p
t
b
e
h
i
n
d
en
s
em
b
le
lear
n
in
g
in
v
o
lv
e
s
tr
ain
in
g
m
u
ltip
le
wea
k
class
if
ier
s
with
tr
ain
in
g
d
ata
an
d
th
en
co
m
b
in
in
g
th
ese
wea
k
class
if
ier
s
to
co
n
s
tr
u
ct
a
r
o
b
u
s
t
class
if
ier
[
2
7
]
,
[
2
8
]
.
E
n
s
em
b
le
m
o
d
els
r
ep
r
esen
t
a
p
o
te
n
t
ML
ap
p
r
o
ac
h
th
at
h
as
d
em
o
n
s
tr
ated
tan
g
ib
le
ad
v
an
tag
es
in
v
ar
io
u
s
ap
p
licatio
n
s
[
2
9
]
,
[
3
0
]
.
Gen
er
ally
,
th
e
g
en
er
aliza
tio
n
ab
ilit
y
o
f
en
s
e
m
b
les
s
u
r
p
ass
es
th
at
o
f
ess
e
n
tial
lear
n
er
s
[
3
0
]
.
T
h
is
m
o
d
el
was d
ev
elo
p
ed
u
s
i
n
g
C
4
.
5
DT
,
GB
T
,
an
d
XGBo
o
s
t le
ar
n
in
g
alg
o
r
ith
m
s
.
2
.
5
.
1
.
C4
.
5
decisi
o
n t
re
e
DT
co
n
s
titu
tes
a
s
eq
u
en
tial
m
o
d
el
th
at
e
f
f
icien
tly
a
n
d
co
h
esiv
ely
co
m
b
i
n
es
a
s
er
ies
o
f
b
asic
test
s
,
wh
er
ein
n
u
m
er
ical
f
ea
tu
r
es
ar
e
co
m
p
a
r
ed
with
th
r
esh
o
ld
v
alu
es
in
ea
ch
test
[
3
1
]
.
DT
a
n
aly
s
is
s
er
v
es
as
a
tech
n
iq
u
e
f
o
r
ca
teg
o
r
izin
g
tar
g
et
f
ac
to
r
ca
teg
o
r
ies
b
y
f
o
r
m
u
latin
g
d
ec
is
io
n
-
m
ak
i
n
g
r
u
les
in
th
e
s
tr
u
ctu
r
e
o
f
a
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
7
,
No
.
1
,
J
an
u
ar
y
20
2
5
:
44
3
-
45
1
446
tr
ee
[
3
2
]
.
T
h
is
s
tu
d
y
em
p
l
o
y
e
d
th
e
C
4
.
5
alg
o
r
ith
m
,
wh
ich
r
ep
r
esen
ts
an
ad
v
an
ce
m
en
t
o
v
e
r
th
e
I
D3
alg
o
r
ith
m
.
T
h
e
C
4
.
5
alg
o
r
ith
m
ad
d
r
ess
es th
e
lim
itatio
n
s
in
h
er
en
t in
t
h
e
I
D3
alg
o
r
ith
m
[
3
3
]
.
(
)
=
−
∑
.
2
=
1
(
)
(
,
)
=
(
)
−
∑
|
|
∈
(
)
.
(
)
(
3
)
T
d
en
o
tes
th
e
tr
ain
in
g
ex
am
p
l
e,
c
r
ep
r
esen
ts
th
e
n
u
m
b
er
o
f
class
es,
p
i
is
th
e
p
r
o
p
o
r
tio
n
o
f
ex
am
p
les
in
class
i
,
A
s
ig
n
if
ies th
e
f
ea
tu
r
e,
(
)
en
co
m
p
ass
th
e
p
o
ten
tial v
alu
es o
f
attr
ib
u
te
A
,
d
en
o
tes th
e
s
u
b
s
et
o
f
T
f
o
r
wh
ich
attr
ib
u
te
A
h
as th
e
v
alu
e
υ
,
d
an
|
T
|
in
d
icate
s
th
e
s
ize
o
f
th
e
s
et.
2
.
5
.
2
.
G
ra
dient
bo
o
s
t
ed
t
re
e
T
h
e
GB
T
alg
o
r
ith
m
c
o
m
b
in
e
s
r
eg
r
ess
io
n
a
n
d
class
if
icatio
n
tr
ee
m
o
d
els
s
im
ilar
t
o
DT
to
im
p
r
o
v
e
p
r
ed
ictiv
e
ac
cu
r
ac
y
b
y
iter
ati
v
ely
r
ef
in
i
n
g
esti
m
ates.
I
t
u
s
e
s
a
n
o
n
lin
ea
r
r
eg
r
ess
io
n
ap
p
r
o
ac
h
to
in
c
r
ea
s
e
tr
ee
p
r
ec
is
io
n
an
d
ad
d
r
ess
es
p
r
ed
i
ctio
n
m
o
d
el
is
s
u
es
[
3
4
]
t
h
r
o
u
g
h
an
iter
ativ
e
b
o
o
s
tin
g
p
r
o
ce
s
s
,
d
escr
ib
ed
b
y
th
e
f
o
r
m
u
la
p
r
o
v
id
ed
.
(
)
=
−
1
(
)
+
.
ℎ
(
)
(
4
)
I
n
th
is
f
o
r
m
u
la,
(
)
r
ep
r
esen
ts
th
e
p
r
ed
ictiv
e
f
u
n
ctio
n
af
ter
in
cl
u
d
in
g
th
e
m
th
tr
ee
,
−
1
(
)
r
ep
r
esen
ts
th
e
p
r
ed
ictiv
e
f
u
n
ctio
n
af
ter
in
cl
u
d
in
g
th
e
(
m
-
1)
th
tr
ee
,
ρ
is
t
h
e
lear
n
in
g
r
ate,
wh
ich
is
a
h
y
p
er
p
ar
am
eter
th
at
d
eter
m
in
es
th
e
in
f
lu
en
ce
o
f
e
ac
h
tr
ee
,
an
d
ℎ
d
en
o
tes
th
e
m
th
wea
k
lear
n
er
th
at
is
tr
ain
ed
o
n
th
e
n
eg
ativ
e
g
r
ad
ien
t o
f
th
e
lo
s
s
f
u
n
ctio
n
.
