I
AE
S
I
nte
rna
t
io
na
l J
o
urna
l o
f
Art
if
icia
l In
t
ellig
ence
(
I
J
-
AI
)
Vo
l.
9
,
No
.
4
,
Dec
em
b
er
2020
,
p
p
.
576
~
5
83
I
SS
N:
2252
-
8938
,
DOI
: 1
0
.
1
1
5
9
1
/i
j
ai.
v
9
.i
4
.
p
p
576
-
5
83
576
J
o
ur
na
l ho
m
ep
a
g
e
:
h
ttp
:
//ij
a
i
.
ia
esco
r
e.
co
m
Preva
lence of
hyp
erte
nsio
n:
predi
ct
iv
e ana
ly
tics re
v
ie
w
Nur
Arif
a
h M
o
hd
No
r
1
,
Azli
na
h M
o
ha
m
ed
2
,
So
f
ia
nita
M
uta
lib
3
1
,3
F
a
c
u
lt
y
o
f
Co
m
p
u
ter an
d
M
a
th
e
m
a
ti
c
a
l
S
c
ien
c
e
s,
Un
iv
e
rsiti
Tek
n
o
l
o
g
i
M
A
RA
(Ui
T
M
)
S
h
a
h
A
la
m
,
M
a
la
y
sia
2
A
d
v
a
n
c
e
d
A
n
a
l
y
ti
c
s E
n
g
in
e
e
rin
g
Ce
n
tre,
F
a
c
u
lt
y
o
f
Co
m
p
u
ter an
d
M
a
th
e
m
a
ti
c
a
l
S
c
ien
c
e
s,
Un
iv
e
rsi
ti
T
e
k
n
o
lo
g
i
M
A
R
A
(UiT
M
)
S
h
a
h
A
la
m
,
M
a
la
y
sia
Art
icle
I
nfo
AB
ST
RAC
T
A
r
ticle
his
to
r
y:
R
ec
eiv
ed
A
p
r
1
6
,
2
0
20
R
ev
i
s
ed
Ju
l
18
,
2
0
20
A
cc
ep
ted
A
ug
2
,
2
0
20
H
y
p
e
rten
sio
n
is
o
n
e
o
f
th
e
n
o
n
-
c
o
m
m
u
n
ica
b
le
d
ise
a
se
(NCD
)
th
a
t
is
c
las
si
fy
a
s
a
g
lo
b
a
l
h
e
a
lt
h
risk
w
it
h
m
a
n
y
c
rit
ica
l
h
e
a
lt
h
c
a
s
e
s.
M
a
la
y
si
a
ra
ise
th
e
sa
m
e
c
o
n
c
e
rn
o
f
th
e
in
c
re
a
sin
g
NCD
h
e
a
lt
h
p
ro
b
lem
.
T
h
is
p
a
p
e
r
a
i
m
s
to
stu
d
y
th
e
tec
h
n
i
q
u
e
s
u
se
d
in
p
re
d
ictiv
e
a
n
a
l
y
ti
c
s
n
a
m
e
l
y
h
e
a
lt
h
c
a
re
a
n
d
id
e
n
ti
f
y
th
e
f
a
c
to
rs
o
f
p
re
v
a
len
c
e
o
n
h
y
p
e
rten
sio
n
.
T
h
is
re
v
ie
w
w
o
u
ld
g
iv
e
a
b
e
tt
e
r
u
n
d
e
rsta
n
d
i
n
g
o
f
p
ro
p
e
r
tec
h
n
iq
u
e
s
a
n
d
su
g
g
e
st
th
e
tec
h
n
iq
u
e
c
o
m
m
o
n
l
y
u
se
d
in
p
re
d
ictiv
e
a
n
a
ly
ti
c
s
e
sp
e
c
iall
y
f
o
r
m
e
d
ica
l
d
a
ta
a
n
d
a
t
th
e
sa
m
e
ti
m
e
p
ro
v
id
e
sig
n
if
ica
n
t
f
a
c
to
rs
o
f
p
re
v
a
len
c
e
h
y
p
e
rten
sio
n
.
A
to
tal
o
f
2
7
p
a
p
e
rs
re
v
ie
w
e
d
,
se
v
e
ra
l
t
e
c
h
n
iq
u
e
s
o
n
p
re
d
ictiv
e
a
n
a
l
y
ti
c
s
in
h
e
a
lt
h
c
a
re
a
re
n
e
u
ra
l
n
e
tw
o
rk
,
d
e
c
isio
n
tree
,
n
a
ïv
e
b
a
y
e
s
,
re
g
re
ss
io
n
a
n
d
s
u
p
p
o
rt
v
e
c
to
r
m
a
c
h
in
e
.
T
h
e
rise
o
f
e
c
o
n
o
m
ic
g
ro
w
th
a
n
d
c
o
rre
late
d
so
c
i
o
-
d
e
m
o
g
r
a
p
h
ic
h
a
v
e
c
a
u
se
rise
in
h
y
p
e
rten
sio
n
p
ro
b
lem
o
v
e
r
p
a
st
y
e
a
rs.
T
h
e
f
a
c
to
rs
o
f
h
y
p
e
rten
sio
n
d
e
p
icte
d
in
th
is
re
v
ie
w
n
a
m
e
l
y
g
e
n
d
e
r,
a
g
e
,
lo
c
a
li
ty
,
f
a
m
il
y
h
isto
ry
,
p
h
y
sic
a
ll
y
in
a
c
t
iv
e
a
n
d
u
n
h
e
a
lt
h
y
li
f
e
st
y
le
n
o
t
c
o
n
f
o
rm
to
a
n
y
b
o
u
n
d
a
ries
t
h
u
s
f
a
r.
T
h
u
s,
th
e
c
h
o
ice
o
n
t
h
e
tec
h
n
iq
u
e
a
n
d
h
y
p
e
rten
sio
n
f
a
c
to
rs
f
o
r
p
re
d
ictiv
e
a
n
a
ly
ti
c
s
is
sig
n
if
ica
n
t
to
c
o
m
e
o
u
t
w
it
h
t
h
e
s
ig
n
if
ica
n
t
p
re
d
ictiv
e
m
o
d
e
l.
T
h
e
p
re
d
ictiv
e
m
o
d
e
l
o
n
p
re
v
a
len
c
e
o
f
h
y
p
e
rte
n
sio
n
m
a
y
p
re
d
ict
t
h
e
se
v
e
rit
y
o
f
a
d
u
lt
h
a
v
in
g
h
y
p
e
rten
sio
n
in
f
u
t
u
re
w
o
rk
.
K
ey
w
o
r
d
s
:
A
r
ti
f
icial
i
n
tel
lig
e
n
ce
Data
an
al
y
tics
H
y
p
er
ten
s
io
n
P
r
ed
ictiv
e
an
al
y
tic
s
T
h
is i
s
a
n
o
p
e
n
a
c
c
e
ss
a
rticle
u
n
d
e
r th
e
CC B
Y
-
SA
li
c
e
n
se
.
C
o
r
r
e
s
p
o
nd
ing
A
uth
o
r
:
Nu
r
A
r
if
a
h
Mo
h
d
No
r
Facu
lt
y
o
f
C
o
m
p
u
ter
an
d
Ma
t
h
e
m
a
tical
Scie
n
ce
U
n
i
v
er
s
iti T
ek
n
o
lo
g
i M
A
R
A
(
UiT
M)
4
0
4
5
0
Sh
ah
A
la
m
,
Sela
n
g
o
r
D
ar
u
l E
h
s
a
n
,
Ma
la
y
s
ia
E
m
ail: a
r
i
f
ah
n
u
r
0
4
@
g
m
ail.
co
m
1.
I
NT
RO
D
UCT
I
O
N
I
n
2
0
1
9
,
Min
is
tr
y
o
f
Hea
lt
h
in
Ma
la
y
s
ia
ai
m
s
to
r
ed
u
ce
th
e
n
u
m
b
er
o
f
s
m
o
k
i
n
g
,
h
y
p
er
ten
s
io
n
,
o
b
esit
y
an
d
m
o
r
e
n
o
n
-
co
m
m
u
n
icab
le
d
is
ea
s
e
(
NC
D)
[
1
]
.
NC
D
is
co
n
s
id
er
ed
as
a
n
o
n
co
n
ta
g
io
u
s
d
is
ea
s
e
Hea
r
t
p
r
o
b
lem
,
s
tr
o
k
e,
ca
n
ce
r
,
d
iab
etes
o
r
h
y
p
er
ten
s
io
n
ar
e
h
ea
lt
h
p
r
o
b
le
m
th
a
t
ca
n
lead
t
o
d
ea
th
.
A
cc
o
r
d
in
g
to
W
HO
d
ata
in
2018
,
s
ig
n
i
f
i
ca
n
tl
y
h
i
g
h
n
u
m
b
er
o
f
N
C
D
c
au
s
e
d
ea
t
h
,
an
d
m
an
y
p
e
o
p
le
r
ep
o
r
ted
d
ied
at
an
ea
r
l
y
ag
e
b
ef
o
r
e
r
ea
ch
i
n
g
7
0
y
ea
r
s
o
ld
.
T
h
e
r
is
k
o
f
N
C
D
r
is
e
e
v
er
y
y
ea
r
a
n
d
m
ai
n
l
y
ar
e
th
o
s
e
w
h
o
ar
e
s
m
o
k
er
s
,
u
n
ac
t
iv
e
li
f
est
y
le,
al
co
h
o
lic
an
d
u
n
h
ea
lt
h
y
d
iet.
H
y
p
er
te
n
s
io
n
al
s
o
o
n
e
o
f
h
ea
lt
h
r
is
k
th
a
t
ca
n
ca
u
s
e
m
o
r
ta
lit
y
[
2
]
.
W
HO
esti
m
ate
2
9
.
3
%
o
f
th
e
w
o
r
ld
’
s
p
o
p
u
lati
o
n
w
ill
b
e
r
is
k
o
f
h
y
p
er
te
n
s
io
n
b
y
t
h
e
y
ea
r
2
0
2
5
.
H
y
p
er
ten
s
io
n
is
a
co
m
m
o
n
co
n
d
itio
n
i
n
m
ed
ical
i
f
n
o
t
tr
ea
tes
ea
r
l
y
ca
n
ca
u
s
e
r
i
s
k
to
cr
itical
h
ea
lt
h
p
r
o
b
lem
[
3
]
.
T
h
er
ef
o
r
e,
p
r
ed
i
ctio
n
o
n
s
ev
er
i
t
y
o
f
h
y
p
er
te
n
s
io
n
is
s
i
g
n
if
ica
n
t
to
g
i
v
e
a
w
ar
en
es
s
to
w
ar
d
h
ea
lt
h
p
r
o
b
lem
.
H
y
p
er
te
n
s
io
n
m
a
y
b
e
a
s
ile
n
t
k
iller
to
s
o
m
e
p
eo
p
le
if
th
e
y
d
o
n
o
t
n
o
tice
t
h
e
s
y
m
p
to
m
s
t
h
at
o
f
te
n
o
cc
u
r
s
.
P
eo
p
le
n
ee
d
t
o
m
o
n
ito
r
th
eir
b
lo
o
d
p
r
ess
u
r
e
an
d
a
w
ar
e
w
it
h
th
e
s
y
m
p
to
m
s
to
av
o
id
s
ev
er
e
co
m
p
lica
tio
n
o
f
h
y
p
er
te
n
s
io
n
.
T
h
e
r
is
k
an
d
f
ac
to
r
m
a
y
d
if
f
er
w
it
h
o
t
h
er
s
b
a
s
ed
o
n
l
i
f
est
y
le
an
d
s
o
cio
-
d
em
o
g
r
ap
h
ic.
Evaluation Warning : The document was created with Spire.PDF for Python.
I
n
t J
A
r
ti
f
I
n
tell
I
SS
N:
2252
-
8938
P
r
ev
a
len
ce
o
f h
yp
erten
s
io
n
:
p
r
ed
ictive
a
n
a
lytics r
ev
iew
(
N
u
r
A
r
ifa
h
Mo
h
d
N
o
r
)
577
I
n
d
u
s
tr
y
o
f
h
ea
lt
h
ca
r
e
g
e
n
er
a
te
lar
g
e
a
m
o
u
n
t
o
f
d
ata
f
r
o
m
th
e
p
atien
t
th
a
t
ca
n
b
e
u
s
ed
f
o
r
f
u
t
u
r
e
p
r
ed
ictio
n
an
d
p
r
ev
en
tio
n
.
