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Science
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l.
3
6
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1
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20
24
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6
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s
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tific
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ch
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ju
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k
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u
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y
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ca
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s
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ater
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lth
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ates
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e
s
o
f
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tim
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th
an
in
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v
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I
n
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if
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s
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p
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m
ag
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itu
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p
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is
n
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ly
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m
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p
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ir
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a
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er
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s
m
ed
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p
r
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b
lem
an
d
co
m
p
lex
[
1
]
.
T
h
er
e
is
n
o
ac
tiv
e
s
cr
ee
n
in
g
ev
alu
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wh
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c
an
lead
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in
c
r
ea
s
ed
m
o
r
b
id
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m
o
r
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.
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ased
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ate,
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s
b
io
m
ar
k
e
r
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in
d
i
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s
th
at
ca
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b
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in
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b
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in
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test
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as
h
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itiv
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cif
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f
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I
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f
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m
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p
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ar
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p
r
ev
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[
2
]
.
Pre
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an
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s
cr
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in
g
at
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r
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H.
Mo
c
h
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ar
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Saleh
Gen
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r
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wh
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r
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u
g
h
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ten
ata
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ch
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k
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p
s
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wev
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s
till
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o
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tim
al
an
d
h
as
th
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ev
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[
3
]
.
Acc
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to
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s
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also
a
s
p
ec
ialis
t
in
Ob
s
tetr
ics
an
d
Gy
n
ec
o
lo
g
y
,
m
o
s
t
h
ea
lth
wo
r
k
er
s
in
d
ea
lin
g
with
p
r
e
g
n
an
c
y
co
m
p
licatio
n
s
ar
e
s
till
f
o
cu
s
ed
o
n
tr
ea
tm
en
t,
wh
ich
will
d
ef
in
itely
co
s
t
m
o
r
e
t
h
an
p
r
e
v
en
tio
n
i
n
ter
m
s
o
f
f
u
n
d
in
g
[
4
]
.
d
r
.
H.
Mo
ch
An
s
ar
i
Saleh
Gen
er
al
H
o
s
p
ital
B
an
jar
m
asin
as
a
h
o
s
p
ital
o
wn
ed
b
y
th
e
lo
ca
l
g
o
v
er
n
m
en
t
an
d
also
as
a
r
ef
er
r
al
h
o
s
p
ital,
is
a
h
o
s
p
ital
th
at
h
as
a
h
ig
h
in
cid
e
n
ce
o
f
p
r
ee
clam
p
s
ia
.
Acc
o
r
d
in
g
to
a
p
r
elim
in
ar
y
s
tu
d
y
co
n
d
u
cted
d
u
r
i
n
g
th
e
l
as
t
3
(
th
r
ee
)
y
ea
r
s
,
th
e
n
u
m
b
e
r
o
f
ca
s
es
h
as
b
ee
n
in
cr
ea
s
in
g
.
T
h
e
i
n
cid
en
ce
o
f
p
r
ee
clam
p
s
ia
in
2
0
2
0
was
1
6
8
(
5
.
9
2
%)
o
f
3
.
0
0
7
m
o
th
er
s
wh
o
g
av
e
b
ir
th
;
in
2
0
2
1
th
e
r
e
wer
e
1
4
5
(
5
.
1
7
%)
ca
s
es
o
f
2
,
8
0
4
w
o
m
en
wh
o
g
a
v
e
b
ir
th
a
n
d
i
n
cr
ea
s
ed
ag
ain
in
2
0
2
2
as
m
an
y
as
1
7
8
(
9
.
8
2
%)
o
f
1
.
8
1
3
ca
s
es
o
f
m
o
t
h
er
s
g
iv
in
g
b
ir
th
[
5
]
,
[
6
]
.
T
h
ese
d
ata
in
d
icate
th
e
h
ig
h
in
cid
e
n
ce
o
f
p
r
ee
clam
p
s
ia
in
d
r
.
H.
Mo
ch
An
s
ar
i Sale
h
Gen
er
al
Ho
s
p
ital Ban
ja
r
m
asin
ev
er
y
y
ea
r
.
Ar
tific
ial
i
n
tellig
en
ce
ca
n
b
e
ap
p
lied
in
h
ea
lth
ca
r
e
f
o
r
m
o
d
elin
g
,
d
ia
g
n
o
s
is
,
ea
r
ly
d
ete
ctio
n
,
an
d
m
o
n
ito
r
in
g
.
An
o
t
h
er
p
o
ten
tial
d
if
f
icu
lty
is
th
e
n
o
is
y
class
o
f
o
u
tc
o
m
es
d
u
e
to
t
h
e
d
is
ea
s
e
’
s
v
ar
ied
g
en
e
ex
p
r
ess
io
n
s
.
Su
ch
is
s
u
es
ca
n
b
e
s
o
lv
ed
u
s
in
g
m
ac
h
in
e
lea
r
n
in
g
tec
h
n
o
l
o
g
ies
[
7
]
–
[
9
]
.
I
t
h
as
th
e
p
o
ten
tial
t
o
ass
is
t
in
d
ec
is
io
n
-
m
ak
in
g
an
d
im
p
r
o
v
e
m
ed
ical
ca
r
e
.
Ma
ch
i
n
e
lear
n
i
n
g
is
wel
l
-
s
u
ited
f
o
r
p
r
ed
ictiv
e
m
o
d
elin
g
o
f
p
r
eg
n
an
cy
o
u
tco
m
es
[
1
0
]
,
[
1
1
]
.
Ma
ch
in
e
l
ea
r
n
i
n
g
,
also
k
n
o
wn
as
s
u
p
er
v
is
ed
,
s
em
i
-
s
u
p
er
v
is
ed
,
u
n
s
u
p
er
v
is
ed
,
o
r
r
ein
f
o
r
ce
m
en
t
lear
n
in
g
,
is
a
s
u
b
s
et
o
f
ar
tific
ial
in
tellig
en
ce
th
at
i
n
v
o
lv
es
th
e
u
s
e
o
f
alg
o
r
ith
m
s
an
d
co
m
p
u
ter
m
o
d
els
to
ac
h
iev
e
a
ce
r
tain
g
o
al.
I
n
d
ec
is
io
n
-
m
ak
in
g
s
ettin
g
s
,
m
ac
h
in
e
-
lear
n
in
g
alg
o
r
ith
m
s
ar
e
co
m
m
o
n
l
y
u
tili
ze
d
to
ac
h
iev
e
h
ig
h
er
p
r
e
d
ictiv
e
ac
cu
r
ac
y
th
a
n
t
r
ad
itio
n
al
g
en
e
r
alize
d
lin
ea
r
m
o
d
els
[
1
2
]
.
Fu
r
th
er
m
o
r
e,
d
ee
p
lear
n
in
g
is
a
m
ac
h
in
e
le
ar
n
in
g
tech
n
iq
u
e
th
at
e
m
p
lo
y
s
n
eu
r
al
n
etwo
r
k
s
,
s
im
ilar
to
n
eu
r
o
n
s
in
th
e
h
u
m
an
b
r
ain
,
to
e
x
tr
ac
t
m
an
y
lev
els
o
f
d
ata
r
ep
r
esen
tatio
n
f
r
o
m
a
g
iv
en
in
p
u
t
in
o
r
d
er
t
o
s
o
lv
e
a
p
r
o
b
lem
[
1
3
]
.
Pre
ec
lam
p
s
ia
is
s
till
a
s
er
io
u
s
p
r
o
b
lem
t
h
at
n
ee
d
s
to
b
e
h
an
d
led
o
p
tim
ally
.
U
n
til
n
o
w,
th
e
etio
lo
g
y
o
f
p
r
ee
clam
p
s
ia
h
as
n
o
t
b
ee
n
cle
ar
ly
ex
p
lain
ed
[
1
4
]
.
So
th
at,
e
ar
ly
d
iag
n
o
s
is
o
f
p
r
ee
clam
p
s
ia
r
em
ain
s
a
clin
ical
ch
allen
g
e
[
1
5
]
.
R
esear
ch
er
f
o
u
n
d
s
ev
er
al
is
s
u
es
th
a
t
h
av
e
n
o
t
b
ee
n
ad
d
r
ess
ed
in
th
e
s
tu
d
i
es
r
ev
iewe
d
,
wh
ich
in
d
icate
g
ap
s
in
th
e
liter
atu
r
e:
−
First,
b
ased
o
n
th
e
s
tatem
en
t
o
f
o
n
e
o
f
th
e
r
esear
ch
ex
p
e
r
ts
wh
o
is
also
a
s
p
ec
iali
s
t
in
Ob
s
tetr
ics
an
d
Gy
n
ec
o
lo
g
y
th
e
p
ath
o
g
e
n
esis
o
f
p
r
ee
clam
p
s
ia
s
till
n
o
t f
u
lly
ex
p
lain
ed
[
4
]
.