2
.
5
.
3
.
E
x
t
re
m
e
g
ra
dient
bo
o
s
t
ing
XGBo
o
s
t
r
ep
r
esen
ts
a
f
u
s
io
n
o
f
b
a
g
g
in
g
an
d
b
o
o
s
tin
g
alg
o
r
ith
m
s
,
co
m
m
en
ci
n
g
with
co
n
s
tr
u
ctin
g
a
wea
k
er
lear
n
er
m
o
d
el
an
d
p
r
o
g
r
ess
iv
ely
en
h
an
ci
n
g
its
ac
cu
r
ac
y
s
eq
u
en
tially
[
3
5
]
.
T
h
e
i
n
itial
tr
ee
in
XGBo
o
s
t
ten
d
s
to
b
e
wea
k
er
in
class
if
icatio
n
,
em
p
lo
y
in
g
p
r
o
b
ab
ilit
y
in
itializatio
n
.
Su
b
s
eq
u
en
t
weig
h
t
u
p
d
ates
ar
e
ap
p
lied
to
ea
ch
c
o
n
s
tr
u
cted
tr
ee
,
r
esu
ltin
g
in
an
e
n
s
em
b
le
o
f
class
if
icatio
n
tr
ee
s
.
T
h
e
f
o
r
m
u
la
em
p
l
o
y
ed
in
th
is
alg
o
r
ith
m
is
as:
̂
=
∑
(
)
,
∈
=
1
(
5
)
I
n
th
e
g
iv
e
n
co
n
tex
t,
(
)
s
y
m
b
o
lizes
th
e
p
r
ed
icted
v
al
u
e
o
f
t
h
e
t
th
r
esid
u
al
tr
ee
f
o
r
t
h
e
t
th
r
esid
u
al,
wh
er
e
x
i
-
y
i
d
en
o
tes
th
e
p
r
ed
icted
v
alu
e
o
f
th
e
m
o
d
el.
T
h
e
ter
m
f
t
c
o
r
r
esp
o
n
d
s
to
t
h
e
r
esid
u
al
n
u
m
b
er
in
th
e
t
th
r
o
u
n
d
.
Ad
d
itio
n
ally
,
F
r
ep
r
esen
ts
th
e
r
esid
u
al
tr
ee
f
u
n
ctio
n
’
s
v
ac
a
n
t sp
ac
e
[
3
6
]
.
2
.
6
.
E
v
a
lua
t
i
o
n
m
et
rics
E
v
alu
atio
n
in
v
o
lv
es
ass
es
s
in
g
th
e
p
er
f
o
r
m
an
ce
o
f
th
e
co
n
s
tr
u
cted
p
r
ed
ictio
n
m
o
d
el.
I
n
th
i
s
r
esear
ch
,
th
e
ev
alu
atio
n
p
r
o
ce
s
s
d
ep
ic
ted
in
th
e
T
ab
le
1
in
v
o
lv
ed
ex
am
in
in
g
ac
h
iev
em
en
ts
in
ac
cu
r
ac
y
,
r
ec
all,
p
r
ec
is
io
n
,
an
d
s
p
ec
if
icity
.
T
ab
le
1
.
E
v
alu
atio
n
m
et
r
ics o
f
th
e
s
tu
d
y
P
e
r
f
o
r
ma
n
c
e
me
t
r
i
c
s
Eq
u
a
t
i
o
n
N
u
mb
e
r
o
f
e
q
u
a
t
i
o
n
s
A
c
c
u
r
a
c
y
=
+
+
+
+
(
6
)
R
e
c
a
l
l
=
+
(
7
)
P
r
e
c
i
s
i
o
n
=
+
(
8
)
S
p
e
c
i
f
i
c
i
t
y
=
+
(
9
)
3.
RE
SU
L
T
S AN
D
D
I
SCU
SS
I
O
N
T
h
is
s
tu
d
y
co
m
p
ar
es
th
e
ef
f
e
ctiv
en
ess
o
f
th
r
ee
e
n
s
em
b
le
a
lg
o
r
ith
m
s
C
4
.
5
,
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ate
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t,
in
teg
r
ated
with
th
e
r
es
am
p
lin
g
m
eth
o
d
u
tili
zin
g
B
o
o
ts
tr
ap
p
in
g
an
d
SMOT
E
,
y
ield
ed
th
e
h
ig
h
est
p
er
f
o
r
m
an
ce
,
ac
h
iev
in
g
a
n
ac
cu
r
ac
y
s
co
r
e
o
f
8
7
.
5
7
%.
T
h
is
f
ig
u
r
e
s
h
o
ws
a
h
ig
h
er
r
esu
lt
th
an
Ho
ch
m
a
n
et
a
l.
[
3
8
]
in
v
esti
g
ated
XGBo
o
s
t
’
s
ca
p
ab
ilit
y
in
p
r
ed
ictin
g
PP
D
in
cid
en
ce
,
d
ev
is
in
g
a
Q
-
b
ased
m
o
d
el,
an
d
ac
q
u
ir
in
g
an
AUC
v
alu
e
o
f
0
.
7
1
2
in
a
n
atio
n
wid
e
b
ir
th
co
h
o
r
t.
C
o
r
r
esp
o
n
d
in
g
ly
,
Ho
c
h
m
an
e
t
a
l.
[
3
8
]
also
n
o
ted
a
ten
d
en
cy
to
war
d
s
lo
w
p
o
s
itiv
e
p
r
e
d
i
ctiv
e
v
alu
e
(
PP
V)
.
T
h
is
tr
en
d
ar
is
es
f
r
o
m
th
e
d
ataset
’
s
lo
w
in
cid
en
ce
o
f
PP
D.
I
n
o
u
r
r
esear
ch
,
em
p
lo
y
i
n
g
th
e
r
esam
p
lin
g
m
eth
o
d
,
s
p
e
c
i
f
i
c
a
l
l
y
B
o
o
t
s
t
r
a
p
p
i
n
g
a
n
d
S
M
O
T
E
,
s
t
il
l
le
d
t
o
h
i
g
h
e
r
a
c
c
u
r
a
c
y
v
a
l
u
e
s
.