T
h
e
p
o
ten
tial
d
ata
is
i
m
p
o
r
ta
n
t
to
i
m
p
r
o
v
e
th
e
q
u
a
l
it
y
o
f
h
ea
lt
h
ca
r
e
f
ie
ld
.
Mo
r
eo
v
er
,
it
ca
n
r
ed
u
ce
th
e
co
s
t
an
d
s
u
p
p
o
r
t
m
ed
ical
an
d
h
ea
lt
h
ca
r
e
p
r
o
ce
s
s
s
u
ch
a
s
d
ec
is
io
n
s
u
p
p
o
r
t
an
d
h
ea
lt
h
m
a
n
ag
e
m
e
n
t
[
4
]
.
T
h
e
p
o
ten
tial
b
e
n
ef
its
f
o
r
h
ea
lt
h
ca
r
e
f
ie
ld
u
s
i
n
g
d
ata
a
n
al
y
tics
ar
e
ca
p
ab
le
f
o
r
p
atter
n
an
al
y
tic,
u
n
s
tr
u
ct
u
r
ed
d
ata
an
al
y
tic
s
,
d
ec
is
io
n
s
u
p
p
o
r
t,
p
r
ed
ictio
n
an
d
tr
ac
ea
b
ilit
y
[
5
]
.
B
y
lo
o
k
in
g
at
th
e
f
i
v
e
ele
m
e
n
t
o
f
p
o
ten
tial
b
en
e
f
it
s
,
p
r
ed
ictiv
e
an
al
y
tics
in
h
ea
lt
h
c
ar
e
ca
n
en
h
an
ce
h
ea
l
th
m
a
n
ag
e
m
en
t
an
d
d
ec
is
io
n
m
ak
in
g
.
I
n
ad
d
itio
n
,
s
i
g
n
i
f
ica
n
t
tec
h
n
iq
u
e
a
n
d
alg
o
r
ith
m
i
s
i
m
p
o
r
ta
n
t
f
o
r
th
e
lear
n
in
g
p
r
o
ce
s
s
o
f
t
h
e
d
ata
t
h
e
n
th
e
b
est
p
r
ed
ictiv
e
m
o
d
el
ca
n
b
e
b
u
ilt.
T
h
u
s
,
t
h
e
o
b
j
ec
tiv
e
o
f
t
h
is
r
ev
ie
w
is
to
id
e
n
tify
t
h
e
f
ac
to
r
s
o
f
p
r
ev
alen
c
e
o
n
h
y
p
er
te
n
s
io
n
i
n
Ma
la
y
s
ia
a
n
d
th
e
o
t
h
er
s
c
o
u
n
tr
ies
th
at
ca
n
b
e
an
al
y
s
ed
u
s
i
n
g
th
e
s
elec
ted
p
r
ed
ictiv
e
an
al
y
tics
t
h
at
co
m
m
o
n
l
y
u
s
ed
b
y
p
r
ev
io
u
s
r
esea
r
ch
er
s
.
Fu
r
t
h
er
m
o
r
e,
a
r
ev
ie
w
o
n
th
e
tec
h
n
iq
u
es
o
f
p
r
ed
ictiv
e
an
al
y
tics
w
il
l
b
e
in
clu
d
ed
in
t
h
e
d
is
c
u
s
s
i
o
n
to
id
en
ti
f
y
t
h
e
s
i
g
n
if
ican
t
tec
h
n
i
q
u
es
f
o
r
p
r
ed
ictiv
e
m
o
d
el
i
n
f
u
t
u
r
e
w
o
r
k
.
T
h
e
r
ev
ie
w
o
r
g
a
n
ized
i
n
th
is
p
ap
er
as
f
o
llo
w
s
.
Sectio
n
2
p
r
esen
ts
th
e
t
w
o
s
eg
m
e
n
tatio
n
o
f
r
elate
d
w
o
r
k
s
w
h
ic
h
ar
e
in
f
o
r
m
atio
n
ab
o
u
t
h
y
p
er
ten
s
io
n
an
d
th
e
d
escr
ip
tio
n
o
f
p
r
ed
ictiv
e
an
aly
tics
i
n
cl
u
d
in
g
th
e
tech
n
iq
u
es
u
s
ed
in
p
r
ed
ictiv
e
an
al
y
tics
.
Sectio
n
3
p
r
esen
ts
th
e
m
et
h
o
d
an
d
m
ater
ial
s
u
s
ed
f
o
r
th
e
r
ev
ie
w
.
Sectio
n
4
p
r
o
v
id
es
t
h
e
d
is
c
u
s
s
io
n
o
n
p
r
ev
alen
ce
f
ac
to
r
s
o
f
h
y
p
er
te
n
s
io
n
a
n
d
p
r
ed
ictiv
e
a
n
al
y
tic
s
’
tech
n
iq
u
es
co
m
m
o
n
l
y
u
s
ed
t
o
b
u
ild
p
r
ed
ictiv
e
m
o
d
el
i
n
h
ea
l
th
ca
r
e.
2.
RE
L
AT
E
D
WO
RK
S
2
.
1
.
H
y
pert
ens
io
n
H
y
p
er
ten
s
io
n
i
s
a
co
m
m
o
n
m
ed
ical
p
r
o
b
lem
t
h
at
c
u
r
r
en
tl
y
i
s
a
b
u
r
d
en
to
g
lo
b
al
h
ea
lt
h
.
H
y
p
er
ten
s
io
n
is
o
n
e
o
f
h
ea
lt
h
r
is
k
t
h
at
ca
n
ca
u
s
e
m
o
r
talit
y
[
2
]
.
Hy
p
e
r
ten
s
io
n
is
d
iag
n
o
s
ed
w
h
e
n
t
h
e
b
lo
o
d
p
r
ess
u
r
e
i
s
g
r
ea
ter
th
a
n
1
3
0
/8
0
mm
H
g
.
No
r
m
al
b
lo
o
d
p
r
ess
u
r
e
is
w
h
e
n
th
e
b
lo
o
d
p
r
ess
u
r
e
is
1
2
0
/8
0
mm
H
g
b
elo
w
.
Ge
n
er
all
y
,
b
lo
o
d
p
r
e
s
s
u
r
e
b
et
w
ee
n
1
2
0
/7
0
an
d
1
4
0
/
9
0
m
m
H
g
w
i
ll
lead
to
t
h
e
r
i
s
k
o
f
i
n
cr
ea
s
ed
b
lo
o
d
p
r
ess
u
r
e.
T
h
e
c
h
an
g
es
i
n
b
lo
o
d
p
r
ess
u
r
e
m
a
y
ac
c
u
r
d
ep
e
n
d
in
g
o
n
o
u
r
d
ail
y
ac
ti
v
itie
s
.
T
h
er
e
ar
e
s
e
v
er
al
co
n
d
itio
n
s
t
h
at
ca
n
af
f
ec
t
t
h
e
b
lo
o
d
p
r
ess
u
r
e
s
u
c
h
as
p
at
ie
n
t
ag
e,
h
ea
r
t
co
n
d
itio
n
,
e
m
o
ti
o
n
s
,
d
ail
y
ac
ti
v
itie
s
an
d
m
ed
icatio
n
tak
e
n
.
H
y
p
er
ten
s
io
n
o
cc
u
r
s
w
h
e
n
h
i
g
h
v
o
lu
m
e
o
f
b
lo
o
d
f
lo
w
s
i
n
n
ar
r
o
w
o
r
co
m
p
licated
ar
ter
ies.
T
h
is
w
il
l c
au
s
e
t
h
e
h
e
ar
t to
p
u
m
p
m
o
r
e
an
d
if
n
o
t tr
ea
ted
ca
n
ca
u
s
e
m
aj
o
r
h
ea
lth
p
r
o
b
lem
s
[
3
]
.
A
cc
o
r
d
in
g
to
[
6
]
,
ad
u
lt
s
o
v
er
th
e
ag
e
o
f
2
5
h
av
e
h
ig
h
er
r
is
k
to
b
e
d
iag
n
o
s
ed
w
i
th
h
y
p
er
ten
s
io
n
.
T
h
er
e
is
an
in
ce
a
s
e
o
f
h
y
p
er
te
n
s
io
n
ca
s
es
f
r
o
m
5
9
4
m
il
lio
n
i
n
1
9
7
5
to
1
.
1
3
b
illi
o
n
in
2
0
1
5
.
Ma
la
y
s
ia
n
s
h
a
v
e
a
h
ig
h
s
co
r
e
r
elate
d
to
h
y
p
e
r
te
n
s
io
n
a
m
o
n
g
t
h
e
s
o
ciet
y
an
d
r
e
p
o
r
ted
as
d
an
g
er
o
u
s
f
o
r
h
ea
lt
h
.
T
h
e
e
m
er
g
en
ce
o
f
h
y
p
er
te
n
s
io
n
p
r
o
b
le
m
o
v
er
th
e
y
ea
r
s
h
a
s
ca
u
s
e
m
o
r
tal
it
y
i
n
Ma
la
y
s
ia
to
r
is
e
p
ar
allel
w
i
th
t
h
e
ex
p
a
n
s
io
n
o
f
d
ev
elo
p
m
en
t
w
h
ic
h
ch
an
g
e
t
h
e
s
o
cio
-
d
e
m
o
g
r
ap
h
ic
b
eh
av
i
o
u
r
[
7
]
.
T
h
e
r
is
in
g
tr
en
d
in
h
y
p
er
te
n
s
io
n
ca
s
e
s
a
m
o
n
g
ad
u
lt d
ep
en
d
s
o
n
t
h
e
e
d
u
ca
tio
n
le
v
el,
r
ac
e,
i
n
co
m
e
le
v
el,
ag
e
a
n
d
d
e
m
o
g
r
ap
h
ic.
T
h
e
co
m
p
licat
io
n
s
o
f
t
h
e
u
n
co
n
tr
o
lled
h
ig
h
b
lo
o
d
p
r
ess
u
r
e
i
n
cl
u
d
in
g
h
ea
r
t
p
r
o
b
lem
,
s
tr
o
k
e,
w
ea
k
en
ed
a
n
d
n
ar
r
o
w
ed
b
lo
o
d
v
ess
els
i
n
t
h
e
k
id
n
e
y
s
.
P
eo
p
le
w
h
o
h
as
h
ig
h
b
lo
o
d
p
r
ess
u
r
e
te
n
d
to
f
ee
l
h
ea
d
ac
h
e,
d
i
f
f
icu
lt
y
o
f
b
r
ea
th
i
n
g
,
f
a
tig
u
e
o
r
v
i
s
io
n
co
m
p
lica
tio
n
.
Hi
g
h
b
lo
o
d
p
r
ess
u
r
e
if
n
o
t
tr
ea
ted
ca
n
ca
u
s
e
co
m
p
lica
tio
n
a
n
d
r
is
k
to
h
ea
r
t
attac
k
s
,
s
tr
o
k
e,
k
id
n
e
y
f
ailu
r
e
an
d
b
lin
d
n
e
s
s
.
T
h
u
s
,
d
ata
o
n
p
r
ev
alen
ce
f
ac
to
r
s
o
f
h
y
p
er
te
n
s
io
n
ar
e
ess
en
tial
in
p
r
o
p
o
s
in
g
n
e
w
s
tr
ateg
ie
s
to
co
m
b
at
h
y
p
er
te
n
s
io
n
p
r
o
b
le
m
an
d
p
r
escr
ib
e
p
r
ev
en
tio
n
s
o
th
at
t
h
e
y
ar
e
a
war
e
o
f
th
eir
h
ea
lt
h
a
n
d
tak
e
n
e
ce
s
s
ar
y
ac
tio
n
.
2
.
2
.
P
re
dict
iv
e
a
na
ly
t
ics a
nd
t
ec
hn
iqu
e
s
Data
an
al
y
tics
is
a
tech
n
iq
u
e
o
f
co
llectin
g
d
ata
an
d
ex
tr
ac
tin
g
t
h
e
d
ata
in
to
m
ea
n
in
g
f
u
l
i
n
f
o
r
m
atio
n
th
at
ca
n
b
e
u
s
ed
f
o
r
s
o
lv
i
n
g
p
r
o
b
lem
a
n
d
co
n
c
lu
s
io
n
.