−
Seco
n
d
,
s
o
f
ar
,
ea
r
ly
d
ete
ctio
n
o
f
p
r
ee
clam
p
s
ia
is
d
o
n
e
co
n
v
en
tio
n
ally
th
r
o
u
g
h
an
ten
atal
ca
r
e
ex
am
in
atio
n
s
,
s
o
th
er
e
is
a
n
ee
d
f
o
r
ar
tific
ial
tech
n
o
lo
g
y
s
u
p
p
o
r
t
th
at
ca
n
d
iag
n
o
s
e
t
h
e
in
cid
e
n
ce
o
f
p
r
ee
clam
p
s
ia
[
3
]
.
−
T
h
ir
d
,
ar
tific
ial
in
tellig
en
ce
-
b
ased
tech
n
o
lo
g
y
is
ex
p
ec
ted
to
b
e
ab
le
to
im
p
r
o
v
e
h
ea
lth
s
er
v
ices,
esp
ec
ially
in
p
r
e
g
n
an
c
y
.
T
h
er
ef
o
r
e,
it
i
s
h
o
p
ed
th
at
f
u
r
th
e
r
r
esear
c
h
is
h
ig
h
l
y
r
ec
o
m
m
en
d
ed
to
f
in
d
th
e
m
o
s
t
ef
f
ec
tiv
e
m
o
d
el
in
d
iag
n
o
s
in
g
p
r
eg
n
a
n
cy
s
p
ec
if
ically
p
r
ee
cla
m
p
s
ia
[
1
6
]
.
Acc
o
r
d
in
g
to
th
e
b
ac
k
g
r
o
u
n
d
ab
o
v
e,
it
ca
n
b
e
co
n
clu
d
ed
t
h
a
t
th
e
ca
s
e
o
f
p
r
ee
clam
p
s
ia
s
till
b
ec
o
m
es
th
e
cr
u
cial
ca
s
e
wh
ich
r
eq
u
ir
e
s
th
e
o
p
tim
al
s
o
lu
tio
n
.
R
eg
ar
d
in
g
th
e
p
r
ee
clam
p
s
ia
ch
ec
k
in
g
th
at
is
s
ti
ll
ca
r
r
ied
o
u
t
co
n
v
en
tio
n
ally
,
th
is
cr
ea
te
s
an
im
p
ac
t
o
n
th
e
m
an
a
g
em
e
n
t
o
f
p
r
ee
clam
p
s
ia
wh
ich
is
s
till
lim
ited
to
o
n
ly
th
e
m
ed
icatio
n
t
o
d
ate.
E
v
en
if
th
er
e
ar
e
al
r
ea
d
y
s
ev
e
r
al
s
tu
d
ies
r
elate
d
to
p
r
ee
clam
p
s
ia
u
s
in
g
tech
n
o
l
o
g
ical
ap
p
r
o
ac
h
th
r
o
u
g
h
m
ac
h
in
e
lear
n
in
g
,
th
e
r
e
ar
e
n
o
r
esu
lt
s
th
at
ca
n
b
e
p
r
ev
en
tiv
e
ac
tio
n
s
in
h
an
d
lin
g
p
r
ee
clam
p
s
ia.
Fu
r
th
er
m
o
r
e,
th
e
r
esu
lts
o
f
p
r
ev
io
u
s
s
tu
d
ies
o
n
ly
f
o
cu
s
ed
o
n
th
e
ac
cu
r
ac
y
n
u
m
b
er
o
f
a
m
o
d
el,
s
o
th
er
e
ar
e
s
till
n
ee
d
s
f
o
r
th
e
m
o
d
el
d
ev
elo
p
m
en
t
th
at
is
a
p
p
lied
b
y
p
r
o
v
id
in
g
f
ea
tu
r
es
to
s
h
o
w
th
e
p
r
im
ar
y
ca
u
s
es
o
f
p
r
ee
clam
p
s
ia.
T
h
er
ef
o
r
e,
b
y
th
e
r
esu
lts
o
f
th
is
s
tu
d
y
th
at
co
m
p
ar
e
p
er
f
o
r
m
a
n
ce
m
etr
ic
an
d
f
ea
tu
r
e
o
f
im
p
o
r
tan
ce
,
th
e
r
esear
ch
er
b
eliev
es
th
at
it
ca
n
r
esu
lt
a
n
e
w
f
in
d
in
g
wh
ich
g
iv
es
c
o
n
tr
ib
u
tio
n
to
th
e
h
ea
lth
f
ield
in
p
r
o
v
id
i
n
g
th
e
ea
r
ly
p
r
ed
ictio
n
to
o
l
f
o
r
p
r
ee
clam
p
s
ia
as
th
e
p
r
ev
e
n
tiv
e
s
tep
s
,
as
well
as
t
h
e
s
h
if
t
o
f
co
n
v
en
tio
n
al
m
eth
o
d
to
th
e
t
ec
h
n
o
lo
g
ical
o
n
e.
B
ased
o
n
th
e
in
tr
o
d
u
ctio
n
ab
o
v
e,
r
esear
ch
er
is
in
ter
ested
to
p
r
o
p
o
s
e
m
o
d
el
to
p
r
ed
ict
p
r
ee
clam
p
s
ia
u
s
in
g
m
ac
h
in
e
lea
r
n
i
n
g
ap
p
r
o
ac
h
.
2.
P
RO
P
O
SE
D
M
O
D
E
L
C
o
n
d
u
cted
a
p
r
elim
in
ar
y
s
tu
d
y
u
s
in
g
t
h
e
liter
atu
r
e
r
e
v
iew
o
n
p
r
e
d
ictin
g
t
h
e
in
c
id
en
ce
o
f
p
r
ee
clam
p
s
ia
u
s
in
g
m
ac
h
i
n
e
l
ea
r
n
in
g
.
Fro
m
th
e
r
esu
lts
o
f
t
h
e
liter
atu
r
e
r
ev
iew,
ar
ticles
th
a
t
ar
e
r
ele
v
an
t
to
th
e
r
esear
ch
ar
e
s
elec
ted
,
wh
ich
i
s
th
en
an
al
y
ze
d
o
n
t
h
eo
r
ies
r
e
la
ted
to
p
r
ee
clam
p
s
ia,
g
ap
s
,
p
r
o
b
lem
s
tatem
en
ts
,
m
eth
o
d
s
u
s
ed
,
an
d
r
ec
o
m
m
en
d
atio
n
s
f
o
r
f
u
r
th
er
r
esear
ch
.
B
ased
o
n
th
e
r
esu
lts
o
f
th
e
a
n
aly
s
is
o
f
th
e
a
r
ticle,
it is
th
en
u
s
ed
as th
e
b
asis
f
o
r
p
r
ep
ar
in
g
th
e
b
ac
k
g
r
o
u
n
d
ac
c
o
r
d
in
g
to
th
e
to
p
ic
to
b
e
s
tu
d
ie
d
.
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
6
,
No
.
1
,
Octo
b
er
20
24
:
694
-
7
0
2
696
Fig
u
r
e
1
s
h
o
ws
th
e
wo
r
k
in
g
p
r
o
ce
s
s
au
to
m
o
d
el
o
f
th
e
m
ac
h
in
e
lear
n
in
g
ap
p
r
o
ac
h
.
I
t h
as
s
tag
es
s
u
ch
as
p
r
elim
in
ar
y
s
tu
d
y
,
d
e
v
elo
p
m
en
t
o
f
m
ac
h
in
e
lear
n
in
g
m
o
d
el,
an
d
e
v
alu
atio
n
m
o
d
el
.
T
h
e
p
r
elim
in
ar
y
s
tu
d
y
b
eg
in
s
b
y
ask
in
g
f
o
r
p
er
m
is
s
io
n
to
co
llect
p
r
e
g
n
an
c
y
d
a
ta
a
t
th
e
h
o
s
p
ital.
T
h
e
d
ata
tak
en
wer
e
p
r
ee
clam
p
s
ia
d
ata
as a
ca
s
e
s
am
p
le,
an
d
d
at
a
o
n
p
r
eg
n
a
n
t w
o
m
en
w
h
o
we
r
e
n
o
t p
r
ee
clam
p
s
ia
as a
co
n
tr
o
l sam
p
le.
T
h
e
n
ex
t p
h
ase
is
m
o
d
el
d
ev
el
o
p
m
en
t.
T
h
is
s
tag
e
b
eg
in
s
with
id
en
tify
r
elate
d
to
th
e
alg
o
r
it
h
m
m
o
d
el
u
s
ed
a
n
d
id
e
n
tify
th
e
f
ac
to
r
s
t
h
at
ca
u
s
e
p
r
ee
clam
p
s
ia.
W
e
d
ev
elo
p
ed
a
m
ac
h
in
e
lear
n
in
g
m
o
d
el
t
o
p
r
ed
ict
t
h
e
in
cid
en
ce
o
f
p
r
ee
cla
m
p
s
ia.