W
h
il
e
o
t
h
e
r
r
e
s
e
a
r
c
h
[
1
4
]
,
[
3
9
]
–
[
4
1
]
s
h
o
ws
R
F
as
th
e
b
e
s
t
alg
o
r
ith
m
in
b
u
ild
in
g
p
r
ed
i
ctio
n
m
o
d
els,
th
is
r
esear
ch
p
r
o
v
id
es
n
ew
in
s
ig
h
ts
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
7
,
No
.
1
,
J
an
u
ar
y
20
2
5
:
44
3
-
45
1
448
in
to
th
e
u
s
e
o
f
e
n
s
em
b
le
l
ea
r
n
in
g
with
XGBo
o
s
t,
wh
ich
also
p
r
o
v
i
d
es
s
atis
f
ac
to
r
y
p
r
ed
ictio
n
m
o
d
el
p
er
f
o
r
m
an
ce
.
T
h
e
ch
allen
g
e
en
c
o
u
n
te
r
ed
in
o
th
er
m
o
d
els
with
in
th
is
s
tu
d
y
is
th
e
p
r
esen
ce
o
f
class
im
b
alan
ce
.
Fo
r
in
s
tan
ce
,
in
Mo
d
el
1
u
tili
zin
g
C
4
.
5
DT
,
a
r
elativ
ely
h
ig
h
ac
cu
r
ac
y
r
ate
o
f
6
8
.
7
7
%
wa
s
ac
h
iev
ed
,
y
et
th
e
p
r
ec
is
io
n
an
d
r
ec
all
v
alu
es
wer
e
n
o
tab
ly
l
o
w
at
4
2
.
0
2
%
an
d
1
6
.
2
5
%,
r
esp
ec
tiv
ely
.
Acc
u
r
a
cy
is
d
ef
in
ed
as
th
e
p
r
o
p
o
r
tio
n
o
f
co
r
r
ec
t
p
r
ed
ict
io
n
s
,
en
co
m
p
ass
in
g
b
o
th
tr
u
e
p
o
s
itiv
es
an
d
n
e
g
ativ
es,
r
elativ
e
to
th
e
to
tal
p
r
ed
ictio
n
s
m
ad
e
.
C
o
n
v
er
s
ely
,
r
ec
all
r
ep
r
esen
ts
th
e
p
er
ce
n
ta
g
e
o
f
ac
tu
al
p
o
s
itiv
e
ca
s
es
co
r
r
ec
tly
id
en
tifie
d
b
y
th
e
alg
o
r
ith
m
,
i
n
d
icatin
g
its
ef
f
ec
tiv
en
ess
in
class
if
y
in
g
p
o
s
itiv
e
ca
s
es.
Ad
d
itio
n
ally
,
p
r
e
cisi
o
n
d
en
o
tes
t
h
e
p
r
o
b
a
b
ilit
y
o
f
ac
cu
r
ately
p
r
ed
ictin
g
p
o
s
itiv
e
class
in
s
tan
ce
s
,
ca
lcu
lated
as
th
e
r
atio
o
f
tr
u
e
p
o
s
itiv
es
to
th
e
s
u
m
o
f
tr
u
e
p
o
s
itiv
es
an
d
n
e
g
ativ
es
[
4
2
]
,
[
4
3
]
.
T
h
is
p
er
f
o
r
m
an
ce
in
d
icate
s
a
f
ailu
r
e
t
o
co
r
r
ec
tly
p
r
ed
ict
in
s
tan
ce
s
o
f
th
e
m
i
n
o
r
ity
clas
s
d
esp
ite
th
e
r
elativ
ely
h
ig
h
a
cc
u
r
ac
y
r
ate
attain
ed
.
Su
c
h
cl
ass
im
b
alan
ce
m
ay
lead
to
class
if
icatio
n
er
r
o
r
s
,
d
im
in
is
h
in
g
th
e
alg
o
r
ith
m
’
s
ac
cu
r
ac
y
.
C
o
n
s
eq
u
e
n
tly
,
f
o
r
a
m
o
r
e
co
m
p
r
eh
en
s
iv
e
ev
alu
atio
n
o
f
m
o
d
el
p
er
f
o
r
m
a
n
ce
,
co
n
s
id
er
i
n
g
all
elem
en
ts
o
f
th
e
co
n
f
u
s
io
n
m
atr
ix
is
im
p
er
ativ
e.
Fig
u
r
e
2
illu
s
tr
ates
th
e
f
ea
t
u
r
es
em
p
lo
y
ed
in
th
is
s
tu
d
y
,
d
e
p
ictin
g
th
e
r
elatio
n
s
h
ip
b
etwe
en
in
d
ep
en
d
en
t
a
n
d
d
ep
e
n
d
en
t
v
a
r
iab
les
u
tili
ze
d
in
th
e
f
ea
tu
r
e
s
elec
tio
n
p
r
o
ce
s
s
th
r
o
u
g
h
FS
a
n
d
BE
.
T
h
is
f
i
g
u
r
e
s
h
o
ws
th
at
FS
ten
d
s
to
y
ield
f
ewe
r
ass
o
ciate
d
v
ar
iab
les
co
m
p
ar
ed
to
BE
.
T
h
is
d
is
cr
ep
a
n
cy
ar
is
es
b
ec
au
s
e
FS
in
itiates
with
ea
ch
f
ea
tu
r
e
an
d
s
u
b
s
eq
u
en
tly
iter
ates
b
y
g
r
ad
u
ally
in
co
r
p
o
r
atin
g
ad
d
itio
n
al
f
ea
tu
r
es,
wh
er
ea
s
,
BE
in
v
o
lv
es
u
tili
zin
g
all
f
e
atu
r
es
in
itially
an
d
th
en
p
r
o
g
r
ess
iv
ely
elim
in
atin
g
th
em
at
ea
ch
iter
atio
n
.