E
x
a
m
p
le
o
f
t
h
e
d
ata
is
w
eb
lo
g
s
,
ca
ll
r
ec
o
r
d
s
,
m
ed
ical
r
ec
o
r
d
,
im
a
g
es,
v
id
eo
,
tex
t
a
n
d
m
o
r
e.
T
h
e
an
al
y
s
is
is
s
i
g
n
i
f
ican
t
w
i
th
d
ata
s
cie
n
ce
,
b
u
s
in
ess
i
n
telli
g
e
n
ce
a
n
d
b
u
s
i
n
ess
an
a
l
y
t
ic.
T
h
e
p
r
o
ce
s
s
o
f
d
ata
a
n
al
y
tic
i
s
a
w
a
y
to
f
i
n
d
u
n
s
ee
n
in
f
o
r
m
atio
n
t
h
at
ca
n
b
e
e
x
tr
ac
ted
f
r
o
m
th
e
r
a
w
d
ata
f
o
r
h
u
m
a
n
co
n
s
u
m
p
tio
n
.
T
h
er
e
ar
e
s
ev
er
al
p
r
o
ce
s
s
es
in
d
ata
a
n
al
y
tics
,
w
h
ic
h
ar
e
to
co
llect
d
ata,
tr
an
s
f
o
r
m
,
clea
n
s
e,
clas
s
i
f
y
a
n
d
co
n
v
er
t
t
h
e
d
ata
to
m
ea
n
i
n
g
f
u
l
r
ep
o
r
tin
g
f
o
r
m
at
th
at
c
an
b
e
u
n
d
er
s
to
o
d
.
P
r
ed
ictiv
e
an
al
y
tic
is
o
n
e
o
f
t
h
e
an
a
l
y
t
ics
ca
te
g
o
r
ies
co
m
m
o
n
l
y
u
s
ed
i
n
i
n
d
u
s
tr
y
s
u
ch
a
s
m
ed
ical,
b
u
s
in
e
s
s
,
ag
r
icu
l
tu
r
e
a
n
d
m
o
r
e.
T
h
e
p
u
r
p
o
s
e
o
f
p
r
ed
ictiv
e
a
n
al
y
tic
i
s
to
p
r
o
v
id
e
an
d
e
v
al
u
ate
a
m
o
d
el
w
it
h
ac
c
u
r
at
e
p
r
ed
ictio
n
f
o
r
f
u
tu
r
e
b
y
lo
o
k
i
n
g
a
t
h
is
to
r
ical
d
ata.
T
h
u
s
,
d
a
ta
is
t
h
e
v
alu
ab
le
a
s
s
et
to
e
x
tr
ac
t
in
f
o
r
m
atio
n
an
d
to
co
m
e
o
u
t
w
i
t
h
s
o
lu
t
io
n
f
o
r
f
u
tu
r
e
p
u
r
p
o
s
es.
Usi
n
g
ar
ti
f
icial
i
n
tel
lig
e
n
ce
(
A
I
)
tech
n
iq
u
es
o
n
p
r
ed
ictiv
e
an
al
y
tics
i
s
an
o
t
h
er
lev
el
o
f
ev
alu
a
tin
g
d
ata
o
n
d
ata
an
aly
tic
s
.
T
h
e
o
b
jectiv
e
o
f
ex
tr
ac
ti
n
g
th
e
d
ata
is
to
co
m
b
in
e
d
ata
an
d
p
r
o
v
id
e
m
ea
n
in
g
f
u
l
r
esu
lt
s
,
d
etec
t
p
atter
n
an
d
th
e
r
elatio
n
s
h
ip
b
et
w
ee
n
p
ar
a
m
eter
s
.
A
I
t
ec
h
n
iq
u
es
t
h
at
co
m
m
o
n
l
y
u
s
e
d
in
d
ata
an
al
y
tic
s
ar
e
ass
o
ciatio
n
r
u
le
m
i
n
i
n
g
,
g
en
et
ic
alg
o
r
it
h
m
,
d
ec
is
io
n
tr
ee
an
al
y
s
is
,
n
eu
r
al
n
et
w
o
r
k
a
n
d
m
o
r
e.
Dif
f
er
en
t
tech
n
iq
u
e
h
as
d
i
f
f
er
en
t
ap
p
r
o
ac
h
es
o
f
p
u
r
p
o
s
e
o
n
t
h
e
d
ata.
S
ig
n
if
ican
t
tech
n
iq
u
e
o
n
p
r
ed
ictio
n
is
i
m
p
o
r
tan
t
to
m
ak
e
t
h
e
al
g
o
r
ith
m
lear
n
ed
t
h
e
p
atter
n
w
el
l d
u
r
in
g
t
h
e
lear
n
in
g
p
r
o
ce
s
s
.
Evaluation Warning : The document was created with Spire.PDF for Python.
I
SS
N
:
2
2
5
2
-
8938
I
n
t J
A
r
ti
f
I
n
tell
,
Vo
l.
9
,
No
.
4
,
Dec
e
m
b
er
20
20
:
5
7
6
–
5
83
578
Data
s
cie
n
ce
w
it
h
ad
v
a
n
ce
d
co
m
p
u
ti
n
g
alg
o
r
it
h
m
u
s
i
n
g
A
I
ar
e
i
n
tr
en
d
tec
h
n
iq
u
e
to
o
p
ti
m
ize,
au
to
m
ate
a
n
d
f
i
n
d
u
n
s
ee
n
v
al
u
e
b
y
h
u
m
a
n
.
Or
g
a
n
izatio
n
s
ar
e
s
ee
k
i
n
g
to
tak
e
b
en
e
f
it
s
f
r
o
m
d
ata
an
al
y
tic
s
an
d
A
I
e
m
er
g
in
g
tr
e
n
d
s
t
h
at
ca
n
b
r
in
g
m
o
r
e
p
r
o
f
it
s
i
n
b
u
s
i
n
es
s
e
s
.
E
n
ter
p
r
is
es
u
s
e
t
h
e
tr
en
d
s
o
f
d
ata
a
n
al
y
tics
an
d
A
I
e
m
b
ed
d
ed
in
en
ter
p
r
is
e
ad
v
an
ce
d
ap
p
licatio
n
t
y
p
icall
y
u
s
ed
in
lar
g
e
o
r
g
an
izatio
n
to
m
an
ag
e
r
eso
u
r
ce
s
an
d
cu
s
to
m
er
in
f
o
r
m
a
tio
n
.
T
h
er
e
ar
e
f
iv
e
co
m
m
o
n
p
r
ed
ictio
n
tech
n
iq
u
es
th
at
m
o
s
tl
y
u
s
ed
t
o
b
u
ild
p
r
ed
ictiv
e
m
o
d
el
n
a
m
el
y
n
e
u
r
al
n
et
w
o
r
k
,
d
ec
is
io
n
tr
ee
,
li
n
ea
r
r
eg
r
es
s
io
n
,
as
s
o
ciatio
n
r
u
le
m
i
n
i
n
g
an
d
s
u
p
p
o
r
t
v
ec
to
r
m
ac
h
i
n
e
(
SVM)
[8
-
13]
.
T
h
e
d
escr
ip
tio
n
b
elo
w
ex
p
lai
n
s
b
r
ief
l
y
o
f
ea
ch
tec
h
n
iq
u
e
w
it
h
th
e
r
ef
er
en
ce
s
p
ap
er
th
at
u
s
ed
th
e
tec
h
n
iq
u
es.
a.
Neu
r
al
n
e
t
w
o
r
k
Neu
r
al
n
e
t
w
o
r
k
i
s
s
u
itab
le
to
f
i
n
d
p
atter
n
s
i
n
d
ata
t
h
r
u
a
n
al
y
s
i
n
g
its
in
p
u
t
a
n
d
o
u
tp
u
t
f
r
o
m
t
h
e
tr
ai
n
in
g
d
ataset.
T
h
e
p
ap
er
b
y
[
8
]
co
m
p
ar
e
d
if
f
er
en
t
lear
n
i
n
g
s
tr
ateg
i
es
w
it
h
v
ar
io
u
s
lear
n
i
n
g
w
ei
g
h
ts
i
n
o
r
d
er
to
id
en
ti
f
y
b
est
a
lg
o
r
it
h
m
w
it
h
th
e
lo
w
e
s
t
er
r
o
r
o
n
th
e
tr
an
i
n
g
d
ataset.
T
h
e
o
u
tco
m
e
p
r
o
v
id
es
s
ev
er
al
ap
p
r
o
ac
h
es f
o
r
p
r
ed
ictiv
e
m
o
d
el
an
d
co
m
p
ar
is
o
n
to
p
r
o
d
u
ce
th
e
b
est n
e
t
w
o
r
k
.
b.
L
i
n
ea
r
r
eg
r
ess
io
n
L
i
n
ea
r
r
eg
r
ess
io
n
m
o
d
el
s
co
n
n
ec
tio
n
o
f
t
w
o
v
ar
iab
les
i
n
a
li
n
ea
r
lin
e
o
n
th
e
o
b
s
er
v
ed
d
ata
.
Fo
r
in
s
ta
n
ce
,
lin
ea
r
r
eg
r
es
s
io
n
al
g
o
r
ith
m
is
u
s
ed
i
n
th
e
h
ea
lt
h
ca
r
e
d
ata
t
o
p
r
ed
ict
w
h
et
h
er
th
e
p
atie
n
t
h
av
i
n
g
a
h
ea
r
t
p
r
o
b
lem
o
r
n
o
t
b
ased
o
n
th
e
r
ec
o
r
d
ed
in
f
o
r
m
atio
n
[
9
]
.
T
h
e
au
th
o
r
s
a
i
m
to
ass
is
t
t
h
e
n
o
n
-
m
ed
ical
s
taf
f
to
u
s
e
t
h
is
ap
p
licatio
n
to
p
r
ed
ict
h
ea
r
t d
is
ea
s
e
a
n
d
r
ed
u
ce
th
e
ti
m
e
co
m
p
le
x
it
y
to
m
ee
t sp
ec
ial
is
t.
c.
Ass
o
ciatio
n
r
u
le
m
in
in
g
An
o
th
er
tec
h
n
iq
u
e
is
a
s
s
o
ciati
o
n
r
u
le
m
in
i
n
g
,
a
tech
n
iq
u
e
t
h
at
d
ata
s
cie
n
tis
t
u
s
es
to
d
eter
m
i
n
e
s
p
ec
i
f
ic
p
atter
n
s
an
d
as
s
o
ciate
d
r
elatio
n
s
w
it
h
i
n
t
h
e
d
ata.
Ass
o
ciatio
n
r
u
le
m
i
n
i
n
g
d
eter
m
in
e
p
atter
n
s
t
h
at
o
cc
u
r
s
co
n
s
ta
n
tl
y
,
co
r
r
elatio
n
s
,
li
n
k
s
,
o
r
u
n
in
te
n
tio
n
a
l
s
tr
u
ct
u
r
es
i
n
s
ets
o
f
ite
m
s
o
r
tr
an
s
ac
tio
n
in
d
atab
ases
.
I
t
is
u
s
u
all
y
ap
p
lied
f
o
r
m
ar
k
et
b
ask
et
an
al
y
s
i
s
.
I
n
[
1
0
]
ap
p
ly
t
h
e
ass
o
ciatio
n
r
u
le
w
it
h
lear
n
in
g
m
an
a
g
e
m
e
n
t
s
y
s
te
m
(
L
M
S)
d
ata
an
d
p
r
esen
t
th
e
r
u
les
a
n
d
r
elev
an
t
r
es
u
lt
s
o
n
its
p
er
f
o
r
m
an
ce
a
n
d
s
u
itab
il
it
y
i
n
L
MS
e
n
v
ir
o
n
m
e
n
t.
B
esid
es,
[
1
1
]
u
s
ed
ass
o
ci
at
io
n
r
u
les to
e
x
tr
ac
t p
atter
n
f
r
o
m
t
h
e
d
ates’
o
f
p
r
o
d
u
ct
d
ataset
to
s
u
p
p
o
r
t
b
u
s
i
n
es
s
es
to
e
x
p
lo
r
e
v
ar
iet
y
asp
ec
t
r
elate
d
to
p
r
o
d
u
ctiv
it
y
an
d
p
r
o
ce
s
s
ex
ce
lle
n
ce
.