T
h
e
d
ata
th
at
h
as
b
ee
n
o
b
tain
ed
is
th
en
an
aly
ze
d
th
r
o
u
g
h
th
r
ee
s
tag
es,
n
am
ely
th
e
d
esig
n
p
r
o
ce
s
s
,
th
e
q
u
alif
icati
o
n
p
r
o
ce
s
s
an
d
th
e
v
alid
atio
n
p
r
o
ce
s
s
.
M
o
d
el
v
alid
atio
n
u
s
i
n
g
m
ac
h
in
e
lear
n
in
g
th
at
b
eg
in
s
with
th
e
s
tag
e
o
f
b
u
ild
in
g
a
m
o
d
el
u
s
in
g
a
u
to
m
o
d
el.
Au
to
m
o
d
el
p
r
o
v
id
es
with
a
s
elec
tio
n
o
f
m
o
d
els
th
at
ar
e
r
elev
an
t
f
o
r
t
h
e
p
r
o
b
lem
[
1
7
]
.
I
f
t
h
er
e
is
n
o
tim
e
co
n
s
tr
ain
t,
t
h
e
b
est
o
p
tio
n
is
p
r
o
b
a
b
ly
to
b
u
ild
all
o
f
th
em
,
an
d
co
m
p
a
r
e
th
eir
p
er
f
o
r
m
a
n
ce
o
n
ce
t
h
e
y
ar
e
f
in
is
h
ed
.
T
y
p
ically
,
we
h
av
e
to
d
ec
i
d
e
o
n
p
r
io
r
ity
lik
e
th
e
ac
c
u
r
ac
y
o
f
t
h
e
f
in
is
h
ed
m
o
d
el
an
d
tim
e
it
tak
es
to
b
u
ild
it.
Au
to
m
o
d
el
h
elp
s
to
ar
r
iv
e
at
a
r
ea
s
o
n
ab
le
co
m
p
r
o
m
is
e.
T
h
e
n
ex
t
s
tep
,
e
n
ter
test
in
g
d
ata
with
p
ar
am
eter
t
h
r
esh
o
l
d
v
alu
e
o
f
all
f
ea
tu
r
es
an
d
b
u
ild
m
o
d
el
alg
o
r
ith
m
.
Af
ter
th
is
s
tag
e
is
co
m
p
lete
th
e
alg
o
r
ith
m
will
g
et
th
e
o
u
tp
u
t
o
f
th
e
b
est
m
o
d
e
l,
an
d
th
e
last
to
th
e
p
r
ep
r
o
ce
s
s
in
g
s
tag
e
o
f
th
e
m
o
d
el
to
ev
alu
atio
n
.
T
h
e
n
ex
t
p
h
ase
af
ter
v
alid
atio
n
is
m
o
d
el
e
v
alu
atio
n
.
E
v
alu
ate
th
e
m
o
d
el
b
y
lo
o
k
in
g
at
th
e
lev
el
o
f
ac
cu
r
ac
y
,
s
en
s
itiv
ity
,
an
d
s
p
ec
if
icatio
n
s
.
Fro
m
th
ese
r
esu
lts
it
ca
n
b
e
s
ee
n
wh
ich
m
o
d
el
is
th
e
b
est
ca
n
b
e
u
s
ed
in
p
r
ed
ictin
g
th
e
in
cid
en
ce
o
f
p
r
ee
clam
p
s
ia
.
T
h
e
Fi
g
u
r
e
1
b
elo
w
is
th
e
wo
r
k
in
g
p
r
o
ce
s
s
o
f
th
e
m
ac
h
in
e
lear
n
in
g
m
eth
o
d
s
y
s
tem
u
s
in
g
au
to
m
o
d
el
alg
o
r
ith
m
in
p
r
ed
ictin
g
th
e
in
cid
en
ce
o
f
p
r
ee
clam
p
s
i
a.
Fig
u
r
e
1
.
W
o
r
k
i
n
g
p
r
o
ce
s
s
au
t
o
m
o
d
el
3.
M
E
T
H
O
D
C
u
r
r
en
t
m
ac
h
in
e
lear
n
in
g
tech
n
iq
u
es
h
av
e
b
ee
n
ex
ten
s
iv
ely
d
ev
elo
p
ed
to
s
o
lv
e
d
if
f
er
en
tial
eq
u
atio
n
s
,
p
ar
ticu
lar
l
y
in
th
e
f
ield
o
f
h
ea
lth
ca
r
e
.
Pro
p
o
s
e
d
m
o
d
el
to
p
r
ed
ict
p
r
ee
clam
p
s
ia
u
s
in
g
m
ac
h
in
e
lear
n
in
g
a
p
p
r
o
ac
h
was
d
e
v
elo
p
ed
in
th
r
ee
p
h
ases
,
wh
ich
a
r
e
p
r
elim
in
ar
y
s
tu
d
y
,
s
elec
t
in
p
u
t
an
d
m
o
d
el
ty
p
es,
as we
ll a
s
p
er
f
o
r
m
an
ce
m
et
r
ics.
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
P
r
o
p
o
s
ed
mo
d
el
to
p
r
ed
ict
p
r
e
ec
la
mp
s
ia
u
s
in
g
ma
ch
in
e
….
(
R
a
d
en
To
p
a
n
A
d
itya
R
a
h
ma
n
)
697
3
.
1
.
P
ha
s
e
o
ne:
prelim
ina
ry
s
t
ud
y
T
h
e
p
r
elim
in
ar
y
s
tu
d
y
b
eg
in
s
to
p
r
ep
ar
e
th
e
d
ata
b
y
ch
an
g
in
g
th
e
d
ata
ac
co
r
d
in
g
to
th
e
s
p
ec
if
ied
co
d
in
g
b
ef
o
r
e
b
ein
g
en
ter
e
d
i
n
to
th
e
m
ac
h
in
e
lear
n
i
n
g
m
o
d
el.
T
h
e
f
ir
s
t
tech
n
ical
s
tag
e
th
at
wo
u
ld
b
e
ca
r
r
ied
o
u
t
wer
e
ch
ec
k
i
n
g
an
d
h
an
d
lin
g
m
is
s
in
g
o
r
in
c
o
m
p
lete
d
ata
,
th
en
m
ap
p
in
g
ca
teg
o
r
ical
an
d
n
o
m
in
al
d
ata
in
to
o
r
d
in
al
d
ata.
T
h
e
n
e
x
t
s
tep
was
th
e
p
r
e
-
p
r
o
ce
s
s
in
g
s
tag
e
with
f
ea
tu
r
e
s
ca
lin
g
u
s
in
g
n
o
r
m
aliza
tio
n
a
n
d
s
tan
d
ar
d
izatio
n
tech
n
iq
u
es
.
F
o
r
p
er
m
is
s
io
n
to
c
o
llect
p
r
eg
n
an
cy
d
ata
at
th
e
h
o
s
p
ital
,
t
h
e
en
tire
p
o
p
u
latio
n
in
th
is
s
tu
d
y
was
p
r
eg
n
an
t
wo
m
en
.
Me
an
wh
ile,
th
e
s
am
p
le
w
as
d
iv
id
ed
in
to
2
(
two
)
,
n
am
e
ly
p
r
eg
n
a
n
t
wo
m
en
with
p
r
ee
clam
p
s
ia
an
d
p
r
eg
n
a
n
t
wo
m
en
with
o
u
t
p
r
ee
clam
p
s
ia.
T
h
e
s
am
p
le
was
d
iv
id
ed
in
to
2
(
two
)
,
n
am
e
ly
p
r
eg
n
a
n
t
wo
m
en
with
p
r
ee
clam
p
s
ia
an
d
p
r
eg
n
an
t
wo
m
e
n
with
o
u
t
p
r
ee
clam
p
s
ia.
B
ased
in
r
esu
lt
Au
n
g
et
a
l
[
1
8
]
u
s
in
g
a
co
m
p
ar
is
o
n
o
f
ca
s
e
d
ata
an
d
co
n
tr
o
l
d
ata
o
f
1
:
2
th
e
b
est
r
atio
.
B
ased
o
n
d
at
a
in
m
ed
ical
r
ec
o
r
d
f
r
o
m
a
n
s
ar
i
s
aleh
g
en
er
al
h
o
s
p
ital.
T
h
is
s
tu
d
y
u
s
ed
a
s
am
p
le
o
f
4
9
1
ca
s
es
(
p
r
e
g
n
an
t
wo
m
en
with
p
r
ee
clam
p
s
ia)
an
d
a
co
n
t
r
o
l
s
am
p
le
(
p
r
e
g
n
an
t
w
o
m
en
with
o
u
t
p
r
ee
clam
p
s
ia)
o
f
982,
s
o
th
e
to
tal
s
am
p
le
is
1
.
4
7
3
.
S
am
p
lin
g
tech
n
iq
u
e
u
s
in
g
s
y
s
tem
atic
r
an
d
o
m
s
am
p
lin
g
.