Ultim
ately
,
th
e
f
ig
u
r
e
u
n
d
er
s
co
r
es
m
ar
ital
s
tatu
s
,
n
u
m
b
er
o
f
liv
in
g
ch
ild
r
e
n
,
h
is
to
r
y
o
f
d
ep
r
ess
io
n
,
f
ea
r
o
f
d
eliv
er
y
,
an
d
r
elatio
n
s
h
ip
with
th
e
f
am
ily
as
p
iv
o
tal
f
ac
to
r
s
in
th
is
r
esear
ch
;
ea
ch
wa
s
s
el
ec
ted
f
iv
e
tim
es
in
b
o
th
f
ea
tu
r
e
s
elec
tio
n
m
eth
o
d
s
.
Fig
u
r
e
2
.
C
o
m
p
lete
lis
t o
f
f
ea
tu
r
es selecte
d
b
y
ea
ch
ML
m
et
h
o
d
Me
an
wh
ile,
th
e
f
ea
tu
r
es
co
n
s
i
s
ten
tly
s
elec
ted
in
th
e
f
ea
tu
r
e
s
elec
tio
n
m
eth
o
d
em
p
lo
y
ed
in
th
is
s
tu
d
y
in
clu
d
e
m
ar
ital
s
tatu
s
,
n
u
m
b
er
o
f
liv
in
g
c
h
ild
r
en
,
h
is
to
r
y
o
f
d
ep
r
ess
io
n
,
f
ea
r
o
f
d
eliv
e
r
y
,
a
n
d
r
elatio
n
s
h
i
p
wit
h
f
am
ily
.
I
n
a
m
ar
ital
r
elatio
n
s
h
ip
,
th
e
s
tatu
s
an
d
q
u
ality
o
f
th
e
r
elatio
n
s
h
ip
ar
e
ess
en
tial.
A
h
ig
h
-
q
u
ality
r
elatio
n
s
h
ip
ca
n
o
f
f
er
th
e
m
o
th
er
s
ec
u
r
ity
,
as
th
er
e
will
b
e
n
o
s
u
s
p
icio
n
with
in
h
o
u
s
eh
o
ld
d
y
n
am
ics.
Ho
wev
er
,
p
ar
tn
e
r
s
ca
n
also
b
e
s
o
u
r
ce
s
o
f
s
tr
ess
,
d
ep
en
d
in
g
o
n
th
e
r
elatio
n
s
h
ip
’
s
q
u
ality
.
T
h
er
ef
o
r
e,
f
o
cu
s
in
g
o
n
r
elatio
n
s
h
ip
q
u
ality
r
ath
er
th
an
ju
s
t
s
tatu
s
p
r
o
v
id
es
r
eso
u
r
ce
s
an
d
en
h
an
ce
s
r
esil
ien
ce
[
4
4
]
.
A
d
d
itio
n
ally
,
h
av
in
g
m
o
r
e
ch
ild
r
e
n
ca
n
h
eig
h
ten
th
e
r
is
k
o
f
p
o
s
tn
atal
d
ep
r
ess
io
n
.
Acc
o
r
d
in
g
to
A
n
tcza
k
et
a
l.
[
4
5
]
,
m
o
th
e
r
s
with
m
u
ltip
le
ch
ild
r
e
n
m
a
y
ex
p
er
ien
ce
f
am
ily
c
o
n
f
licts
,
p
ar
ticu
lar
ly
r
e
g
ar
d
i
n
g
c
h
ild
c
ar
e
an
d
ed
u
ca
tio
n
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
A
p
r
ed
ictive
mo
d
el
fo
r
p
o
s
tp
a
r
tu
m
d
ep
r
ess
io
n
:
en
s
emb
le
lea
r
n
in
g
…
(
Win
d
a
A
yu
F
a
z
r
a
n
in
g
tya
s
)
449
r
esp
o
n
s
ib
ilit
ies.
Fu
r
th
er
m
o
r
e,
a
h
is
to
r
y
o
f
d
ep
r
ess
io
n
ex
p
er
ien
ce
d
b
y
th
e
m
o
th
er
b
ef
o
r
e
p
r
eg
n
an
cy
an
d
ch
ild
b
ir
th
also
co
n
tr
ib
u
tes to
t
h
e
lik
elih
o
o
d
o
f
PP
D.
T
h
e
p
r
ed
ictio
n
m
o
d
el
d
e
v
elo
p
ed
in
th
is
s
tu
d
y
h
as
y
ield
e
d
co
m
m
en
d
a
b
le
o
u
tco
m
es,
b
u
t
ce
r
tain
lim
itatio
n
s
p
er
s
is
t.
T
h
e
d
ataset
co
m
p
r
is
es
p
r
im
ar
y
d
ata,
wh
ich
r
em
ain
s
r
elativ
ely
li
m
ited
in
q
u
an
tity
,
s
u
g
g
esti
n
g
th
e
p
o
ten
tial
co
n
s
id
er
atio
n
o
f
in
te
g
r
atin
g
elec
tr
o
n
ic
h
ea
lth
r
ec
o
r
d
s
(
E
HR
)
f
o
r
p
r
ed
ictin
g
PP
D
in
cid
en
ce
.
Alth
o
u
g
h
th
e
p
r
e
d
ictio
n
m
o
d
el
s
h
o
wca
s
ed
p
r
o
m
is
in
g
r
esu
lts
,
ex
p
lo
r
in
g
al
ter
n
ativ
e
en
s
em
b
le
lear
n
in
g
s
tr
ateg
ies,
s
u
ch
as
RF
an
d
a
d
ap
tiv
e
b
o
o
s
tin
g
,
co
u
ld
en
h
an
ce
m
o
d
el
p
er
f
o
r
m
an
ce
.
T
h
is
r
esear
ch
h
o
l
d
s
s
ig
n
if
ican
t
v
alu
e
f
o
r
h
ea
lth
c
ar
e
p
r
o
f
ess
io
n
als
an
d
th
e
c
o
m
m
u
n
ity
,
p
ar
ticu
lar
ly
p
o
s
tp
ar
tu
m
m
o
th
er
s
,
as
im
p
lem
en
tin
g
ML
-
d
r
iv
en
au
t
o
m
atic
PP
D
s
cr
ee
n
in
g
ca
n
s
tr
ea
m
lin
e
h
ea
lth
ass
ess
m
en
t
p
r
o
ce
d
u
r
es,
s
av
in
g
v
alu
ab
le
tim
e.