T
h
e
r
esu
lt
s
p
r
o
d
u
ce
in
s
i
g
h
ts
i
n
f
o
r
m
atio
n
o
n
b
u
s
in
e
s
s
v
ital
s
i
g
n
,
t
h
e
it
s
s
tr
ate
g
y
r
elate
d
to
c
o
n
s
u
m
er
an
d
m
ar
k
et
in
g
v
ie
ws.
d.
Dec
is
io
n
tr
ee
clas
s
i
f
ier
Dec
is
io
n
tr
ee
al
g
o
r
ith
m
s
p
lit
s
d
ata
i
n
to
s
u
b
s
et
s
b
ased
o
n
a
n
attr
ib
u
te
v
al
u
e.
T
h
e
p
r
o
ce
s
s
co
n
ti
n
u
e
s
f
o
r
ea
ch
co
n
s
eq
u
e
n
t
s
u
b
s
et
u
n
til
tar
g
et
f
o
u
n
d
.
Fu
r
t
h
er
m
o
r
e,
p
r
ed
ictiv
e
an
al
y
tic
s
al
s
o
ca
n
b
e
u
s
ed
i
n
ed
u
ca
tio
n
f
ield
to
p
r
ed
ict
u
n
i
v
er
s
it
y
s
tu
d
e
n
t
in
tak
e
i
n
s
elec
ti
n
g
t
h
e
s
t
u
d
en
t
ap
p
lican
ts
to
b
e
o
f
f
er
ed
.
T
h
e
r
esear
ch
p
r
o
d
u
ce
b
y
[
1
2
]
,
p
r
esen
ts
p
r
ed
ictio
n
o
f
s
tu
d
e
n
t
in
tak
e
u
s
i
n
g
d
ec
is
io
n
tr
e
e
an
d
k
-
Nea
r
est
Neig
h
b
o
u
r
alg
o
r
it
h
m
.
T
h
e
e
x
p
er
i
m
e
n
t
ai
m
s
to
p
r
o
v
id
e
th
e
ap
p
r
o
p
r
iate
m
o
d
el
to
p
r
ed
ict
s
tu
d
en
t
ac
ce
p
tan
ce
t
o
th
e
o
f
f
er
g
i
v
en
w
it
h
t
h
e
b
est s
elec
ted
attr
ib
u
te
s
in
a
n
in
te
lli
g
en
t
w
a
y
.
e.
Su
p
p
o
r
t
v
ec
to
r
m
ac
h
i
n
e
(
SV
M)
SVM
co
n
s
tr
u
ct
h
y
p
er
p
lan
es
in
m
u
ltid
i
m
en
s
io
n
al
s
p
ac
e
to
p
r
o
v
id
e
class
i
f
icatio
n
w
it
h
s
ep
ar
ate
class
lev
els
o
n
ei
th
er
s
id
e.
P
r
ed
ictiv
e
an
al
y
tics
also
w
o
r
k
w
ell
f
o
r
i
m
a
g
e
r
ec
o
g
n
itio
n
an
d
u
s
e
f
u
l
f
o
r
p
r
ed
ictio
n
.
I
n
[
1
3
]
u
s
ed
i
m
ag
e
s
as
t
h
e
d
at
a
to
p
r
ed
ict
d
iab
etes.
T
h
e
r
esear
ch
er
u
s
ed
P
DR
i
m
ag
e
s
a
n
d
t
est
t
h
e
m
o
d
el
u
s
i
n
g
p
r
o
b
ab
ilis
tic
n
eu
r
al
n
e
t
w
o
r
k
(
P
NN)
,
B
ay
e
s
ian
cla
s
s
i
f
i
er
an
d
SVM
tech
n
iq
u
e
s
.
3.
M
E
T
H
O
D
AND
M
AT
E
RIA
L
S
T
h
is
s
ec
tio
n
p
r
esen
ts
th
e
t
w
o
d
i
f
f
er
en
t
g
ap
s
o
f
r
e
v
ie
w
w
h
ich
is
h
ea
l
th
d
o
m
ai
n
f
o
cu
s
in
g
o
n
h
y
p
er
te
n
s
io
n
a
n
d
p
r
ed
ictiv
e
a
n
al
y
tics
tec
h
n
iq
u
e
s
.
T
h
e
o
v
er
v
ie
w
g
ap
o
f
r
esear
ch
ar
ea
i
n
th
i
s
p
ap
er
is
s
h
o
w
n
i
n
Fig
u
r
e
1
.
A
liter
at
u
r
e
r
ev
ie
w
w
as
co
n
d
u
cted
to
id
en
t
if
y
r
ec
en
t
j
o
u
r
n
al
an
d
ar
ticles
ab
o
u
t
t
h
e
p
r
ed
ictiv
e
an
al
y
tic
o
p
p
o
r
tu
n
ities
i
n
h
ea
lt
h
ca
r
e.
I
n
ad
d
itio
n
,
th
e
r
ev
ie
w
also
f
o
cu
s
es
o
n
t
h
e
d
o
m
a
in
w
h
ic
h
is
p
r
ev
alen
ce
f
ac
to
r
s
o
f
h
y
p
er
ten
s
io
n
i
n
ad
u
lt.
T
h
e
k
ey
w
o
r
d
ter
m
s
w
er
e
u
s
ed
ar
e
“
p
r
ed
ictiv
e
an
al
y
tics
”
,
“
p
r
ed
ictio
n
”,
“
b
i
g
d
ata
an
al
y
tic
s
”,
“
p
r
ev
ale
n
ce
o
f
h
y
p
er
te
n
s
io
n
”,
“f
ac
to
r
s
o
f
h
y
p
er
te
n
s
io
n
”,
“
r
i
s
k
o
f
h
y
p
er
te
n
s
io
n
”,
“
a
w
ar
e
n
es
s
o
f
h
y
p
er
ten
s
io
n
”
a
n
d
m
o
r
e.
T
h
e
s
o
u
r
ce
o
f
f
i
n
d
i
n
g
th
e
ar
ticl
es
w
er
e
Go
o
g
le
Sc
h
o
lar
,
Scie
n
ce
Dir
ec
t,
Sp
r
in
g
er
,
I
E
E
E
,
P
L
OS O
NE
a
n
d
m
o
r
e.
Evaluation Warning : The document was created with Spire.PDF for Python.
I
n
t J
A
r
ti
f
I
n
tell
I
SS
N:
2252
-
8938
P
r
ev
a
len
ce
o
f h
yp
erten
s
io
n
:
p
r
ed
ictive
a
n
a
lytics r
ev
iew
(
N
u
r
A
r
ifa
h
Mo
h
d
N
o
r
)
579
Fig
u
r
e
1
.
Gap
o
f
r
esear
ch
ar
ea
A
to
tal
o
f
1
5
p
ap
er
s
o
n
p
r
e
d
ictiv
e
a
n
al
y
tic
a
n
d
1
2
p
ap
er
s
o
n
t
h
e
p
r
ev
alen
ce
o
f
h
y
p
er
ten
s
io
n
w
er
e
s
t
u
d
ied
an
d
a
n
al
y
s
ed
t
o
g
at
h
er
all
th
e
in
f
o
r
m
a
tio
n
o
n
t
h
e
p
r
ev
a
len
ce
o
f
h
y
p
er
ten
s
io
n
:
p
r
ed
ictiv
e
an
al
y
tics
r
ev
ie
w
.
T
h
e
liter
atu
r
e
r
ev
ie
w
in
cl
u
d
ed
ar
ticles
p
u
b
lis
h
ed
w
it
h
i
n
t
h
e
last
7
y
ea
r
s
h
as
b
ee
n
r
ev
ie
w
ed
to
f
u
l
f
i
l
th
e
o
b
j
ec
tiv
e
o
f
th
i
s
r
ev
ie
w
p
ap
er
to
an
al
y
ze
.
T
h
e
to
tal
n
u
m
b
er
o
f
2
7
p
ap
er
s
th
at
w
a
s
p
u
b
lis
h
ed
f
r
o
m
2
0
1
3
u
n
til
2
0
1
9
.
A
ll
th
e
s
o
u
r
ce
s
o
f
liter
at
u
r
e
r
ev
ie
w
h
a
v
e
b
ee
n
an
al
y
s
e
s
to
co
m
e
o
u
t
with
t
h
e
b
est
r
ev
ie
w
ab
o
u
t
p
r
ed
ictiv
e
a
n
al
y
tics
an
d
th
e
p
r
ev
ale
n
ce
o
n
h
y
p
er
te
n
s
io
n
.
Fro
m
t
h
e
r
ea
d
in
g
,
F
i
g
u
r
e
2
s
h
o
w
s
t
h
e
p
er
ce
n
tag
e
o
f
ar
ticle
co
llected
to
b
e
r
ev
ie
w
ed
b
y
y
ea
r
it i
s
p
u
b
lis
h
ed
.
Fig
u
r
e
2
.
Nu
m
b
er
o
f
r
ev
ie
w
p
ap
er
o
n
h
y
p
er
ten
s
io
n
an
d
p
r
ed
ictiv
e
a
n
al
y
tics
b
y
p
u
b
lis
h
in
g
y
ea
r
s
T
h
e
ar
ticles
th
at
r
elate
d
o
n
p
r
ev
alen
ce
f
ac
to
r
s
o
f
h
y
p
er
te
n
s
i
o
n
w
er
e
f
o
u
n
d
a
m
o
n
g
s
e
v
er
al
co
u
n
tr
ies
s
u
c
h
as
I
n
d
o
n
e
s
ia,
T
h
ailan
d
,
I
n
d
ia,
C
h
i
n
a,
Sp
ain
,
Ko
r
ea
,
L
eb
an
o
n
a
n
d
T
u
r
k
e
y
i
n
cl
u
d
ed
Ma
lay
s
ia.
T
h
ese
ar
ticles
w
er
e
r
e
v
ie
w
ed
to
co
m
p
ile
t
h
e
co
m
m
o
n
f
ac
to
r
s
o
f
h
y
p
er
te
n
s
io
n
a
m
o
n
g
t
h
e
co
u
n
tr
ies.
T
h
e
co
m
m
o
n
an
d
r
elev
a
n
ce
f
ac
to
r
s
ca
n
b
e
u
s
ed
a
s
t
h
e
s
elec
ted
v
ar
iab
l
es
to
b
u
ild
p
r
ed
ictiv
e
m
o
d
el
o
n
h
y
p
er
ten
s
io
n
a
s
s
h
o
w
n
i
n
T
ab
le
1
in
Sectio
n
4
.
Nex
t,
s
ev
er
al
ar
ticles
o
f
p
r
ed
ictiv
e
an
al
y
tic
s
th
at
r
elate
d
o
n
th
e
p
r
ed
ictio
n
tech
n
iq
u
e
th
at
u
s
ed
m
ed
ical
d
ata
w
er
e
id
en
ti
f
ied
a
n
d
co
m
p
iled
.
T
h
u
s
,
t
h
e
c
h
o
ice
o
f
t
h
e
tech
n
iq
u
e
to
b
u
ild
p
r
ed
ictiv
e
m
o
d
el
is
i
m
p
o
r
tan
t to
p
r
o
v
id
e
w
i
th
t
h
e
s
i
g
n
i
f
ican
t
p
r
ed
ictiv
e
m
o
d
el.
4.
RE
SU
L
T
S
AND
D
I
SCU
SS
I
O
N
T
h
is
s
ec
tio
n
w
il
l d
is
cu
s
s
t
h
e
s
i
g
n
i
f
ica
n
t p
r
ev
ale
n
ce
f
ac
to
r
s
o
f
h
y
p
er
ten
s
io
n
a
m
o
n
g
s
e
v
er
al
c
o
u
n
tr
ies
an
d
th
e
p
r
ed
ictiv
e
a
n
al
y
t
ics r
e
v
ie
w
.
4
.
1
.
P
re
v
a
lence
f
a
c
t
o
rs o
f
hy
pert
ens
io
n
T
h
e
in
cr
ea
s
e
in
h
y
p
er
te
n
s
io
n
d
ep
en
d
s
o
n
th
e
g
en
d
er
,
ag
e,
l
o
ca
lit
y
,
m
ar
ita
l
s
tat
u
s
,
i
n
co
m
e,
cu
r
r
en
t
s
m
o
k
er
,
cu
r
r
en
t
d
r
in
k
er
,
p
h
y
s
i
ca
ll
y
ac
tiv
it
y
,
f
a
m
i
l
y
h
is
to
r
y
a
n
d
B
MI
s
tat
u
s
.