Featu
r
es
u
s
in
g
in
th
is
r
ese
ar
ch
1
4
(
f
o
u
r
teen
)
s
u
ch
as:
m
o
th
er
ag
e,
p
r
o
f
ess
io
n
,
ed
u
ca
tio
n
,
g
r
av
i
d
ity
,
p
ar
ity
,
an
ten
atal
ca
r
e
(
ANC),
p
r
eg
n
a
n
cy
in
ter
v
al,
b
o
d
y
m
ass
in
d
ex
,
h
em
o
g
lo
b
in
,
h
is
to
r
y
o
f
ab
o
r
ti
o
n
,
h
is
to
r
y
o
f
ce
s
ar
ea
n
s
ec
tio
n
(
C
S),
h
is
to
r
y
o
f
d
iab
etes
m
ellitu
s
,
h
is
to
r
y
o
f
h
y
p
e
r
ten
s
io
n
,
a
n
d
h
i
s
to
r
y
o
f
p
r
ee
clam
p
s
ia.
an
d
f
o
r
lab
el
f
ea
tu
r
e
is
p
r
ee
c
lam
p
s
ia.
3
.
2
.
P
ha
s
e
t
w
o
:
s
elec
t
inp
ut
a
nd
m
o
del t
y
pes
T
h
e
n
ex
t
s
tag
e
is
s
elec
t
in
p
u
t
an
d
m
o
d
el
ty
p
es
u
s
in
g
a
u
to
m
o
d
el
.
Sp
lit
d
ata
is
a
v
alid
atio
n
tech
n
iq
u
e
wh
ich
d
iv
id
es
th
e
d
ata
in
to
t
wo
p
ar
ts
r
an
d
o
m
ly
,
s
o
m
e
as
tr
ain
in
g
d
ata
an
d
s
o
m
e
o
th
e
r
s
as
test
in
g
d
ata.
B
y
u
s
in
g
s
p
lit
d
ata
will
b
e
ca
r
r
ied
o
u
t
b
y
tr
ain
i
n
g
tr
ials
b
ased
o
n
a
p
r
ed
ete
r
m
in
ed
s
p
lit
r
at
io
b
ef
o
r
e,
f
o
r
th
en
th
e
r
em
ain
d
er
o
f
th
e
s
p
lit
r
atio
tr
ain
in
g
d
ata
will
b
e
co
n
s
id
er
ed
as
d
a
ta
test
in
g
.
T
r
ain
in
g
d
ata
is
d
ata
th
at
w
ill
b
e
u
s
ed
in
tr
ai
n
in
g
d
o
in
g
lear
n
in
g
wh
ile
d
ata
test
in
g
is
d
at
a
th
at
h
as
n
ev
er
b
ee
n
u
s
ed
as
lear
n
in
g
an
d
will
s
er
v
e
as
d
ata
test
in
g
th
e
tr
u
th
o
r
ac
cu
r
ac
y
o
f
th
e
r
esu
lts
lear
n
in
g
[
1
9
]
.
T
h
e
s
elec
ted
in
p
u
t
ex
p
lain
s
ab
o
u
t
f
ea
tu
r
es
s
elec
tio
n
f
r
o
m
attr
ib
u
tes
wh
ich
u
s
e
in
th
is
r
e
s
ea
r
ch
.
I
n
elig
ib
le
f
ea
tu
r
es
o
n
th
i
s
r
esear
ch
will
b
e
ex
clu
d
e
f
r
o
m
th
e
m
o
d
el.
Af
t
er
th
at
b
u
ild
m
o
d
el
u
s
in
g
au
t
o
m
o
d
el
s
u
ch
as
Naïv
e
B
ay
e
s
(
NB
)
,
g
en
er
alize
d
lin
ier
m
o
d
el
(
GL
M)
,
l
o
g
is
tic
r
eg
r
ess
io
n
(
L
R
)
,
f
ast
lar
g
e
m
ar
g
in
(
FLM
)
,
d
ee
p
lear
n
in
g
,
d
ec
is
io
n
tr
ee
(
DT
)
,
r
an
d
o
m
f
o
r
est (
FR
)
,
g
r
a
d
ien
t b
o
o
s
ted
tr
ee
s
(
GB
T
)
,
an
d
s
u
p
p
o
r
t v
ec
to
r
m
ac
h
in
e
(
SVM)
.
3
.
3
.
P
ha
s
e
t
hree
:
perf
o
rma
nce
m
et
rics
T
h
e
n
ex
t
p
h
ase
af
ter
v
alid
atio
n
wa
s
m
o
d
el
e
v
alu
atio
n
.
T
ab
l
e
1
p
er
f
o
r
m
a
n
ce
m
etr
ics
as
a
e
v
alu
ate
d
th
e
m
o
d
el
b
y
lo
o
k
in
g
at
th
e
le
v
el
o
f
ar
ea
u
n
d
e
r
cu
r
v
e
(
AUC
)
,
ac
cu
r
ac
y
,
s
en
s
itiv
ity
,
an
d
s
p
ec
if
ic
ity
.
Acc
u
r
ac
y
is
a
m
etr
ic
d
escr
ib
es
h
o
w
ac
c
u
r
ately
th
e
m
o
d
el
ca
n
class
if
y
th
e
co
r
r
ec
t
r
atio
(
p
o
s
i
tiv
e
an
d
n
eg
ativ
e)
with
th
e
o
v
er
all
d
ata
.
AUC
is
a
p
r
ed
ictio
n
f
u
n
ctio
n
o
n
ea
c
h
tr
ain
in
g
s
et,
ev
alu
ates
th
e
tr
u
e
p
er
f
o
r
m
an
ce
(
o
r
th
e
tr
u
e
)
p
r
o
b
a
b
ilit
y
o
f
th
e
c
o
r
r
ec
t
r
a
n
k
in
g
o
f
two
r
a
n
d
o
m
ly
s
elec
ted
o
b
s
er
v
atio
n
s
,
wh
e
r
e
o
n
e
is
a
p
o
s
itiv
e
s
am
p
le
an
d
th
e
o
th
er
a
n
eg
ativ
e
s
am
p
le
)
i
n
th
e
ap
p
r
o
p
r
iate
v
alid
atio
n
s
et,
an
d
f
in
all
y
,
tak
es
an
a
b
o
v
e
av
er
ag
e
v
alid
atio
n
s
et.
T
h
e
tr
u
e
v
alu
e
o
f
th
is
tar
g
et
p
ar
am
eter
is
r
a
n
d
o
m
,
as
it
d
ep
en
d
s
o
n
th
e
s
ep
ar
atio
n
o
f
th
e
s
am
p
le
d
ata
in
to
tr
ain
in
g
s
ets
an
d
th
e
f
it
o
f
th
e
co
r
r
esp
o
n
d
in
g
p
r
e
d
ictio
n
f
u
n
ctio
n
.
Sen
s
itiv
ity
(
tr
u
e
p
o
s
itiv
e
r
ate)
is
a
m
etr
i
c
m
ea
s
u
r
es th
e
p
r
o
p
o
r
tio
n
o
f
n
e
g
ativ
es th
at
ar
e
co
r
r
ec
tly
i
d
en
t
if
ied
.
An
d
r
ec
all
o
r
s
p
ec
if
icity
(
tr
u
e
n
eg
ativ
e
r
ate)
is
a
m
etr
ic
m
ea
s
u
r
es
th
e
p
r
o
p
o
r
tio
n
o
f
n
eg
ativ
es
th
at
a
r
e
co
r
r
ec
tly
id
en
tifie
d
as
n
e
g
ativ
e
o
r
h
ad
n
o
c
o
n
d
itio
n
.
Fro
m
th
ese
r
esu
lts
,
it
ca
n
b
e
s
ee
n
wh
ich
m
o
d
el
is
th
e
b
est
ca
n
b
e
u
s
ed
as
th
e
b
est
m
o
d
el
in
p
r
ed
ictin
g
th
e
in
cid
en
ce
o
f
p
r
ee
clam
p
s
ia
[
2
0
]
.
T
ab
le
1
.
Per
f
o
r
m
an
ce
m
etr
ics
A
c
t
u
a
l
l
a
b
e
l
P
r
e
d
i
c
t
e
d
l
a
b
e
l
l
i
n
g
P
r
e
d
i
c
t
e
d
l
a
b
e
l
l
i
n
g
P
o
si
t
i
v
e
N
e
g
a
t
i
v
e
P
o
si
t
i
v
e
c
l
a
ss
Tr
u
e
p
o
si
t
i
v
e
(
t
p
)
F
a
l
se
n
e
g
a
t
i
v
e
(
f
n
)
N
e
g
a
t
i
v
e
c
l
a
ss
F
a
l
se
p
o
si
t
i
v
e
(
f
p
)
Tr
u
e
n
e
g
a
t
i
v
e
(
t
n
)
4.