Fu
r
th
er
m
o
r
e,
p
r
o
m
p
t
i
d
en
tific
atio
n
o
f
PP
D
s
y
m
p
to
m
s
en
ab
les
tim
ely
an
d
ap
p
r
o
p
r
iate
in
ter
v
en
tio
n
s
f
o
r
af
f
ec
ted
m
o
t
h
er
s
.
4.
CO
NCLU
SI
O
N
T
h
e
co
m
b
in
atio
n
o
f
p
h
y
s
ical
an
d
em
o
tio
n
al
e
x
h
au
s
tio
n
a
f
ter
ch
ild
b
ir
th
,
c
o
u
p
le
d
with
h
o
r
m
o
n
al
f
lu
ctu
atio
n
s
,
ca
n
lead
to
m
en
t
al
h
ea
lth
is
s
u
es,
n
o
tab
ly
PP
D.
Sev
er
e
ca
s
es
o
f
PP
D
ca
n
escalate
to
p
o
s
tp
ar
tu
m
p
s
y
ch
o
s
is
,
u
n
d
er
s
co
r
in
g
th
e
cr
itical
n
ee
d
f
o
r
s
wif
t
a
n
d
ac
cu
r
ate
s
cr
ee
n
in
g
m
et
h
o
d
s
f
ac
ilit
ated
b
y
tech
n
o
lo
g
ical
ad
v
an
ce
m
e
n
ts
.
T
h
is
r
esear
ch
em
p
lo
y
s
an
e
n
s
em
b
le
lear
n
in
g
m
o
d
el
in
teg
r
at
ed
with
r
esam
p
lin
g
an
d
f
ea
tu
r
e
s
elec
tio
n
tech
n
iq
u
es
to
d
ev
elo
p
a
m
o
d
el
d
em
o
n
s
tr
atin
g
o
p
tim
al
p
er
f
o
r
m
an
ce
.
T
h
e
m
o
s
t
ef
f
ec
tiv
e
p
r
ed
ictio
n
m
o
d
el
f
o
r
PP
D
is
ac
h
iev
ed
th
r
o
u
g
h
th
e
XGBo
o
s
t
alg
o
r
ith
m
,
in
teg
r
ated
with
B
o
o
ts
tr
ap
p
in
g
an
d
SMOT
E
,
y
ield
in
g
an
ac
cu
r
ac
y
r
ate
o
f
8
7
.
5
7
%
an
d
an
AUC
o
f
8
6
.
7
9
%.
C
o
n
v
er
s
ely
,
th
e
GB
T
alg
o
r
ith
m
ex
h
ib
its
less
r
o
b
u
s
t
p
er
f
o
r
m
a
n
ce
ac
r
o
s
s
all
m
o
d
els,
with
ac
cu
r
ac
y
r
ates
o
f
6
3
.
7
7
%
in
M
o
d
el
1
,
6
9
.
9
5
%
i
n
M
o
d
el
2
,
7
2
.
8
8
%
with
f
o
r
war
d
s
elec
tio
n
,
a
n
d
6
9
.
0
5
%
with
b
ac
k
war
d
elim
in
atio
n
in
M
o
d
el
3
.
Fu
r
th
er
m
o
r
e,
m
ar
ital
s
tatu
s
,
n
u
m
b
e
r
o
f
liv
i
n
g
ch
ild
r
e
n
,
h
is
to
r
y
o
f
d
ep
r
ess
io
n
,
f
ea
r
o
f
d
eliv
er
y
,
an
d
r
elati
o
n
s
h
ip
with
f
am
ily
em
er
g
e
as
in
d
ep
e
n
d
en
t
v
ar
iab
les
co
r
r
elate
d
with
th
e
d
ep
e
n
d
en
t
v
ar
iab
le.
T
h
is
s
tu
d
y
’
s
PP
D
p
r
ed
ictio
n
m
o
d
el
is
p
o
ten
tially
u
s
ef
u
l
in
clin
ical
s
ettin
g
s
.
Nev
er
th
eless
,
we
ass
er
t
th
at
f
u
r
th
er
d
ev
elo
p
m
e
n
t
a
n
d
o
p
tim
izatio
n
o
f
th
is
m
o
d
el
ar
e
f
ea
s
ib
le
th
r
o
u
g
h
u
tili
zin
g
lar
g
er
d
atasets
an
d
ex
p
lo
r
in
g
alter
n
ativ
e
en
s
em
b
le
lear
n
in
g
alg
o
r
ith
m
s
,
as we
ll a
s
v
ar
io
u
s
r
esam
p
lin
g
an
d
f
ea
tu
r
e
s
elec
tio
n
m
eth
o
d
s
.
ACK
NO
WL
E
DG
E
M
E
NT
S
T
h
e
au
th
o
r
s
ex
p
r
ess
h
ea
r
tf
elt
ap
p
r
ec
iatio
n
to
Su
ltan
I
d
r
is
E
d
u
ca
tio
n
Un
iv
er
s
ity
,
Ma
lay
s
ia
,
an
d
Sar
i
Mu
lia
Un
iv
er
s
ity
f
o
r
g
r
an
tin
g
th
e
o
p
p
o
r
tu
n
ity
to
u
n
d
er
tak
e
th
is
r
esear
ch
.
RE
F
E
R
E
NC
E
S
[
1
]
N
.
I
n
san
,
A
.
W
e
k
e
,
S
.
F
o
r
r
e
st
,
a
n
d
J.
R
a
n
k
i
n
,
“
S
o
c
i
a
l
d
e
t
e
r
mi
n
a
n
t
s
o
f
a
n
t
e
n
a
t
a
l
d
e
p
r
e
ss
i
o
n
a
n
d
a
n
x
i
e
t
y
a
m
o
n
g
w
o
me
n
i
n
S
o
u
t
h
A
si
a
:
a
sy
s
t
e
m
a
t
i
c
r
e
v
i
e
w
&a
mp
;
me
t
a
-
a
n
a
l
y
si
s,
”
PLO
S
O
N
E
,
v
o
l
.