B
ased
o
n
t
h
e
r
ev
ie
w
,
t
h
er
e
ar
e
1
1
f
ac
to
r
s
th
a
t
h
a
v
e
b
ee
n
r
eg
u
lar
l
y
d
is
c
u
s
s
ed
b
y
r
esear
c
h
er
s
f
o
r
d
eter
m
i
n
i
n
g
t
h
e
p
r
ev
ale
n
ce
o
f
h
y
p
er
te
n
s
io
n
in
Ma
la
y
s
ia
,
I
n
d
o
n
e
s
ia,
T
h
ailan
d
,
I
n
d
ia,
C
h
i
n
a,
Sp
ai
n
,
Ko
r
ea
,
L
eb
an
o
n
,
T
u
r
k
e
y
an
d
I
n
d
ia.
F
ig
u
r
e
3
p
r
esen
t
s
t
h
e
ass
o
ciate
d
f
ac
to
r
o
f
h
y
p
er
te
n
s
io
n
f
r
o
m
t
h
e
1
2
ar
ticles
m
ea
n
w
h
ile
T
ab
le
1
p
r
esen
ts
t
h
e
ass
o
ciate
d
f
ac
to
r
o
f
h
y
p
er
te
n
s
io
n
b
ased
o
n
9
co
u
n
t
r
ies.
B
ased
o
n
th
e
ar
ticles
f
o
u
n
d
ab
o
u
t
p
r
ev
alen
ce
o
f
h
y
p
er
te
n
s
io
n
i
n
9
co
u
n
tr
ies,
g
e
n
d
er
,
ag
e,
B
MI
s
tatu
s
,
lo
ca
lit
y
a
n
d
s
m
o
k
er
ar
e
th
e
to
p
f
ac
to
r
s
o
f
h
y
p
er
ten
s
io
n
as
s
h
o
w
n
in
T
ab
le
1
.
Ou
t
o
f
1
2
a
r
ticles
f
r
o
m
9
co
u
n
tr
ies,
th
e
g
e
n
d
er
,
ag
e
a
n
d
B
MI
s
tatu
s
ar
e
th
e
f
a
m
o
u
s
v
ar
iab
les
t
h
at
h
a
v
e
b
ee
n
d
is
cu
s
s
ed
.
Me
an
w
h
ile
d
r
in
k
er
,
ed
u
ca
tio
n
t
y
p
e,
in
c
o
m
e,
f
a
m
il
y
h
i
s
to
r
y
an
d
m
ar
ital
s
tatu
s
ar
e
also
m
e
n
ti
o
n
ed
as
f
ac
to
r
s
o
f
h
y
p
er
te
n
s
io
n
.
Ho
w
e
v
er
,
th
e
s
e
f
ac
to
r
s
ar
e
n
o
t p
o
p
u
lar
d
is
cu
s
s
ed
in
o
th
er
s
co
u
n
tr
ies,
b
u
t it
c
an
af
f
ec
t t
h
e
r
ate
o
f
a
w
ar
e
n
ess
a
m
o
n
g
p
eo
p
le.
P
r
e
v
a
l
e
n
c
e
F
a
c
t
o
r
s o
f
H
y
p
e
r
t
e
n
sio
n
Pr
e
d
i
c
t
i
v
e
A
n
a
l
y
i
c
s
P
r
e
d
i
c
t
i
v
e
M
o
d
e
l
o
f
H
y
p
e
r
t
e
n
sio
n
1
2
3
2
7
4
8
0
2
4
6
8
10
2
0
1
3
2
0
1
4
2
0
1
5
2
0
1
6
2
0
1
7
2
0
1
8
2
0
1
9
N
u
mb
e
r
o
f
p
a
p
e
r
s
Y
e
a
r
s
Evaluation Warning : The document was created with Spire.PDF for Python.
I
SS
N
:
2
2
5
2
-
8938
I
n
t J
A
r
ti
f
I
n
tell
,
Vo
l.
9
,
No
.
4
,
Dec
e
m
b
er
20
20
:
5
7
6
–
5
83
580
T
h
e
g
eo
g
r
ap
h
ical
f
ac
to
r
s
(
lo
ca
lit
y
)
ar
e
lik
el
y
to
a
f
f
ec
t
t
h
e
a
w
ar
e
n
ess
o
f
h
y
p
er
ten
s
io
n
.
P
eo
p
le
w
h
o
s
ta
y
ed
i
n
u
r
b
an
ar
ea
ar
e
m
o
r
e
a
w
ar
e
th
e
n
p
eo
p
le
in
th
e
r
u
r
al
ar
ea
.
T
h
e
f
ac
t
th
at
i
t
is
a
n
u
r
b
an
ar
ea
h
as
g
o
o
d
p
r
ev
en
tio
n
an
d
co
n
tr
o
l
p
r
o
g
r
am
s
to
ac
ce
s
s
b
y
co
m
m
u
n
itie
s
.
A
cc
e
s
s
ib
il
it
y
to
h
ea
l
th
ca
r
e
s
e
r
v
ices
i
n
Ma
la
y
s
ia
h
as
b
ee
n
e
m
p
h
asized
as
th
e
k
e
y
f
o
cu
s
ar
ea
o
f
th
e
Ma
la
y
s
ian
g
o
v
er
n
m
en
t.
I
t
is
th
e
co
n
ce
r
n
o
f
in
eq
u
it
y
o
f
un
b
ala
n
ce
d
d
o
cto
r
p
o
p
u
latio
n
r
atio
i
n
u
r
b
an
a
n
d
r
u
r
al
a
r
ea
.
Ur
b
an
ar
ea
s
h
av
e
m
o
r
e
d
o
cto
r
s
an
d
b
etter
r
eso
u
r
ce
s
as
co
m
p
ar
ed
to
r
u
r
al
ar
ea
s
.
Hen
ce
,
av
ailab
ili
t
y
t
o
ap
t
h
ea
lth
ch
ec
k
-
u
p
i
n
th
e
r
u
r
al
ar
ea
co
u
ld
b
e
in
ad
eq
u
ate.
A
cc
o
r
d
in
g
to
[
1
4
]
,
p
eo
p
le
w
h
o
ar
e
n
o
t
b
ein
g
p
h
y
s
icall
y
a
ctiv
e
a
n
d
p
r
ac
tici
n
g
u
n
h
ea
lt
h
y
li
f
e
s
t
y
le
h
av
e
th
e
te
n
d
en
c
y
to
d
ev
elo
p
h
y
p
er
te
n
s
io
n
.
B
esid
es
th
at,
s
m
o
k
i
n
g
a
n
d
d
r
in
k
in
g
alco
h
o
l
to
o
m
u
ch
ar
e
also
co
n
s
id
er
ed
to
b
e
u
n
h
ea
lt
h
y
li
f
est
y
le
th
at
i
m
m
ed
iate
l
y
r
i
s
e
y
o
u
r
b
lo
o
d
p
r
ess
u
r
e
an
d
in
cr
ea
s
e
h
ea
r
t
d
is
ea
s
e
r
is
k
.
R
eg
u
lar
p
h
y
s
ical
e
x
er
cise
s
u
c
h
as
w
alk
in
g
,
j
o
g
g
in
g
o
r
cy
c
li
n
g
i
s
o
n
e
o
f
th
e
s
tep
s
to
m
a
k
e
th
e
b
lo
o
d
v
ess
els
w
o
r
k
w
ell
a
n
d
m
an
a
g
e
b
o
d
y
w
ei
g
h
t.
O
v
er
w
ei
g
h
t
o
r
o
b
ese
w
o
u
ld
ca
u
s
e
t
h
e
b
o
d
y
to
p
r
o
ce
s
s
m
o
r
e
b
lo
o
d
to
s
u
p
p
l
y
o
x
y
g
en
a
n
d
n
u
tr
ie
n
ts
t
o
th
e
b
o
d
y
an
d
it
w
il
l p
r
ess
u
r
e
o
n
th
e
b
o
d
y
’
s
ar
ter
y
w
alls
.
Fig
u
r
e
3
.
Facto
r
s
o
n
p
r
ev
alen
c
e
o
f
h
y
p
er
te
n
s
io
n
T
ab
le
1
.
A
n
al
y
s
i
s
o
f
f
ac
to
r
s
as
s
o
ciate
d
w
it
h
h
y
p
er
ten
s
io
n
i
n
ad
u
l
t b
ased
o
n
o
th
er
s
co
u
n
tr
ie
s
.
T
h
e
in
co
m
e
a
n
d
ed
u
ca
tio
n
lev
el
h
a
v
e
co
r
r
elatio
n
f
ac
to
r
s
th
a
t
co
u
ld
r
is
k
to
h
y
p
er
te
n
s
io
n
.
P
eo
p
le
w
it
h
g
o
o
d
ed
u
ca
tio
n
an
d
h
i
g
h
-
i
n
co
m
e
le
v
el
co
u
ld
ac
ce
s
s
b
etter
m
ed
ical
ca
r
e
an
d
ta
k
e
a
w
ar
e
n
ess
o
n
th
eir
h
ea
lt
h
p
r
o
b
lem
.
T
h
e
y
ca
n
u
s
e
m
a
n
y
s
o
u
r
ce
s
s
u
c
h
as
i
n
ter
n
et
o
r
b
o
o
k
s
to
r
ea
d
,
u
n
d
er
s
ta
n
d
an
d
ac
t
o
n
th
e
h
ea
lt
h
in
f
o
r
m
atio
n
.
Ho
w
e
v
er
,
lo
w
i
n
co
m
e
s
tat
u
s
w
o
u
ld
ex
p
o
s
e
to
r
estricte
d
ac
ce
s
s
o
f
h
ea
lth
ca
r
e
an
d
u
n
a
w
ar
en
es
s
o
f
r
is
k
s
in
h
y
p
er
te
n
s
io
n
.
4
.
2
.
P
re
dict
iv
e
a
na
ly
t
ics re
v
i
ew
Data
m
i
n
i
n
g
,
s
ta
tis
tic
s
m
o
d
e
llin
g
,
d
ee
p
lear
n
in
g
a
n
d
ar
tif
icial
i
n
telli
g
e
n
ce
ar
e
th
e
ex
a
m
p
le
o
f
tech
n
iq
u
es
o
n
t
h
e
p
r
ed
ictiv
e
an
al
y
tics
.
T
h
er
e
ar
e
s
ev
er
a
l
in
d
u
s
tr
y
a
n
d
s
ec
to
r
s
i
n
cl
u
d
ed
h
ea
lth
ca
r
e
u
s
e
p
r
ed
ictiv
e
an
al
y
tics
in
d
i
f
f
er
en
t
w
a
y
s
to
e
x
tr
ac
t
th
e
v
al
u
ab
le
in
f
o
r
m
atio
n
in
o
r
d
er
to
d
eter
m
in
e
p
atter
n
an
d
p
r
ed
ict
f
u
t
u
r
e
o
u
tco
m
e
s
a
n
d
tr
en
d
s
.
B
ased
o
n
t
h
e
a
n
al
y
s
is
o
f
t
h
e
r
ev
ie
w
ed
p
ap
er
s
,
th
er
e
ar
e
v
ar
io
u
s
tech
n
iq
u
es
t
h
at
h
a
v
e
b
ee
n
co
m
p
iled
in
th
e
w
o
r
k
o
f
o
th
er
a
u
th
o
r
s
o
n
p
r
ed
ictiv
e
an
al
y
tic
s
.
W
e
ca
m
e
o
u
t
w
it
h
12
11
10
8
6
4
3
3
3
2
0
2
4
6
8
10
12
14
Nu
mb
e
r
o
f
A
r
t
i
c
l
e
s
F
a
c
t
o
r
s o
f
H
y
p
e
r
t
e
n
si
o
n
F
a
c
t
o
r
C
o
u
n
t
r
y
M
a
l
a
y
si
a
I
n
d
o
n
e
si
a
T
h
a
i
l
a
n
d
C
h
i
n
a
S
p
a
i
n
K
o
r
e
a
L
e
b
a
n
o
n
T
u
r
k
e
y
I
n
d
i
a
G
e
n
d
e
r
√
√
√
√
√
√
√
√
√
A
g
e
√
√
√
√
√
√
√
√
B
M
I
S
t
a
t
u
s
√
√
√
√
√
√
√
L
o
c
a
l
i
t
y
√
√
√
√
√
√
S
mo
k
e
r
√
√
√
√
√
D
r
i
n
k
e
r
√
√
√
Ed
u
c
a
t
i
o
n
√
√
√
M
a
r
i
t
a
l
S
t
a
t
u
s
√
√
P
h
y
si
c
a
l
A
c
t
i
v
i
t
y
√
√
F
a
mi
l
y
H
i
st
o
r
y
√
√
I
n
c
o
me
L
e
v
e
l
√
√
R
e
f
e
r
e
n
c
e
[
3
,
6
,
15]
[
1
6
]
[
1
7
]
[
1
8
-
1
9
]
[
2
0
]
[
2
1
]
[
2
2
]
[
2
3
]
[
2
4
-
2
5
]
Evaluation Warning : The document was created with Spire.PDF for Python.