RE
SU
L
T
S AN
D
D
I
SCU
SS
I
O
N
T
h
e
m
o
d
el
d
ev
elo
p
ed
in
th
is
r
esear
ch
u
s
es
an
au
t
o
m
o
d
el
th
at
is
s
im
u
lated
u
s
in
g
a
r
a
p
id
m
in
er
.
T
h
e
m
o
d
el
p
e
r
f
o
r
m
an
ce
r
esu
lts
ar
e
th
en
co
m
p
ar
ed
f
o
r
ea
ch
alg
o
r
ith
m
b
y
lo
o
k
in
g
at
t
h
e
ac
cu
r
ac
y
v
alu
e.
T
h
en
lo
o
k
at
th
e
co
r
r
elatio
n
weig
h
ts
in
th
e
s
elec
ted
alg
o
r
it
h
m
to
d
eter
m
in
e
th
e
im
p
o
r
tan
t
f
ea
tu
r
es
wh
ich
ar
e
im
p
o
r
tan
t f
ea
t
u
r
es f
o
r
th
e
o
cc
u
r
r
en
ce
o
f
p
r
ee
clam
p
s
ia.
4
.
1
.
P
er
f
o
r
m
a
nce
m
e
t
rics
Pre
p
r
o
ce
s
s
in
g
th
e
d
ata
b
ef
o
r
e
an
aly
s
is
is
ca
r
r
ied
o
u
t
to
en
s
u
r
e
th
at
th
er
e
a
r
e
n
o
er
r
o
r
s
in
th
e
d
ata
in
p
u
t
s
o
t
h
at
m
is
s
in
g
d
o
es
n
o
t
o
cc
u
r
d
u
r
i
n
g
a
n
aly
s
is
.
Af
ter
h
an
d
lin
g
th
e
m
is
s
in
g
v
alu
e,
th
e
n
ex
t
s
tep
is
to
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
6
,
No
.
1
,
Octo
b
er
20
24
:
694
-
7
0
2
698
d
iv
id
e
th
e
d
ataset
in
to
8
0
%
f
o
r
test
in
g
d
ata
an
d
2
0
%
f
o
r
tr
a
i
n
in
g
d
ata
[
2
1
]
.
Af
ter
th
at,
ca
r
r
y
o
u
t
an
aly
s
is
with
an
au
to
m
o
d
el
wh
ich
will
b
e
th
e
f
in
al
r
esu
lt
b
y
co
m
p
ar
in
g
th
e
ac
cu
r
ac
y
v
alu
es
f
o
r
ea
ch
alg
o
r
ith
m
.
T
h
e
f
ig
u
r
es
b
el
o
w
ar
e
th
e
m
o
d
elin
g
r
esu
lts
o
f
ea
c
h
alg
o
r
ith
m
an
d
r
u
n
tim
e.
Fig
u
r
e
2
p
r
esen
ts
th
e
to
tal
ti
m
e
n
ee
d
ed
f
o
r
m
o
d
ellin
g
o
n
ea
c
h
alg
o
r
ith
m
.
T
h
e
p
r
o
d
u
ctio
n
o
f
RF
alg
o
r
ith
m
n
ee
d
s
th
e
lo
n
g
es
t
tim
e
am
o
n
g
o
t
h
er
m
o
d
els,
wh
ich
is
1
m
i
n
u
te
1
6
s
ec
o
n
d
s
,
wh
ile
th
e
q
u
ic
k
est p
r
o
d
u
ctio
n
o
f
alg
o
r
ith
m
is
LR
,
wh
ich
is
6
s
ec
o
n
d
s
.
Fig
u
r
e
3
s
h
o
ws
th
e
co
m
p
ar
is
o
n
o
f
m
etr
ic
s
p
er
f
o
r
m
an
ce
r
esu
lts
o
n
ea
ch
alg
o
r
ith
m
.
DT
b
ec
o
m
es
th
e
m
o
d
el
with
th
e
h
ig
h
est
ac
cu
r
a
cy
(
9
2
.
2
%)
an
d
NB
b
ec
o
m
es
t
h
e
m
o
d
el
with
lo
west
ac
cu
r
ac
y
(
8
9
.
5
%).
T
ab
le
2
s
h
o
w
th
e
r
esu
lt
p
er
f
o
r
m
an
ce
m
etr
ic
o
n
th
e
s
elec
ted
m
o
d
els.
B
ased
o
n
th
e
m
o
d
ellin
g
,
we
c
an
s
ee
th
e
DT
h
av
e
th
e
b
est
m
o
d
el
with
ac
cu
r
ac
y
9
2
.
2
%,
AUC
0
.
9
1
,
p
r
ec
is
io
n
9
2
.
3
,
s
en
s
itiv
ity
8
3
.
6
,
s
p
ec
if
icity
9
6
.
5
with
th
e
to
tal
tim
e
7
s
ec
o
n
d
.
Fig
u
r
e
2
.
T
o
tal
t
im
e
Fig
u
r
e
3
.
Acc
u
r
ac
y
T
ab
le
2
.
Per
f
o
r
m
an
ce
m
etr
ics
M
o
d
e
l
A
c
c
AUC
P
r
e
c
S
e
n
s
S
p
e
c
C
l
a
s
si
f
i
c
a
t
i
o
n
e
r
r
o
r
R
u
n
t
i
me
(
sec
o
n
d
)
S
V
M
9
0
.
7
0
.
9
5
8
6
.
9
8
5
.
0
9
3
.
6
9
.
3
1
6
s
G
B
T
9
1
.
2
0
.
9
2
9
0
.
6
8
2
.
1
9
5
.
7
8
.
8
3
4
s
RF
9
1
.
9
0
.
9
3
9
1
.
4
8
3
.
6
9
6
.
1
8
.
1
1
m
i
n
1
6
s
D
e
e
p
l
e
a
r
n
i
n
g
9
0
.
0
0
.
9
2
8
8
.
4
8
0
.
7
9
4
.
6
1
0
.
0
1
1
s
F
LM
9
0
.
7
0
.
9
2
9
2
.
5
7
8
.
6
9
6
.
8
9
.
3
9
s
LR
9
0
.
5
0
.
9
2
9
2
.
4
7
7
.
9
9
6
.
8
9
.
5
6
s
G
LM
9
0
.
5
0
.
9
2
9
2
.
4
7
7
.
9
9
6
.
8
9
.
5
9
s
DT
9
2
.
2
0
.
9
1
9
2
.
3
8
3
.
6
9
6
.
5
9
.
5
7
s
NB
8
9
.
5
0
.
9
2
8
5
.
4
8
2
.
9
9
2
.
9
7
.
8
1
1
s
R
ec
eiv
er
o
p
er
atin
g
ch
a
r
ac
ter
i
s
tics
(
R
OC
)
c
o
m
p
ar
is
o
n
s
h
o
wed
in
Fig
u
r
e
4
b
elo
w
is
th
e
r
esu
lt
o
f
class
if
icatio
n
p
r
o
b
lem
in
d
eter
m
in
in
g
th
r
esh
o
ld
s
in
s
ep
a
r
ate
m
o
d
els.
R
UV
cu
r
v
e
s
h
o
ws
th
e
tr
ad
e
-
o
f
f
b
etwe
en
s
en
s
itiv
ity
(
T
PR
)
an
d
s
p
ec
if
i
city
(
1
-
FP
R
)
.
All
in
all,
th
e
cr
ea
ted
m
o
d
el
g
i
v
es
clo
s
er
cu
r
v
e
to
th
e
to
p
lef
t
co
r
n
er
,
wh
ic
h
m
ea
n
s
th
e
alg
o
r
ith
m
m
o
d
el
s
h
o
ws
th
e
g
o
o
d
p
er
f
o
r
m
a
n
ce
r
an
d
o
m
ly
class
if
ies
th
e
p
o
in
ts
lo
ca
ted
alo
n
g
th
e
d
iag
o
n
al
(
FP
R
=
T
PR
)
.
Ho
wev
er
,
DT
m
o
d
el
s
h
o
w
s
b
etter
p
er
f
o
r
m
an
ce
c
o
m
p
ar
e
d
to
o
th
e
r
m
o
d
els.
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
P
r
o
p
o
s
ed
mo
d
el
to
p
r
ed
ict
p
r
e
ec
la
mp
s
ia
u
s
in
g
ma
ch
in
e
….
(
R
a
d
en
To
p
a
n
A
d
itya
R
a
h
ma
n
)
699
Fig
u
r
e
4
.
R
OC
c
o
m
p
ar
is
o
n
4
.
2
.
F
e
a
t
ures o
f
i
m
po
rt
a
nce
Featu
r
es
o
f
I
m
p
o
r
tan
t
is
an
i
n
d
ex
in
a
d
ata
s
et
th
at
s
h
o
w
s
th
e
r
elativ
e
im
p
o
r
tan
ce
o
f
f
ea
tu
r
es
in
p
r
ed
ictin
g
th
e
tar
g
et
v
ar
ia
b
le,
n
am
ely
Pre
ec
lam
p
s
ia.