1
7
,
n
o
.
2
,
p
.
e
0
2
6
3
7
6
0
,
F
e
b
.
2
0
2
2
,
d
o
i
:
1
0
.
1
3
7
1
/
j
o
u
r
n
a
l
.
p
o
n
e
.
0
2
6
3
7
6
0
.
[
2
]
A
.
F
.
N
a
b
h
a
n
e
t
a
l
.
,
“
W
o
me
n
’
s
r
e
p
r
o
d
u
c
t
i
v
e
s
p
a
n
:
a
s
y
st
e
ma
t
i
c
s
c
o
p
i
n
g
r
e
v
i
e
w
,
”
H
u
m
a
n
Re
p
r
o
d
u
c
t
i
o
n
O
p
e
n
,
v
o
l
.
2
0
2
2
,
n
o
.
2
,
p
p
.
1
–
1
9
,
M
a
r
.
2
0
2
2
,
d
o
i
:
1
0
.
1
0
9
3
/
h
r
o
p
e
n
/
h
o
a
c
0
0
5
.
[
3
]
H
.
B
r
a
d
s
h
a
w
,
J
.
N
.
R
i
d
d
l
e
,
R
.
S
a
l
i
m
g
a
r
a
e
v
,
L
.
Z
h
a
u
n
o
v
a
,
a
n
d
J
.
L
.
P
a
y
n
e
,
“
R
i
s
k
f
a
c
t
o
r
s
a
s
s
o
c
i
a
t
e
d
w
i
t
h
p
o
s
t
p
a
r
t
u
m
d
e
p
r
e
s
s
i
v
e
s
y
m
p
t
o
m
s
:
a
m
u
l
t
i
n
a
t
i
o
n
a
l
s
t
u
d
y
,
”
J
o
u
r
n
a
l
o
f
A
f
f
e
c
t
i
v
e
D
i
s
o
r
d
e
r
s
,
v
o
l
.
3
0
1
,
p
p
.
3
4
5
–
3
5
1
,
M
a
r
.
2
0
2
2
,
d
o
i
:
1
0
.
1
0
1
6
/
j
.
j
a
d
.
2
0
2
1
.
1
2
.
1
2
1
.
[
4
]
J.
S
l
o
mi
a
n
,
G
.
H
o
n
v
o
,
P
.
Em
o
n
t
s,
J.
-
Y
.
R
e
g
i
n
st
e
r
,
a
n
d
O
.
B
r
u
y
è
r
e
,
“
C
o
n
se
q
u
e
n
c
e
s
o
f
ma
t
e
r
n
a
l
p
o
s
t
p
a
r
t
u
m
d
e
p
r
e
s
si
o
n
:
a
sy
st
e
ma
t
i
c
r
e
v
i
e
w
o
f
ma
t
e
r
n
a
l
a
n
d
i
n
f
a
n
t
o
u
t
c
o
mes
,
”
W
o
m
e
n
’
s
H
e
a
l
t
h
,
v
o
l
.
1
5
,
Ja
n
.
2
0
1
9
,
d
o
i
:
1
0
.
1
1
7
7
/
1
7
4
5
5
0
6
5
1
9
8
4
4
0
4
4
.
[
5
]
J.
Ja
y
a
s
e
e
l
a
n
a
n
d
M
p
.
M
o
h
a
n
,
“
C
o
p
i
n
g
st
r
a
t
e
g
i
e
s
u
se
d
b
y
p
o
st
n
a
t
a
l
m
o
t
h
e
r
s
w
i
t
h
p
e
r
c
e
i
v
e
d
s
t
r
e
ss
,
”
I
n
d
i
a
n
J
o
u
r
n
a
l
o
f
Psy
c
h
i
a
t
r
y
,
v
o
l
.
6
2
,
n
o
.
4
,
p
.
4
5
1
,
2
0
2
0
,
d
o
i
:
1
0
.
4
1
0
3
/
p
sy
c
h
i
a
t
r
y
.
I
n
d
i
a
n
JP
s
y
c
h
i
a
t
r
y
_
3
7
3
_
1
9
.
[
6
]
S
.
M.
S
a
mu
r
i
,
T.
V
.
N
o
v
a
,
B
.
R
a
h
m
a
t
u
l
l
a
h
,
S
.
L.
W
a
n
g
,
a
n
d
Z
.
.
A
l
-
Q
a
y
si
,
“
C
l
a
ssi
f
i
c
a
t
i
o
n
m
o
d
e
l
f
o
r
b
r
e
a
s
t
c
a
n
c
e
r
mammo
g
r
a
ms
,
”
I
I
U
M
E
n
g
i
n
e
e
r
i
n
g
J
o
u
rn
a
l
,
v
o
l
.
2
3
,
n
o
.
1
,
p
p
.
1
8
7
–
1
9
9
,
J
a
n
.
2
0
2
2
,
d
o
i
:
1
0
.
3
1
4
3
6
/
i
i
u
me
j
.
v
2
3
i
1
.
1
8
2
5
.
[
7
]
D
.
P
.
S
i
n
a
m
b
e
l
a
,
B
.
R
a
h
ma
t
u
l
l
a
h
,
N
.
H
.
C
.
La
h
,
a
n
d
A
.
W
.
S
e
l
a
m
a
t
,
“
M
a
c
h
i
n
e
l
e
a
r
n
i
n
g
a
p
p
r
o
a
c
h
e
s
f
o
r
p
r
e
d
i
c
t
i
n
g
p
o
st
p
a
r
t
u
m
h
e
m
o
r
r
h
a
g
e
:
a
c
o
mp
r
e
h
e
n
s
i
v
e
s
y
st
e
m
a
t
i
c
l
i
t
e
r
a
t
u
r
e
r
e
v
i
e
w
,
”
I
n
d
o
n
e
si
a
n
J
o
u
rn
a
l
o
f
E
l
e
c
t
ri
c
a
l
E
n
g
i
n
e
e
ri
n
g
a
n
d
C
o
m
p
u
t
e
r
S
c
i
e
n
c
e
(
I
J
EEC
S
)
,
v
o
l
.