I
n
t J
A
r
ti
f
I
n
tell
I
SS
N:
2252
-
8938
P
r
ev
a
len
ce
o
f h
yp
erten
s
io
n
:
p
r
ed
ictive
a
n
a
lytics r
ev
iew
(
N
u
r
A
r
ifa
h
Mo
h
d
N
o
r
)
581
m
o
s
t
co
m
m
o
n
p
r
ed
ictio
n
tec
h
n
iq
u
e
s
u
s
ed
b
y
t
h
e
r
esear
c
h
er
to
b
u
ild
p
r
ed
ictiv
e
m
o
d
el
i
n
h
ea
lth
ca
r
e
as
s
h
o
w
n
at
T
ab
le
2
.
Neu
r
al
n
et
w
o
r
k
,
d
ec
is
io
n
tr
ee
,
n
aï
v
e
b
a
y
e
s
a
n
d
r
eg
r
es
s
io
n
c
l
ass
i
f
ier
ar
e
t
h
e
m
o
s
t
co
m
m
o
n
tech
n
iq
u
e
u
s
ed
i
n
d
ia
g
n
o
s
i
n
g
.
T
h
ese
te
ch
n
iq
u
es
h
a
v
e
th
e
h
i
g
h
est
ac
cu
r
ac
y
th
at
co
m
m
o
n
l
y
u
s
ed
b
y
t
h
e
r
esear
c
h
er
.
B
asicall
y
,
th
e
tec
h
n
iq
u
e
u
s
ed
o
n
p
r
e
d
ictio
n
d
is
ea
s
e
s
u
c
h
as
o
n
h
ea
r
t
d
is
ea
s
e,
d
iab
etes
an
d
liv
er
p
r
ed
ictio
n
u
s
i
n
g
th
e
f
ac
to
r
s
o
f
t
h
e
d
is
ea
s
e.
T
h
e
p
r
ed
ictio
n
m
ad
e
g
i
v
es
i
m
p
ac
t
o
n
t
h
e
d
is
ea
s
e
an
d
i
m
p
r
o
v
e
th
e
t
h
e
co
s
t
o
f
ca
r
e
b
ef
o
r
e
an
d
af
ter
.
T
ab
le
2
s
h
o
w
s
n
e
u
r
al
n
e
t
w
o
r
k
,
d
ec
is
i
o
n
tr
ee
,
n
aïv
e
b
a
y
es
a
n
d
r
eg
r
ess
io
n
ar
e
w
it
h
h
i
g
h
av
er
ag
e
ac
c
u
r
ac
y
.
T
h
e
av
er
a
g
e
ac
cu
r
ac
y
f
o
r
ea
c
h
tec
h
n
iq
u
e
is
ca
lc
u
late
b
y
t
h
e
to
tal
ac
c
u
r
ac
y
d
iv
id
e
b
y
t
h
e
n
u
m
b
er
o
f
r
esear
ch
er
s
u
s
in
g
t
h
e
tech
n
iq
u
e.
Neu
r
al
n
et
w
o
r
k
s
h
o
w
s
t
h
e
b
etter
av
er
ag
e
p
r
ed
ictio
n
ac
cu
r
a
c
y
b
ec
au
s
e
o
f
th
e
lear
n
in
g
weig
h
t
an
d
p
ar
am
eter
s
et
tin
g
d
u
r
i
n
g
t
h
e
ex
ec
u
tio
n
o
f
th
e
al
g
o
r
ith
m
o
n
th
e
d
ata.
T
h
e
f
r
eq
u
en
t
tech
n
iq
u
e
u
s
ed
b
y
r
esear
ch
er
o
n
p
r
ed
ictiv
e
an
al
y
tic
f
o
r
h
ea
lt
h
ca
r
e
is
d
ec
is
io
n
t
r
ee
.
T
h
e
ac
cu
r
ac
y
r
esu
lt
o
n
p
r
ed
ictio
n
r
est
u
p
o
n
th
e
co
r
r
ec
t selec
ted
f
ac
to
r
s
th
a
t
w
er
e
es
s
e
m
b
le
t
h
r
o
u
g
h
o
u
t th
e
p
r
ed
ictio
n
p
r
o
ce
d
u
r
e.
T
ab
le
2
.
C
o
m
m
o
n
tec
h
n
iq
u
e
u
s
ed
in
p
r
ed
ictiv
e
an
al
y
tic
o
n
h
ea
lth
ca
r
e
d
o
m
ain
A
I
T
e
c
h
n
i
q
u
e
s
T
o
p
i
c
P
r
e
d
i
c
t
i
o
n
A
v
e
r
a
g
e
A
c
c
u
r
a
c
y
R
e
f
e
r
e
n
c
e
s
N
e
u
r
a
l
N
e
t
w
o
r
k
Ty
p
e
o
f
D
i
se
a
se
,
H
e
a
r
t
D
i
se
a
se
9
5
.
4
6
%
[
2
6
-
2
7
]
D
e
c
i
si
o
n
T
r
e
e
H
e
a
r
t
D
i
se
a
se
,
D
i
a
b
e
t
e
s,
L
i
v
e
r
D
i
se
a
s
e
8
4
.
7
5
%
[
9
,
27
-
3
1
]
N
a
ï
v
e
B
a
y
e
s
H
e
a
r
t
D
i
se
a
se
8
7
.
5
0
%
[9
,
27
,
31
-
3
3
]
S
V
M
H
e
a
r
t
D
i
se
a
se
8
5
.
1
9
%
[
3
2
,
34]
L
o
g
i
st
i
c
R
e
g
r
e
ssi
o
n
D
i
a
b
e
t
e
s
9
0
.
5
0
%
[9
,
28
,
32
,
3
5
]
Ho
w
e
v
er
,
th
e
s
tr
u
ct
u
r
e
o
f
th
e
d
ata
also
af
f
ec
t
s
th
e
p
er
f
o
r
m
an
ce
o
f
t
h
e
alg
o
r
it
h
m
.
T
h
e
f
i
n
d
in
g
als
o
f
o
u
n
d
t
h
at,
t
h
er
e
ar
e
r
esear
ch
er
s
w
h
o
e
x
p
lo
r
ed
h
y
b
r
id
p
r
ed
ictiv
e
a
n
al
y
tic
tech
n
iq
u
e
s
.
T
h
e
a
m
al
g
a
m
atio
n
o
f
d
ata
m
in
in
g
tec
h
n
iq
u
es
h
elp
s
t
o
in
cr
ea
s
e
p
er
f
o
r
m
a
n
ce
o
f
t
h
e
p
r
ed
ictiv
e
m
o
d
el.
Mo
r
eo
v
er
,
r
esear
ch
er
also
tr
ies
to
u
s
e
d
i
f
f
er
e
n
t
p
ar
a
m
e
ter
s
ettin
g
o
n
th
eir
m
o
d
ellin
g
to
in
cr
ea
s
e
ac
c
u
r
ac
y
o
f
t
h
e
p
r
ed
ictio
n
.
T
h
u
s
,
t
h
e
co
m
b
i
n
atio
n
o
f
t
h
e
tech
n
iq
u
e
s
m
a
y
i
m
p
r
o
v
e
t
h
e
p
r
ed
ictio
n
m
o
d
el
a
n
d
co
m
e
o
u
t
w
it
h
b
ett
er
ac
cu
r
ac
y
.
5.
CO
NCLU
SI
O
N
T
h
e
r
ev
ie
w
p
ap
er
f
o
cu
s
o
n
t
h
e
p
r
ed
ictiv
e
a
n
al
y
tic
tec
h
n
iq
u
es
a
n
d
t
h
e
p
r
ev
ale
n
ce
o
n
h
y
p
er
ten
s
io
n
a
m
o
n
g
ad
u
lt
as
w
ell
a
s
th
e
f
ac
to
r
s
b
ased
o
n
s
ev
er
al
co
u
n
tr
ie
s
an
d
th
e
a
w
ar
en
e
s
s
o
f
h
y
p
er
t
en
s
io
n
.
I
n
b
r
ief
,
th
e
r
ate
o
f
th
e
a
w
ar
en
e
s
s
i
s
d
if
f
er
en
t
b
y
t
h
e
f
ac
to
r
s
a
s
s
o
ciate
d
a
m
o
n
g
t
h
e
p
eo
p
le.
B
ased
o
n
th
e
r
ev
ie
w
,
w
e
ca
n
p
r
ev
en
t
a
m
aj
o
r
h
ea
lth
is
s
u
e
a
n
d
co
m
p
licat
io
n
s
t
h
at
co
n
tr
ib
u
te
to
th
e
d
is
ea
s
e
i
f
w
e
id
en
ti
f
y
ea
r
l
y
t
h
e
f
ac
to
r
s
th
at
a
f
f
ec
ted
t
h
e
h
ea
lt
h
i
s
s
u
es
.
W
e
id
en
ti
f
y
1
1
f
ac
to
r
s
o
f
h
y
p
er
ten
s
io
n
t
h
at
ar
e
s
ig
n
i
f
ica
n
t
a
n
d
r
elev
a
n
ce
to
b
e
u
s
ed
a
s
attr
ib
u
te
to
b
u
ild
th
e
p
r
ed
ictiv
e
m
o
d
el.
T
h
e
f
ac
to
r
s
ar
e
g
en
d
er
,
a
g
e,
B
MI
s
tatu
s
,
lo
ca
lit
y
,
s
m
o
k
er
s
tatu
s
,
d
r
in
k
er
s
tat
u
s
,
ed
u
ca
t
i
o
n
,
m
ar
ital
s
tat
u
s
,
p
h
y
s
ica
l
ac
tiv
it
y
,
f
a
m
il
y
h
i
s
to
r
y
an
d
i
n
co
m
e
le
v
el.
T
h
ese
f
ac
to
r
s
ar
e
m
o
s
t
d
is
c
u
s
s
ed
in
9
co
u
n
tr
ies
w
h
ic
h
ar
e
Ma
la
y
s
ia,
I
n
d
o
n
esia,
T
h
aila
n
d
,
C
h
in
a,
Sp
ai
n
,
Ko
r
ea
,
L
eb
an
o
n
,
T
u
r
k
e
y
an
d
I
n
d
ia.
Mo
r
eo
v
er
,
th
e
c
h
o
ice
o
f
s
i
g
n
if
ica
n
t
tec
h
n
iq
u
e
is
i
m
p
o
r
tan
t
b
esid
e
th
e
d
ata
to
co
m
e
o
u
t
w
i
th
s
i
g
n
if
ican
t
p
r
e
d
ictiv
e
m
o
d
el.
Ne
u
r
al
n
et
w
o
r
k
,
d
ec
is
io
n
tr
ee
,
r
eg
r
es
s
io
n
a
n
d
n
aïv
e
b
a
y
es
ar
e
th
e
s
u
g
g
e
s
ted
tec
h
n
iq
u
es
in
s
h
ap
in
g
g
o
o
d
p
r
ed
ictio
n
m
o
d
el.
I
n
ad
d
itio
n
,
d
is
cu
s
s
io
n
ab
o
u
t
t
h
e
a
w
ar
e
n
ess
b
et
w
ee
n
th
e
f
ac
to
r
s
w
er
e
a
ls
o
p
r
esen
te
d
in
t
h
e
d
is
c
u
s
s
io
n
i
n
o
r
d
er
to
co
m
e
o
u
t
w
it
h
s
ig
n
i
f
ica
n
t
v
ar
iab
les
th
at
ca
n
b
e
u
s
e
i
n
t
h
e
d
ataset
to
i
m
p
r
o
v
e
th
e
p
er
f
o
r
m
a
n
ce
m
o
d
elli
n
g
.