T
o
d
et
er
m
in
e
th
e
ac
cu
r
ac
y
o
f
im
p
o
r
tan
t
f
ea
tu
r
es
u
s
in
g
weig
h
t
o
f
co
r
r
elatio
n
m
o
d
el
l
i
n
g
.
T
h
e
Fig
u
r
e
5
s
h
o
ws
th
e
r
esu
lts
o
f
th
e
c
o
r
r
elatio
n
weig
h
ts
s
tar
tin
g
f
r
o
m
th
e
lar
g
est.
Fig
u
r
e
5
.
Featu
r
es o
f
i
m
p
o
r
tan
ce
B
ased
o
n
th
e
tab
le
ab
o
v
e,
we
ca
n
s
ee
th
e
3
(
th
r
ee
)
h
ig
h
est
f
ea
tu
r
es
ca
u
s
es
p
r
ee
clam
p
s
ia
is
h
is
to
r
y
o
f
h
y
p
er
ten
s
io
n
,
h
is
to
r
y
o
f
d
iab
etes
m
ellitu
s
,
an
d
h
is
to
r
y
o
f
p
r
ee
clam
p
s
ia.
Hy
p
er
te
n
s
io
n
c
an
b
e
ca
u
s
ed
b
y
a
v
ar
iety
o
f
in
d
iv
id
u
al
an
d
en
v
ir
o
n
m
e
n
tal
f
ac
to
r
s
,
in
clu
d
in
g
g
en
etics,
an
d
co
n
tin
u
o
u
s
ex
p
o
s
u
r
e
to
s
tr
ess
.
Hy
p
er
ten
s
io
n
is
a
s
ig
n
o
f
p
r
ee
clam
p
s
ia
[2
2
]
.
Hy
p
er
ten
s
io
n
is
clo
s
ely
r
elate
d
to
t
h
e
o
cc
u
r
r
e
n
ce
o
f
p
r
ee
clam
p
s
ia.
Acc
o
r
d
in
g
to
t
h
e
r
esu
lts
o
f
th
e
s
tu
d
y
,
h
y
p
e
r
ten
s
io
n
in
th
e
1
s
t
tr
im
ester
h
as
a
h
ig
h
r
is
k
o
f
p
r
ee
clam
p
s
ia,
esp
ec
ially
in
p
r
im
ig
r
av
id
a
[
23
]
.
Hy
p
e
r
ten
s
io
n
s
u
f
f
er
ed
s
in
ce
b
ef
o
r
e
p
r
eg
n
a
n
cy
h
as
r
esu
lted
in
d
is
r
u
p
tio
n
o
r
d
am
ag
e
to
im
p
o
r
tan
t
o
r
g
a
n
s
.
W
ith
p
r
eg
n
an
c
y
,
th
e
b
o
d
y
will
g
ain
weig
h
t,
r
esu
ltin
g
in
m
o
r
e
s
ev
er
e
d
is
o
r
d
er
s
ag
ai
n
with
th
e
o
n
s
et
o
f
e
d
em
a
an
d
p
r
o
tein
u
r
ia
[
24
]
.
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
6
,
No
.
1
,
Octo
b
er
20
24
:
694
-
7
0
2
700
Pre
g
n
an
cy
d
iab
etes,
ch
r
o
n
ic
r
en
al
il
ln
ess
,
an
d
s
y
s
tem
ic
lu
p
u
s
er
y
th
em
ato
s
u
s
wer
e
all
f
o
u
n
d
to
b
e
h
ig
h
ly
lin
k
ed
to
s
ev
e
r
e
p
r
ee
cl
am
p
s
ia
[
25
]
.
W
o
m
en
with
a
h
is
to
r
y
o
f
p
r
e
-
g
estatio
n
al
d
iab
et
es
h
av
e
m
o
r
e
lik
ely
to
ex
p
er
ie
n
ce
p
r
ee
clam
p
s
ia
co
m
p
ar
ed
to
wo
m
en
with
o
u
t
p
r
e
-
g
estatio
n
al
d
iab
etes
[
26
]
.
W
o
m
e
n
wh
o
ex
p
er
ien
ce
r
esis
tan
ce
in
s
u
lin
b
ef
o
r
e
p
r
eg
n
a
n
cy
ca
n
c
au
s
e
m
ar
k
ed
v
ascu
lar
d
am
ag
e
with
ch
r
o
n
ic
in
f
lam
m
atio
n
,
f
ac
ilit
atin
g
ath
er
o
g
en
ic
an
d
p
r
o
th
r
o
m
b
o
tic
p
r
o
ce
s
s
es
will
af
f
ec
t
n
o
r
m
al
b
lo
o
d
v
ess
els
an
d
n
o
r
m
al
p
lace
n
tatio
n
[
27
]
.
Ha
v
in
g
a
h
is
to
r
y
o
f
p
r
ee
clam
p
s
i
a
is
a
v
er
y
h
ig
h
r
is
k
f
ac
to
r
f
o
r
th
e
r
ec
u
r
r
en
ce
o
f
p
r
ee
clam
p
s
ia
[
28
]
.
Pre
g
n
a
n
t
wo
m
en
with
a
h
is
to
r
y
o
f
p
r
e
v
io
u
s
p
r
ee
clam
p
s
ia
will
h
av
e
an
in
cr
ea
s
ed
r
is
k
o
f
f
u
tu
r
e
p
r
eg
n
an
cies
d
u
e
to
p
r
ee
clam
p
s
ia
is
a
d
is
ea
s
e
th
at
ar
e
a
t
r
is
k
o
f
r
ec
u
r
r
en
ce
[
29
]
.
Pre
g
n
an
t
wo
m
e
n
with
a
h
is
to
r
y
o
f
p
r
ee
clam
p
s
ia
h
a
v
e
an
in
cr
ea
s
ed
r
i
s
k
o
f
ca
r
d
io
v
as
cu
lar
d
is
ea
s
e
(
C
VD
)
an
d
d
em
en
tia
in
s
u
b
s
eq
u
en
t
p
r
eg
n
a
n
cies.
On
e
o
f
th
e
d
is
ea
s
es
in
th
e
C
VD
s
y
s
te
m
is
h
y
p
e
r
ten
s
io
n
wh
ic
h
is
a
s
y
m
p
to
m
o
f
p
r
ee
clam
p
s
ia
[3
0
]
.
5.
CO
NCLU
SI
O
N
T
h
e
p
r
o
p
o
s
ed
m
o
d
el
h
as
b
ee
n
p
r
o
v
en
to
b
e
th
e
s
im
u
latio
n
a
n
d
ex
p
er
im
en
t
wh
ich
ca
n
b
e
u
s
ed
to
test
an
d
v
alid
ate
its
im
p
lem
en
tatio
n
in
p
r
eg
n
an
cy
s
cr
ee
n
in
g
th
r
o
u
g
h
co
r
r
ec
t
f
ea
tu
r
e
s
elec
tio
n
wh
ich
d
ef
in
itely
y
ield
s
co
n
tr
ib
u
tio
n
to
th
e
f
ie
ld
o
f
tech
n
o
lo
g
y
.
T
h
e
f
i
n
d
in
g
s
o
f
th
is
s
tu
d
y
p
o
s
s
ess
s
tr
e
n
g
th
s
co
m
p
ar
e
d
to
p
r
ev
io
u
s
s
tu
d
ies.
T
h
ey
ar
e
co
m
p
leted
with
th
e
f
ea
tu
r
es
o
f
im
p
o
r
tan
ce
th
at
h
as
n
ev
e
r
b
ee
n
ca
r
r
ied
o
u
t
in
th
e
p
r
ev
io
u
s
s
tu
d
ies.
Fu
r
th
er
m
o
r
e,
th
is
s
tu
d
y
also
h
as
its
g
a
p
wh
ich
th
e
r
esu
lts
th
at
ca
n
n
o
t
b
e
g
en
e
r
alize
d
.
T
h
is
ex
p
lain
s
th
e
co
m
p
lex
ity
o
f
tech
n
o
lo
g
y
-
b
ased
h
ea
lth
s
er
v
ice
th
at
h
as
b
ee
n
s
er
v
e
d
in
th
e
h
ea
lth
f
ield
.
Mo
r
eo
v
er
,
th
is
s
u
cc
ess
ev
alu
atio
n
o
f
m
o
d
el
d
ev
el
o
p
m
en
t
p
r
o
v
id
es
im
p
licatio
n
in
th
e
f
ield
o
f
h
ea
lth
t
o
h
elp
h
ea
lth
wo
r
k
er
s
in
ca
r
r
y
in
g
o
u
t
p
r
e
g
n
an
c
y
s
cr
ee
n
i
n
g
,
th
e
tr
u
s
t
o
f
ex
p
ec
ta
n
t
o
n
t
h
e
s
er
v
ice
q
u
ality
g
iv
e
n
s
u
r
ely
af
f
ec
ts
s
o
ciety
to
u
tili
ze
tech
n
o
lo
g
y
-
b
ased
s
er
v
ice
r
ath
e
r
t
h
an
th
e
co
n
v
en
tio
n
al
o
n
e.