3
4
,
n
o
.
3
,
p
p
.
2
0
8
7
–
2
0
9
5
,
J
u
n
.
2
0
2
4
,
d
o
i
:
1
0
.
1
1
5
9
1
/
i
j
e
e
c
s.
v
3
4
.
i
3
.
p
p
2
0
8
7
-
2
0
9
5
.
[
8
]
A
.
A
ma
n
a
t
e
t
a
l
.
,
“
D
e
e
p
l
e
a
r
n
i
n
g
f
o
r
d
e
p
r
e
ss
i
o
n
d
e
t
e
c
t
i
o
n
f
r
o
m
t
e
x
t
u
a
l
d
a
t
a
,
”
El
e
c
t
ro
n
i
c
s
,
v
o
l
.
1
1
,
n
o
.
5
,
p
.
6
7
6
,
F
e
b
.
2
0
2
2
,
d
o
i
:
1
0
.
3
3
9
0
/
e
l
e
c
t
r
o
n
i
c
s
1
1
0
5
0
6
7
6
.
[
9
]
Z.
W
a
n
g
e
t
a
l
.
,
“
M
a
p
p
i
n
g
g
l
o
b
a
l
p
r
e
v
a
l
e
n
c
e
o
f
d
e
p
r
e
s
si
o
n
a
mo
n
g
p
o
st
p
a
r
t
u
m
w
o
me
n
,
”
T
r
a
n
sl
a
t
i
o
n
a
l
Ps
y
c
h
i
a
t
ry
,
v
o
l
.
1
1
,
n
o
.
1
,
p
.
5
4
3
,
O
c
t
.
2
0
2
1
,
d
o
i
:
1
0
.
1
0
3
8
/
s
4
1
3
9
8
-
0
2
1
-
0
1
6
6
3
-
6.
[
1
0
]
S
.
W
.
N
.
A
me
l
i
a
a
n
d
C
.
P
.
K
i
smo
y
o
,
“
H
u
s
b
a
n
d
’
s
r
o
l
e
i
n
p
e
r
i
n
a
t
a
l
d
e
p
r
e
s
si
o
n
d
u
r
i
n
g
t
h
e
n
e
w
n
o
r
m
a
l
p
e
r
i
o
d
o
f
t
h
e
C
O
V
I
D
-
1
9
p
a
n
d
e
mi
c
i
n
S
e
w
o
n
S
u
b
-
D
i
st
r
i
c
t
,
”
Em
b
ri
o
,
v
o
l
.
1
4
,
n
o
.
2
,
p
p
.
2
1
2
–
2
2
0
,
N
o
v
.
2
0
2
2
,
d
o
i
:
1
0
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y
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@
u
n
ism
.
a
c
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i
d
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h
b
ib
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h
m
a
t
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ll
a
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c
u
rre
n
tl
y
a
n
a
ss
o
c
iate
p
r
o
fe
ss
o
r
in
th
e
De
p
a
rtme
n
t
o
f
Co
m
p
u
ti
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g
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F
a
c
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l
ty
o
f
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o
m
p
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ti
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g
a
n
d
M
e
ta
-
Tec
h
n
o
lo
g
y
,
S
u
lt
a
n
Id
ris
E
d
u
c
a
ti
o
n
U
n
iv
e
rsit
y
,
M
a
lay
sia
.
Ha
v
i
n
g
re
c
e
iv
e
d
a
B
.E
n
g
(El
e
c
tri
c
a
l)
fro
m
Va
n
d
e
rb
il
t
Un
iv
e
rsity
,
USA,
a
M
.E
ng
.
S
c
fro
m
M
u
lt
ime
d
ia
U
n
iv
e
rsit
y
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M
a
lay
sia
,
a
n
d
a
D
.
P
h
il
i
n
En
g
.
S
c
i
e
n
c
e
fro
m
t
h
e
Un
i
v
e
rsity
o
f
Ox
fo
rd
,
UK
,
sh
e
is
k
e
e
n
to
a
p
p
ly
th
e
tec
h
n
ica
l
a
n
d
re
se
a
rc
h
sk
il
ls
g
a
in
e
d
t
o
imp
r
o
v
e
th
e
q
u
a
li
ty
o
f
re
se
a
rc
h
a
n
d
e
d
u
c
a
ti
o
n
in
M
a
l
a
y
sia
.
S
h
e
h
a
s
a
u
t
h
o
re
d
a
wi
d
e
r
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g
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o
f
p
u
b
li
c
a
ti
o
n
s
a
n
d
h
a
s
b
e
e
n
in
v
it
e
d
t
o
re
v
iew
a
rti
c
les
fo
r
h
i
g
h
-
im
p
a
c
t
jo
u
rn
a
ls
a
n
d
c
o
n
fe
re
n
c
e
s.
Cu
rre
n
t
re
se
a
rc
h
in
tere
sts
in
c
lu
d
e
ima
g
e
a
n
d
si
g
n
a
l
p
ro
c
e
ss
in
g
,
p
a
tt
e
rn
re
c
o
g
n
it
io
n
,
m
a
c
h
in
e
lea
rn
in
g
,
lea
rn
in
g
a
n
a
ly
ti
c
s,
c
h
il
d
d
e
v
e
lo
p
m
e
n
t,
ICT,
a
n
d
e
d
u
c
a
ti
o
n
.
S
h
e
c
a
n
b
e
c
o
n
tac
ted
a
t
e
m
a
il
:
b
a
h
b
i
b
i@m
e
ta.u
p
si
.
e
d
u
.
m
y
.