T
h
e
i
m
p
o
r
tan
t
ch
alle
n
g
es
i
n
p
r
ed
ictiv
e
an
al
y
tic
s
i
s
to
b
u
ild
p
r
ec
is
e
an
d
c
o
m
p
u
t
atio
n
all
y
e
f
f
icie
n
t
m
o
d
el
f
o
r
Me
d
ical
ap
p
licatio
n
.
I
n
c
o
n
clu
s
io
n
,
th
e
r
ev
ie
w
o
b
j
ec
tiv
e
to
u
n
d
er
s
ta
n
d
t
h
e
co
r
r
elatio
n
a
m
o
n
g
th
e
r
elate
d
f
ac
to
r
s
a
n
d
r
ev
ie
w
o
n
t
h
e
p
r
ed
ictiv
e
a
n
al
y
tic
tech
n
iq
u
es i
n
t
h
e
h
ea
lt
h
ca
r
e
ca
n
b
e
en
h
a
n
ce
d
f
o
r
f
u
t
u
r
e
w
o
r
k
.
ACK
NO
WL
E
D
G
E
M
E
NT
S
W
e
w
o
u
ld
li
k
e
to
e
x
p
r
ess
o
u
r
th
an
k
s
to
t
h
e
Facu
l
t
y
o
f
C
o
m
p
u
ter
an
d
Ma
th
e
m
atica
l
Scie
n
ce
s
,
UiT
M
f
o
r
s
u
p
p
o
r
t
r
en
d
er
ed
th
u
s
f
ar
,
I
n
s
tit
u
te
o
f
Gr
ad
u
ate
S
tu
d
y
,
U
iT
M
f
o
r
f
u
n
d
i
n
g
an
d
to
a
n
o
n
y
m
o
u
s
r
ev
ie
w
er
s
f
o
r
th
eir
u
s
ef
u
l s
u
g
g
esti
o
n
s
.
RE
F
E
R
E
NC
E
S
[1
]
A
.
A
n
n
u
a
r,
“
In
2
0
1
9
,
He
a
lt
h
M
in
i
str
y
re
so
lv
e
s to
c
u
t
sm
o
k
in
g
,
h
y
p
e
rten
sio
n
,
o
b
e
sity
a
n
d
m
o
re
,
”
M
a
l
a
y
M
a
il
,
2
0
1
9
.
[2
]
G
.
J
a
g
a
d
e
e
sh
,
P
.
Ba
lak
u
m
a
r,
a
n
d
K.
M
a
u
n
g
-
U,
“
P
re
f
a
c
e
,
”
in
Pa
t
h
o
p
h
y
sio
l
o
g
y
a
n
d
P
h
a
r
ma
c
o
th
e
ra
p
y
o
f
Ca
rd
io
v
a
sc
u
la
r Dise
a
se
,
S
p
ri
n
g
e
r
In
tern
a
ti
o
n
a
l
P
u
b
li
sh
i
n
g
S
w
it
z
e
rlan
d
,
p
p
.
6
3
5
-
6
5
3
,
2
0
1
5
.
[3
]
C.
Na
in
g
,
P
.
N.
Ye
o
h
,
V
.
N.
W
a
i,
N.
N.
W
in
,
L
.
P
.
Ku
a
n
,
a
n
d
K.
A
u
n
g
,
“
Hy
p
e
rten
sio
n
i
n
M
a
lay
sia
A
n
A
n
a
l
y
sis
o
f
Evaluation Warning : The document was created with Spire.PDF for Python.
I
SS
N
:
2
2
5
2
-
8938
I
n
t J
A
r
ti
f
I
n
tell
,
Vo
l.
9
,
No
.
4
,
Dec
e
m
b
er
20
20
:
5
7
6
–
5
83
582
T
re
n
d
s F
ro
m
th
e
Na
ti
o
n
a
l
S
u
rv
e
y
s 1
9
9
6
t
o
2
0
1
1
,
”
M
e
d
ici
n
e
(
Ba
lt
i
mo
re
).
,
v
o
l.
9
5
,
n
o
.
2
,
p
p
.
1
-
7
,
2
0
1
6
.
[4
]
W
.
Ra
g
h
u
p
a
th
i
a
n
d
V.
Ra
g
h
u
p
a
t
h
i,
“
Big
d
a
ta
a
n
a
ly
ti
c
s
in
h
e
a
lt
h
c
a
re
:
p
ro
m
ise
a
n
d
p
o
ten
ti
a
l
,
”
He
a
l.
In
f.
S
c
i.
S
y
st.
,
2
0
1
4
.
[5
]
Y.
W
a
n
g
,
L
.
A
.
Ku
n
g
,
a
n
d
T
.
A
.
B
y
rd
,
“
Big
d
a
ta
a
n
a
ly
ti
c
s:
Un
d
e
rsta
n
d
i
n
g
it
s
c
a
p
a
b
il
it
ies
a
n
d
p
o
ten
t
ial
b
e
n
e
f
it
s
f
o
r
h
e
a
lt
h
c
a
re
o
rg
a
n
iza
ti
o
n
s,”
T
e
c
h
n
o
l.
Fo
re
c
a
st.
S
o
c
.
Ch
a
n
g
e
,
2
0
1
6
.
[6
]
B.
M
a
h
a
d
ir
e
t
a
l
.
,
“
F
a
c
to
rs as
so
c
i
a
ted
w
it
h
th
e
se
v
e
rit
y
o
f
h
y
p
e
rten
sio
n
a
m
o
n
g
M
a
lay
si
a
n
a
d
u
lt
s,”
P
L
o
S
On
e
,
2
0
1
9
.
[7
]
S
.
A
b
d
u
l
-
ra
z
a
k
e
t
a
l.
,
“
P
re
v
a
len
c
e
,
a
w
a
r
e
n
e
ss
,
trea
t
m
e
n
t,
c
o
n
tro
l
a
n
d
so
c
io
d
e
m
o
g
ra
p
h
ic
d
e
term
in
a
n
ts
o
f
h
y
p
e
rten
sio
n
in
M
a
lay
sia
n
a
d
u
lt
s,
”
BM
C
Pu
b
li
c
He
a
lt
h
,
2
0
1
6
.
[8
]
C.
S
h
a
rm
a
,
“
Big
Da
ta
A
n
a
l
y
ti
c
s
Us
in
g
Ne
u
ra
l
n
e
tw
o
rk
s,
”
S
a
n
Jo
se
S
tate
Un
iv
e
rsity
,
2
0
1
4
.
[9
]
R.
P
ra
sa
d
,
P
.
A
n
jali,
S
.
A
d
il
,
a
n
d
N.
De
e
p
a
,
“
He
a
rt
Dis
e
a
se
P
re
d
ictio
n
u
sin
g
L
o
g
isti
c
Re
g
re
s
sio
n
Alg
o
rit
h
m
u
sin
g
M
a
c
h
in
e
L
e
a
rn
in
g
,
”
In
t.
J
.
En
g
.
A
d
v
.
T
e
c
h
n
o
l.
,
v
o
l.
8
,
n
o
.
3
,
p
p
.
6
5
9
-
6
6
2
,
2
0
1
9
.
[1
0
]
K.
P
o
o
n
siriv
o
n
g
a
n
d
C
.
Jitt
a
w
iri
a
y
n
u
k
o
o
n
,
“
Big
d
a
ta
a
n
a
ly
ti
c
s
u
sin
g
a
ss
o
c
iatio
n
ru
les
i
n
e
L
e
a
r
n
in
g
Big
Da
ta
A
n
a
l
y
ti
c
s U
sin
g
As
so
c
iatio
n
Ru
les
in
e
L
e
a
rn
in
g
,
”
Res
e
a
rc
h
Ga
te
,
p
p
.
1
4
-
1
8
,
2
0
1
8
.
[1
1
]
M
.
A
.
A
l
-
Ha
g
e
r
y
,
“
Ex
trac
ti
n
g
h
id
d
e
n
p
a
tt
e
rn
s
f
ro
m
d
a
tes
’
p
ro
d
u
c
t
d
a
ta
u
si
n
g
a
m
a
c
h
in
e
lea
rn
in
g
t
e
c
h
n
iq
u
e
,
”
IAE
S
In
t.
J
.
Arti
f.
I
n
tell.
,
v
o
l.
8
,
n
o
.
3
,
p
p
.
2
0
5
-
2
1
4
,
2
0
1
9
.
[1
2
]
M
.
Y.
I.
Ba
sh
e
e
r,
S
.
M
u
talib
,
N.
H.
A
.
Ha
m
id
,
S
.
A
b
d
u
l
-
Ra
h
m
a
n
,
a
n
d
A
.
M
.
A
.
M
a
li
k
,
“
P
re
d
ict
iv
e
a
n
a
l
y
ti
c
s
o
f
u
n
iv
e
rsity
stu
d
e
n
t
i
n
tak
e
u
sin
g
su
p
e
rv
ise
d
m
e
th
o
d
s,”
IAE
S
I
n
t.
J
.
A
rtif
.
In
tel
l.
,
v
o
l
.
8
,
n
o
.
4
,
p
p
.
3
6
7
-
3
7
4
,
2
0
1
9
.
[1
3
]
K.
C.
Ra
n
i
a
n
d
Y.
P
ra
sa
n
t
h
,
“
A
De
c
isio
n
S
y
ste
m
f
o
r
P
re
d
icti
n
g
Dia
b
e
tes
u
sin
g
Ne
u
ra
l
Ne
tw
o
rk
s,”
IAE
S
In
t.
J
.
Arti
f.
In
tell.
,
v
o
l.
6
,
n
o
.
2
,
p
.
5
6
,
2
0
1
7
.
[1
4
]
D.
Ra
m
e
sh
a
n
d
C.
S
.
S
c
ien
c
e
,
“
L
a
c
k
o
f
e
x
e
r
c
ise
a
n
d
c
h
ro
n
ic
d
i
se
a
se
,
”
Co
mp
r.
Ph
y
si
o
l.
,
v
o
l.
5
,
n
o
.
2
,
p
p
.
1
5
9
-
1
6
9
,
1
9
9
7
.
[1
5
]
M
.
A
.
Om
a
r
e
t
a
l.
,
“
P
re
v
a
len
c
e
o
f
Yo
u
n
g
A
d
u
lt
Hy
p
e
rten
sio
n
i
n
M
a
lay
sia
a
n
d
it
s
A
ss
o
c
iate
d
F
a
c
to
rs :
F
i
n
d
i
n
g
s
F
ro
m
Na
ti
o
n
a
l
He
a
lt
h
a
n
d
M
o
rb
i
d
it
y
S
u
rv
e
y
2
0
1
1
,
”
M
a
la
y
sia
n
J
.
Pu
b
li
c
He
a
l.
M
e
d
.
,
v
o
l.
1
6
,
n
o
.
3
,
p
p
.
2
7
4
-
2
8
3
,
2
0
1
6
.
[1
6
]
K.
P
e
lt
z
e
r,
“
T
h
e
P
re
v
a
len
c
e
a
n
d
S
o
c
ial
De
term
in
a
n
ts
o
f
H
y
p
e
r
ten
sio
n
a
m
o
n
g
A
d
u
lt
s
in
I
n
d
o
n
e
sia
:
A
Cro
ss
-
S
e
c
ti
o
n
a
l
P
o
p
u
lati
o
n
-
Ba
se
d
Na
ti
o
n
a
l
S
u
rv
e
y
,
”
Hin
d
a
wi
I
n
t.
J
.
Hy
p
e
rte
n
s.
,
2
0
1
8
.
[1
7
]
S
.
L
e
rtt
h
a
n
a
p
o
r
n
,
K.
S
u
w
a
n
th
ip
,
C.
S
o
n
g
sa
e
n
g
t
h
u
m
,
R.
Ra
n
g
sin
,
a
n
d
B.
S
.
Id
,
“
P
re
v
a
len
c
e
a
n
d
a
ss
o
c
iate
d
f
a
c
to
rs o
f
u
n
c
o
n
tro
ll
e
d
b
l
o
o
d
p
re
ss
u
re
a
m
o
n
g
h
y
p
e
rten
siv
e
p
a
ti
e
n
ts
in
th
e
ru
ra
l
c
o
m
m
u
n
it
ies
in
th
e
c
e
n
tral
a
re
a
s
in
T
h
a
il
a
n
d
:
A
c
ro
ss
-
s
e
c
ti
o
n
a
l
stu
d
y
,
”
PL
o
S
O
n
e
,
p
p
.