Fo
r
f
u
r
th
e
r
r
esear
ch
er
,
it
is
s
u
g
g
ested
to
co
n
d
u
ct
r
es
ea
r
ch
in
th
e
d
if
f
er
e
n
t
ar
ea
s
with
th
e
s
am
e
m
o
d
el
o
f
alg
o
r
it
h
m
an
d
f
ea
tu
r
e
in
o
r
d
er
t
o
p
r
e
d
ict
p
r
ee
clam
p
s
ia
s
o
th
at
th
e
r
esu
lt c
an
b
e
g
en
e
r
alize
d
an
d
th
e
d
is
ea
s
e
will b
e
p
r
ev
en
ted
.
RE
F
E
R
E
NC
E
S
[
1
]
S
.
R
a
n
a
,
E.
Le
m
o
i
n
e
,
J.
P
.
G
r
a
n
g
e
r
,
a
n
d
S
.
A
.
K
a
r
u
m
a
n
c
h
i
,
“
P
r
e
e
c
l
a
m
p
s
i
a
,
”
C
i
rc
.
Re
s
.
,
v
o
l
.
1
2
4
,
n
o
.
7
,
p
p
.
1
0
9
4
–
1
1
1
2
,
M
a
r
.
2
0
1
9
,
d
o
i
:
1
0
.
1
1
6
1
/
C
I
R
C
R
ESA
H
A
.
1
1
8
.
3
1
3
2
7
6
.
[
2
]
M
i
n
i
s
t
r
y
o
f
H
e
a
l
t
h
,
N
a
t
i
o
n
a
l
G
u
i
d
e
l
i
n
e
s
f
o
r
Me
d
i
c
a
l
S
e
r
v
i
c
e
s
f
o
r
t
h
e
M
a
n
a
g
e
m
e
n
t
o
f
Pre
g
n
a
n
c
y
C
o
m
p
l
i
c
a
t
i
o
n
s
,
v
o
l
.
9
1
,
n
o
.
H
K
.
0
1
.
0
7
.
I
n
d
o
n
e
si
a
:
M
i
n
i
st
r
y
o
f
H
e
a
l
t
h
R
e
p
u
b
l
i
k
I
n
d
o
n
e
si
a
,
2
0
1
7
.
[
3
]
E.
R
.
H
i
l
l
e
s
u
n
d
e
t
a
l
.
,
“
P
r
e
e
c
l
a
m
p
s
i
a
a
n
d
g
e
s
t
a
t
i
o
n
a
l
w
e
i
g
h
t
g
a
i
n
i
n
t
h
e
N
o
r
w
e
g
i
a
n
F
i
t
f
o
r
D
e
l
i
v
e
r
y
t
r
i
a
l
N
C
T0
1
0
0
1
6
8
N
C
T
,
”
B
M
C
R
e
s.
N
o
t
e
s
,
v
o
l
.
1
1
,
n
o
.
1
,
p
p
.
1
–
6
,
2
0
1
8
,
d
o
i
:
1
0
.
1
1
8
6
/
s1
3
1
0
4
-
0
1
8
-
3
3
9
6
-
4.
[
4
]
D
.
L.
R
o
l
n
i
k
,
K
.
H
.
N
i
c
o
l
a
i
d
e
s,
a
n
d
L
.
C
.
P
o
o
n
,
“
P
r
e
v
e
n
t
i
o
n
o
f
p
r
e
e
c
l
a
m
p
s
i
a
w
i
t
h
a
s
p
i
r
i
n
,
”
A
m.
J.
O
b
st
e
t
.
G
y
n
e
c
o
l
.
,
v
o
l
.
2
2
6
,
n
o
.
2
,
p
p
.
S
1
1
0
8
–
S
1
1
1
9
,
2
0
2
2
,
d
o
i
:
1
0
.
1
0
1
6
/
j
.
a
j
o
g
.
2
0
2
0
.
0
8
.
0
4
5
.
[
5
]
S
.
Z
h
o
u
e
t
a
l
.
,
“
N
o
n
i
n
v
a
si
v
e
p
r
e
e
c
l
a
mp
si
a
p
r
e
d
i
c
t
i
o
n
u
si
n
g
p
l
a
sma
c
e
l
l
–
f
r
e
e
R
N
A
si
g
n
a
t
u
r
e
s
,
”
A
m.
J
.
O
b
st
e
t
.
G
y
n
e
c
o
l
.
,
v
o
l
.
2
2
9
,
n
o
.
5
,
p
p
.
5
5
3
.
e
1
-
5
5
3
.
e
1
6
,
N
o
v
.
2
0
2
3
,
d
o
i
:
1
0
.
1
0
1
6
/
j
.
a
j
o
g
.
2
0
2
3
.
0
5
.
0
1
5
.
[
6
]
H
e
a
d
o
f
t
h
e
P
r
o
v
i
n
c
i
a
l
H
e
a
l
t
h
S
e
r
v
i
c
e
,
S
o
u
t
h
K
a
l
i
m
a
n
t
a
n
Pr
o
v
i
n
c
e
H
e
a
l
t
h
Pr
o
f
i
l
e
2
0
2
0
.
B
a
n
j
a
r
mas
i
n
:
H
e
a
d
o
f
t
h
e
P
r
o
v
i
n
c
i
a
l
H
e
a
l
t
h
S
e
r
v
i
c
e
,
S
o
u
t
h
B
o
r
n
e
o
,
2
0
2
1.
[
7
]
R
.
T.
R
a
h
ma
n
,
M
.
L
a
k
u
l
u
,
a
n
d
I
.
P
a
n
e
ssai
,
“
A
d
v
a
n
c
i
n
g
P
r
e
e
c
l
a
m
p
s
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n
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[
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1
]
T.
To
mi
m
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T.
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mu
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a
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h
e
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th
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h
e
wa
s
De
p
u
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n
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se
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rc
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n
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v
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ti
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)
a
n
d
c
u
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tl
y
h
e
is
th
e
Dire
c
to
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th
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lt
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u
c
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Un
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sity
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M
o
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h
e
re
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e
d
h
is
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h
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D.
d
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re
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m
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wle
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g
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a
n
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t)
fr
o
m
t
h
e
U
n
iv
e
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tra
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,
i
n
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,
h
is
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.
S
c
.
i
n
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o
f
twa
re
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g
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in
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h
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.
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in
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m
p
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fro
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n
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,
in
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9
9
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His
re
se
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fo
c
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se
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e
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c
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ti
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l
tec
h
n
o
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o
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y
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in
fo
rm
a
ti
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sy
ste
m
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n
d
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rti
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icia
l
i
n
telli
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e
.
His
re
se
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w
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k
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h
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v
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e
n
p
u
b
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sh
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n
jo
u
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l,
b
o
o
k
s a
n
d
c
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re
n
c
e
s.
He
c
a
n
b
e
c
o
n
tac
ted
a
t
e
m
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il
:
m
o
d
i@m
e
ta.u
p
si.ed
u
.
m
y
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Is
m
a
il
Yusu
f
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a
n
e
ss
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i
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e
su
c
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ll
y
stu
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lo
m
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c
o
m
m
u
n
ica
ti
o
n
En
g
in
e
e
rin
g
a
t
Un
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e
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Ha
sa
n
u
d
d
in
,
In
d
o
n
e
sia
.
Ba
c
h
e
lo
r
o
f
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n
d
u
strial
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g
i
n
e
e
rin
g
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t
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Ja
k
a
rta,
In
d
o
n
e
sia
(c
o
m
p
lete
d
2
0
0
5
).
M
a
ste
r
o
f
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c
ien
c
e
in
In
fo
rm
a
ti
o
n
a
n
d
Co
m
m
u
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ica
ti
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n
Tec
h
n
o
lo
g
y
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t
D
e
p
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rtme
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t
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Artifi
c
ial
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telli
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e
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c
e
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h
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ica
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Un
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rsity
o
f
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lay
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lac
c
a
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),
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a
lay
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o
m
p
lete
d
in
Au
g
u
st
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0
1
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),
a
n
d
P
h
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D
.
a
t
De
p
a
rtme
n
t
o
f
Artif
icia
l
In
telli
g
e
n
c
e
in
Un
iv
e
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M
a
lay
a
,
M
a
lay
sia
(c
o
m
p
lete
d
in
F
e
b
r
u
a
ry
2
0
1
3
).
He
fo
ll
o
we
d
t
h
e
p
r
o
fe
ss
io
n
a
l
e
n
g
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n
e
e
r
p
ro
g
ra
m
(In
sin
y
u
r,
Ir.
)
a
t
Un
iv
e
rsitas
An
d
a
las
In
d
o
n
e
sia
a
n
d
c
o
m
p
lete
d
in
2
0
2
1
.