H
u
sni
Na
p
a
r
in
g
a
in
e
d
h
is
u
n
d
e
r
g
ra
d
u
a
te
d
e
g
re
e
(S
1
)
fro
m
th
e
De
p
a
rtme
n
t
o
f
Tec
h
n
ica
l
In
f
o
rm
a
ti
o
n
,
Tec
h
n
ica
l
F
a
c
u
lt
y
,
Ka
li
m
a
n
tan
Isla
m
ic
U
n
iv
e
rsit
y
,
S
y
e
k
h
M
u
h
a
m
m
a
d
Ars
y
a
d
Al
-
Ba
n
jar
y
Ba
n
jarm
a
sin
i
n
2
0
1
3
a
n
d
G
ra
d
u
a
te
P
r
o
g
ra
m
(S
2
)
fr
o
m
F
a
c
u
lt
y
o
f
Co
m
p
u
ter
S
c
ien
c
e
(In
telli
g
e
n
c
e
S
y
ste
m
),
Un
iv
e
rsity
o
f
Dia
n
Nu
sw
a
n
t
o
ro
,
S
e
m
a
ra
n
g
,
Ja
wa
Ten
g
a
h
in
2
0
1
5
.
He
g
ra
d
u
a
ted
P
h
.
D.
p
r
o
g
ra
m
in
th
e
F
a
c
u
lt
y
o
f
Co
m
p
u
ti
n
g
a
n
d
M
e
ta
-
Tec
h
n
o
lo
g
y
,
Un
iv
e
rsiti
P
e
n
d
id
i
k
a
n
S
u
l
tan
I
d
ris.
His
m
a
in
re
se
a
rc
h
i
n
tere
st
is
in
f
o
rm
a
ti
o
n
sy
ste
m
,
d
a
ta
m
in
in
g
,
d
a
ta
v
isu
a
li
z
a
ti
o
n
,
a
n
d
m
u
lt
ime
d
ia
.
He
c
a
n
b
e
c
o
n
tac
ted
a
t
e
m
a
il
:
1
n
a
p
a
ri
n
h
u
sn
i@g
m
a
il
.
c
o
m
.
Mo
h
a
m
m
a
d
B
a
sit
c
u
rre
n
tl
y
se
rv
e
s
a
s
a
lec
tu
re
r
in
th
e
Nu
rsin
g
De
p
a
rtme
n
t
with
in
th
e
F
a
c
u
lt
y
o
f
He
a
lt
h
a
t
S
a
ri
M
u
li
a
Un
i
v
e
rsity
,
lo
c
a
ted
i
n
Ba
n
j
a
rm
a
sin
,
S
o
u
t
h
Ka
li
m
a
n
tan
,
In
d
o
n
e
sia
.
His
p
a
ss
io
n
l
ies
in
u
ti
li
z
in
g
h
is
tec
h
n
ica
l
a
n
d
re
se
a
rc
h
e
x
p
e
rti
se
to
e
n
h
a
n
c
e
th
e
q
u
a
li
t
y
o
f
re
se
a
rc
h
a
n
d
e
d
u
c
a
t
io
n
,
p
a
rti
c
u
larl
y
in
Ward
Nu
r
sin
g
M
a
n
a
g
e
m
e
n
t.
He
h
a
s
c
o
n
tri
b
u
ted
sig
n
ifi
c
a
n
tl
y
t
o
t
h
e
fi
e
ld
th
r
o
u
g
h
n
u
m
e
ro
u
s p
u
b
li
c
a
ti
o
n
s
in
n
a
ti
o
n
a
ll
y
a
c
c
re
d
it
e
d
a
n
d
in
tern
a
ti
o
n
a
l
j
o
u
r
n
a
ls
a
n
d
c
o
m
m
u
n
it
y
se
rv
ice
in
it
iati
v
e
s.
Lev
e
ra
g
in
g
h
is
e
x
ten
si
v
e
e
d
u
c
a
ti
o
n
a
l
b
a
c
k
g
ro
u
n
d
,
h
e
a
ls
o
h
o
l
d
s
t
h
e
p
o
siti
o
n
o
f
Ch
a
ir
o
f
th
e
Nu
rsin
g
De
p
a
rtme
n
t
a
t
S
a
ri
M
u
li
a
Un
iv
e
rsity
.
H
e
c
a
n
b
e
c
o
n
tac
ted
a
t
e
m
a
il
:
sy
a
fa
b
a
sit@g
m
a
il
.
c
o
m
.
No
r
As
ia
h
Ra
z
a
k
is
c
u
rre
n
tl
y
a
se
n
io
r
lec
t
u
re
r
in
t
h
e
S
o
f
twa
re
En
g
i
n
e
e
rin
g
a
n
d
S
m
a
rt
Tec
h
n
o
lo
g
y
De
p
a
rtme
n
t
a
t
th
e
F
a
c
u
lt
y
o
f
Co
m
p
u
ti
n
g
a
n
d
M
e
ta
-
Tec
h
n
o
lo
g
y
,
S
u
lt
a
n
I
d
ri
s
Ed
u
c
a
ti
o
n
U
n
iv
e
rsit
y
(UPS
I)
.
S
h
e
e
a
rn
e
d
h
e
r
P
h
.
D
.
in
Ed
u
c
a
ti
o
n
a
l
Tec
h
n
o
lo
g
y
fro
m
Un
iv
e
rsiti
P
u
tra
M
a
lay
sia
in
2
0
1
9
,
fo
c
u
s
in
g
o
n
e
-
lea
rn
i
n
g
.
He
r
c
u
rre
n
t
re
se
a
rc
h
in
tere
sts
in
c
lu
d
e
d
ig
it
a
l
iza
ti
o
n
i
n
e
d
u
c
a
ti
o
n
,
f
o
c
u
s
in
g
o
n
lea
rn
in
g
stra
teg
ies
fo
r
e
n
h
a
n
c
in
g
2
1
st
-
c
e
n
tu
r
y
lea
rn
in
g
sk
il
ls.
S
h
e
a
lso
se
rv
e
s a
s a
r
e
v
iew
e
r
fo
r
n
a
ti
o
n
a
l
a
n
d
i
n
tern
a
ti
o
n
a
l
a
c
a
d
e
m
ic j
o
u
rn
a
ls
.
S
h
e
c
a
n
b
e
c
o
n
tac
ted
at
e
m
a
il
:
n
o
ra
sia
h
.
ra
z
a
k
@m
e
ta
.
u
p
si.ed
u
.
m
y
.
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