T
h
e
m
a
in
field
o
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se
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stu
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ied
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y
t
h
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lu
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th
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p
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f
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telli
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x
p
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sy
ste
m
s,
g
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ti
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f
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z
y
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w
h
ich
a
re
u
se
d
t
o
so
l
v
e
p
r
o
b
lem
s
in
o
p
e
ra
ti
o
n
s
re
se
a
rc
h
,
tran
sp
o
rtati
o
n
,
c
o
n
tr
o
l
s
y
ste
m
s
,
a
n
d
m
u
lt
i
-
o
b
jec
ti
v
e
o
p
ti
m
iza
ti
o
n
.
H
e
c
a
n
c
o
n
tac
t
a
t
e
m
a
il
:
ism
a
il
y
u
su
f.
p
a
n
e
ss
a
i@y
a
h
o
o
.
c
o
m
.
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
6
,
No
.
1
,
Octo
b
er
20
24
:
694
-
7
0
2
702
Esti
Yua
n
d
a
r
i
s
h
e
is
a
lec
tu
r
e
r
a
t
th
e
F
a
c
u
lt
y
o
f
He
a
lt
h
,
S
a
ri
M
u
li
a
U
n
iv
e
rsit
y
,
S
o
u
t
h
Ka
li
m
a
n
tan
,
In
d
o
n
e
sia
.
Cu
rre
n
tl
y
s
h
e
is
c
o
m
p
leti
n
g
h
is
m
a
ste
r
a
t
Dip
o
n
e
g
o
r
o
Un
iv
e
rsity
,
S
e
m
a
ra
n
g
,
I
n
d
o
n
e
sia
.
He
r
re
se
a
rc
h
a
re
a
s
a
re
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u
b
li
c
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a
lt
h
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les
c
e
n
c
e
,
a
n
d
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a
lt
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o
f
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m
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tea
c
h
e
s
h
e
a
lt
h
p
o
li
c
y
a
d
m
i
n
istratio
n
,
re
se
a
rc
h
o
f
m
e
th
o
d
o
lo
g
y
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a
n
d
se
v
e
ra
l
o
th
e
r
c
o
u
rse
s
re
late
d
to
p
u
b
li
c
h
e
a
lt
h
.
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h
a
s
p
u
b
li
s
h
e
d
b
o
o
k
s
wit
h
IS
BNs
with
ti
tl
e
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se
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rc
h
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e
th
o
d
o
l
o
g
y
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n
d
S
tati
stic
in
2
0
1
7
.
S
h
e
h
a
s
a
lso
re
c
e
iv
e
d
se
v
e
ra
l
re
se
a
rc
h
g
ra
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ts
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ro
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th
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in
istr
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c
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ti
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p
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b
li
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d
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sia
with
ti
tl
e
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ro
b
lem
s
o
f
e
a
rly
m
a
rriag
e
in
tee
n
a
g
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rs
i
n
2
0
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9
,
a
n
d
fa
c
to
rs
c
a
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g
e
a
rly
m
a
rriag
e
in
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0
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0
.
S
h
e
h
a
s
p
r
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d
u
c
e
d
se
v
e
ra
l
stu
d
ies
th
a
t
h
a
v
e
b
e
e
n
p
u
b
li
s
h
e
d
in
re
p
u
tab
le
in
ter
n
a
ti
o
n
a
l
a
n
d
n
a
ti
o
n
a
l
ly
a
c
c
re
d
it
e
d
jo
u
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n
a
ls.
S
h
e
c
a
n
b
e
c
o
n
tac
ted
a
t
e
m
a
il
:
y
u
a
n
d
a
ries
ti
@g
m
a
il
.
c
o
m
.
Ika
Ma
r
d
ia
tu
l
Ulfa
s
h
e
is a l
e
c
tu
re
r
a
t
th
e
Be
tan
g
As
i
Ra
y
a
M
i
d
wife
ry
Ac
a
d
e
m
y
,
P
a
lan
g
k
a
Ra
y
a
,
Ce
n
tral
Ka
li
m
a
n
t
a
n
.
Th
e
m
o
st r
e
c
e
n
t
e
d
u
c
a
ti
o
n
h
e
c
o
m
p
lete
d
wa
s a
M
a
ste
rs i
n
P
u
b
l
ic
He
a
lt
h
in
t
h
e
F
ield
o
f
R
e
p
ro
d
u
c
ti
v
e
He
a
lt
h
a
t
Re
sp
a
ti
I
n
d
o
n
e
sia
Un
i
v
e
rsity
,
Ja
k
a
rta
In
d
o
n
e
sia
.
T
h
e
fiel
d
o
f
sc
ien
c
e
stu
d
ied
is
m
id
wife
r
y
a
n
d
p
re
g
n
a
n
c
y
.
S
h
e
h
a
s
re
c
e
iv
e
d
a
re
se
a
rc
h
g
ra
n
t
fro
m
m
in
istry
o
f
h
ig
h
e
r
e
d
u
c
a
ti
o
n
re
g
a
rd
i
n
g
t
h
e
e
v
a
lu
a
ti
o
n
o
f
t
h
e
u
se
o
f
KIA
b
o
o
k
s
f
o
r
p
re
g
n
a
n
t
wo
m
e
n
in
2
0
1
9
a
n
d
sh
e
h
a
s
p
u
b
li
sh
e
d
a
n
IS
BN
b
o
o
k
a
b
o
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t
m
id
wife
ry
c
a
re
in
p
re
g
n
a
n
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y
i
n
2
0
1
6
,
2
0
2
0
,
a
n
d
2
0
2
2
.
S
h
e
c
a
n
b
e
c
o
n
tac
ted
a
t
e
m
a
il
:
ik
a
m
a
rd
iatu
lu
lfa@g
m
a
il
.
c
o
m
.
Fi
tr
ia
n
i
Nin
g
sih
sh
e
is
a
lec
t
u
re
r
a
t
th
e
Be
tan
g
As
i
Ra
y
a
M
id
wife
ry
Ac
a
d
e
m
y
,
P
a
lan
g
k
a
Ra
y
a
,
Ce
n
tral
Ka
li
m
a
n
t
a
n
.
Th
e
m
o
st r
e
c
e
n
t
e
d
u
c
a
ti
o
n
h
e
c
o
m
p
lete
d
wa
s a
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a
ste
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n
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u
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a
lt
h
in
t
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ield
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c
ti
v
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a
lt
h
a
t
Re
sp
a
ti
I
n
d
o
n
e
sia
Un
i
v
e
rsity
,
Ja
k
a
rta
In
d
o
n
e
sia
.
Th
e
field
o
f
sc
ien
c
e
stu
d
ied
is
m
id
wife
ry
i
n
c
h
il
d
b
ir
th
a
n
d
n
e
w
b
o
r
n
s.
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h
e
h
a
s
re
c
e
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e
d
a
re
se
a
rc
h
g
ra
n
t
fro
m
m
in
istry
o
f
h
ig
h
e
r
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d
u
c
a
ti
o
n
o
n
th
e
e
ffe
c
ti
v
e
n
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ss
o
f
a
c
o
m
b
in
a
ti
o
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o
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o
x
y
to
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n
m
a
ss
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stfe
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g
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g
b
re
a
st
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k
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u
m
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m
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n
in
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n
d
t
h
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lac
tatio
n
m
a
n
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g
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t
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n
ter,
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a
lan
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k
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in
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n
d
h
a
s
p
u
b
l
ish
e
d
a
n
IS
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b
o
o
k
o
n
t
h
e
lac
tatio
n
m
a
n
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g
e
m
e
n
t
c
o
n
tro
l
m
o
d
e
l
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n
2
0
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0
.
S
h
e
c
a
n
b
e
c
o
n
tac
ted
a
t
e
m
a
il
:
fe
g
h
a
n
z
@g
m
a
il
.
c
o
m
.
Le
n
si
Na
ta
li
a
Ta
m
b
u
n
a
n
s
h
e
is
a
lec
tu
re
r
a
t
t
h
e
Be
tan
g
As
i
Ra
y
a
M
id
wife
ry
Ac
a
d
e
m
y
,
P
a
lan
g
k
a
Ra
y
a
,
Ce
n
tral
Ka
li
m
a
n
tan
.
Th
e
m
o
st r
e
c
e
n
t
e
d
u
c
a
ti
o
n
h
e
c
o
m
p
lete
d
wa
s
a
M
a
ste
rs
in
P
u
b
li
c
He
a
lt
h
i
n
th
e
F
ield
o
f
Re
p
ro
d
u
c
ti
v
e
He
a
lt
h
a
t
Re
sp
a
ti
In
d
o
n
e
sia
Un
iv
e
rsity
,
Ja
k
a
rta
In
d
o
n
e
sia
.
T
h
e
field
o
f
sc
i
e
n
c
e
stu
d
ied
is
m
i
d
wife
ry
a
n
d
p
re
g
n
a
n
c
y
.
S
h
e
h
a
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