1
-
1
4
,
2
0
1
9
.
[1
8
]
J.
W
a
n
g
e
t
a
l.
,
“
Di
ff
e
r
e
n
c
e
s in
p
r
e
v
a
len
c
e
o
f
h
y
p
e
rten
sio
n
a
n
d
a
ss
o
c
iate
d
risk
f
a
c
to
rs i
n
u
rb
a
n
a
n
d
r
u
ra
l
re
sid
e
n
ts o
f
th
e
n
o
rth
e
a
ste
rn
re
g
io
n
o
f
th
e
P
e
o
p
le ’
s Re
p
u
b
li
c
o
f
Ch
in
a
:
A
c
ro
s
s
-
se
c
ti
o
n
a
l
stu
d
y
,
”
PL
o
S
On
e
,
p
p
.
1
-
1
4
,
2
0
1
8
.
[1
9
]
B.
L
iu
e
t
a
l.
,
“
A
Co
m
p
a
riso
n
o
n
P
re
v
a
len
c
e
o
f
H
y
p
e
rt
e
n
sio
n
a
n
d
Re
late
d
Ris
k
F
a
c
to
rs
b
e
t
we
e
n
Is
lan
d
a
n
d
Ru
ra
l
Re
sid
e
n
ts o
f
Da
li
a
n
Cit
y
,
Ch
in
a
,
”
Hin
d
a
wi
I
n
t.
J
.
Hy
p
e
rte
n
s.
,
v
o
l.
2
0
1
9
,
p
p
.
1
-
9
,
2
0
1
9
.
[2
0
]
A
.
Co
rb
a
tó
n
-
a
n
c
h
u
e
l
o
,
M
.
T
.
M
a
rtí
n
e
z
-
larra
d
,
N.
P
ra
d
o
-
g
o
n
z
á
lez
,
C.
F
e
rn
á
n
d
e
z
-
p
é
re
z
,
R.
G
a
b
riel,
a
n
d
M
.
S
e
rra
n
o
-
río
s,
“
P
re
v
a
len
c
e
,
T
r
e
a
t
m
e
n
t,
a
n
d
A
ss
o
c
iate
d
F
a
c
to
rs
o
f
H
y
p
e
rte
n
sio
n
in
S
p
a
in
:
A
Co
m
p
a
ra
ti
v
e
S
tu
d
y
b
e
tw
e
e
n
P
o
p
u
latio
n
s,”
Hin
d
a
w
i
In
t.
J
.
Hy
p
e
rte
n
s.
,
v
o
l.
2
0
1
8
,
2
0
1
8
.
[2
1
]
S
.
Ka
n
g
e
t
a
l
.
,
“
P
re
v
a
len
c
e
,
Aw
a
re
n
e
ss
,
T
re
a
t
m
e
n
t,
a
n
d
Co
n
tr
o
l
o
f
H
y
p
e
rten
sio
n
i
n
Ko
re
a
,
”
S
c
i.
Rep
.
,
p
p
.
3
-
1
0
,
2
0
1
9
.
[2
2
]
D.
M
a
tar,
A
.
H.
F
ra
n
g
ieh
,
S
.
A
b
o
u
a
ss
i,
a
n
d
F
.
Bteic
h
,
“
P
re
v
a
len
c
e
,
Aw
a
re
n
e
ss
,
T
re
a
t
m
e
n
t,
a
n
d
Co
n
tro
l
o
f
H
y
p
e
rten
sio
n
i
n
L
e
b
a
n
o
n
,
”
P
u
b
li
c
He
a
l.
Fo
c
u
s
,
p
p
.
3
8
1
-
3
8
8
,
2
0
1
5
.
[2
3
]
İ.
Da
şta
n
,
A
.
Erem
,
a
n
d
V
.
Çe
ti
n
k
a
y
a
,
“
Urb
a
n
a
n
d
ru
ra
l
d
if
f
e
re
n
c
e
s
in
h
y
p
e
rten
sio
n
risk
f
a
c
to
rs
in
T
u
rk
e
y
,
”
T
u
rk
ish
S
o
c
.
C
a
rd
i
o
l.
,
p
p
.
3
9
-
4
7
,
2
0
1
7
.
[2
4
]
C.
M
it
ra
,
M
.
L
a
l,
T
.
S
in
g
h
,
a
n
d
S
.
S
.
De
e
p
ti
,
“
P
re
v
a
len
c
e
a
n
d
ro
le
o
f
risk
fa
c
to
rs
f
o
r
h
y
p
e
r
ten
sio
n
in
1
8
-
6
9
y
e
a
rs
o
f
a
g
e
in
ru
ra
l
a
n
d
u
rb
a
n
a
re
a
s
o
f
d
istri
c
t
Am
rit
sa
r,
P
u
n
ja
b
,
In
d
ia,”
In
t.
J
.
Co
mm
u
n
it
y
M
e
d
.
Pu
b
li
c
He
a
l.
,
v
o
l.
4
,
n
o
.
2
,
p
p
.
4
6
0
-
4
6
4
,
2
0
1
7
.
[2
5
]
B.
M
o
h
a
n
e
t
a
l.
,
“
P
re
v
a
len
c
e
o
f
su
sta
in
e
d
h
y
p
e
rten
sio
n
a
n
d
o
b
e
si
ty
a
m
o
n
g
u
rb
a
n
a
n
d
r
u
ra
l
a
d
o
les
c
e
n
ts :
a
sc
h
o
o
l
-
b
a
se
d
,
c
ro
ss
-
se
c
ti
o
n
a
l
stu
d
y
in
N
o
rth
I
n
d
ia,”
BM
J
Op
e
n
,
p
p
.
1
-
9
,
2
0
1
9
.
[2
6
]
C.
W
e
n
g
,
T
.
C.
Hu
a
n
g
,
a
n
d
R.
Ha
n
,
“
Dise
a
se
p
re
d
ictio
n
w
it
h
d
if
fe
re
n
t
t
y
p
e
s
o
f
n
e
u
ra
l
n
e
t
wo
rk
c
las
si
f
iers
,
”
T
e
lem
a
t.
In
fo
rm
a
ti
c
s
,
v
o
l.
3
3
,
n
o
.
2
,
p
p
.
2
7
7
-
2
9
2
,
2
0
1
6
.
[2
7
]
A
.
T
a
n
e
ja,
“
He
a
rt
Dise
a
se
P
re
d
i
c
ti
o
n
S
y
ste
m
Us
in
g
Da
ta
M
in
in
g
T
e
c
h
n
iq
u
e
s,”
Or
ien
t.
J
.
C
o
mp
u
t
.
S
c
i.
T
e
c
h
n
o
l.
,
v
o
l.
6
,
n
o
.
4
,
p
p
.
4
5
7
-
4
6
6
,
2
0
1
3
.
[2
8
]
S
.
M
a
n
n
a
,
S
.
M
a
it
y
,
S
.
M
u
n
s
h
i,
a
n
d
M
.
A
d
h
ik
a
ri,
“
Dia
b
e
tes
P
re
d
ictio
n
M
o
d
e
l
Us
in
g
Clo
u
d
A
n
a
ly
ti
c
s,”
In
t.
Co
n
f.
Ad
v
.
Co
m
p
u
t
.
Co
mm
u
n
.
I
n
f
o
rm
a
ti
c
s
,
p
p
.
3
0
-
3
6
,
2
0
1
8
.
[2
9
]
N.
Na
h
a
r
a
n
d
F
.
A
ra
,
“
L
iv
e
r
Dis
e
a
se
P
re
d
icti
o
n
b
y
u
sin
g
Dif
f
e
re
n
t
De
c
isio
n
T
re
e
T
e
c
h
n
iq
u
e
s,”
I
n
t.
J
.
Da
t
a
M
i
n
.
Kn
o
wl.
M
a
n
a
g
.
Pro
c
e
ss
,
v
o
l.
8
,
n
o
.
2
,
p
p
.
1
-
9
,
2
0
1
8
.
[3
0
]
J.
M
a
g
u
ire
a
n
d
V.
Dh
a
r,
“
Co
m
p
a
ra
ti
v
e
e
ff
e
c
ti
v
e
n
e
ss
f
o
r
o
ra
l
a
n
ti
-
d
iab
e
ti
c
trea
tm
e
n
ts
a
m
o
n
g
n
e
w
l
y
d
iag
n
o
se
d
ty
p
e
2
d
ia
b
e
ti
c
s :
d
a
ta
-
d
riv
e
n
p
re
d
ictiv
e
a
n
a
ly
ti
c
s in
h
e
a
lt
h
c
a
re
,
”
He
a
l.
S
y
st.
,
v
o
l.
2
,
p
p
.
7
3
-
9
2
,
2
0
1
3
.
[3
1
]
K.
G
.
Ka
m
a
ra
j
a
n
d
S
.
P
riy
a
a
,
“
M
u
lt
i
Dise
a
se
P
re
d
ictio
n
u
sin
g
D
a
ta
M
in
i
n
g
T
e
c
h
n
iq
u
e
s,”
I
n
t.
J
.
S
y
st.
S
o
ft
w.
En
g
.
,
v
o
l.
1
0
,
n
o
.
2
,
p
p
.
5
2
0
-
5
2
8
,
2
0
1
6
.
[3
2
]
M
.
S
.
A
m
in
,
Y.
K.
Ch
iam
,
a
n
d
K.
D.
V
a
ra
th
a
n
,
“
Id
e
n
ti
f
ica
ti
o
n
o
f
sig
n
if
ica
n
t
fe
a
tu
re
s
a
n
d
d
a
ta
m
in
in
g
tec
h
n
iq
u
e
s
i
n
p
re
d
ictin
g
h
e
a
rt
d
ise
a
se
,
”
T
e
lem
a
t.
In
f
o
rm
a
ti
c
s
,
v
o
l
.
3
6
,
p
p
.
8
2
-
9
3
,
2
0
1
8
.
[3
3
]
F
.
Hu
a
n
g
,
S
.
W
a
n
g
,
a
n
d
C.
C.
Ch
a
n
,
“
P
re
d
ictin
g
d
ise
a
se
b
y
u
sin
g
d
a
ta
m
in
in
g
b
a
se
d
o
n
h
e
a
lt
h
c
a
re
in
f
o
r
m
a
ti
o
n
s
y
ste
m
,
”
2
0
1
2
IEE
E
In
t.
C
o
n
f.
Gr
a
n
u
l.
C
o
mp
u
t.
,
p
p
.
1
9
1
-
1
9
4
,
2
0
1
2
.
[3
4
]
W
.
Yu
,
T
.
L
iu
,
R.
V
a
ld
e
z
,
M
.
Gw
in
n
,
a
n
d
M
.
J.
Kh
o
u
ry
,
“
A
p
p
li
c
a
ti
o
n
o
f
su
p
p
o
r
t
v
e
c
to
r
m
a
c
h
in
e
m
o
d
e
li
n
g
f
o
r
p
re
d
ictio
n
o
f
c
o
m
m
o
n
d
ise
a
se
s:
t
h
e
c
a
se
o
f
d
iab
e
tes
a
n
d
p
re
-
d
iab
e
t
e
s,”
BM
C
M
e
d
.
In
fo
rm
.
De
c
is.
M
a
k
.
,
v
o
l.
1
0
,
n
o
.
Evaluation Warning : The document was created with Spire.PDF for Python.
I
n
t J
A
r
ti
f
I
n
tell
I
SS
N:
2252
-
8938
P
r
ev
a
len
ce
o
f h
yp
erten
s
io
n
:
p
r
ed
ictive
a
n
a
lytics r
ev
iew
(
N
u
r
A
r
ifa
h
Mo
h
d
N
o
r
)
583
1
6
,
2
0
1
0
.
[3
5
]
A
.
A
.
A
lj
u
m
a
h
,
M
.
G
.
A
h
a
m
a
d
,
a
n
d
M
.
K.
S
id
d
iq
u
i,
“
P
re
d
ictiv
e
A
n
a
l
y
si
s
o
n
Hy
p
e
rten
sio
n
T
re
a
tme
n
t
Us
in
g
Da
ta
M
in
i
n
g
A
p
p
ro
a
c
h
i
n
S
a
u
d
i
A
ra
b
ia,”
In
tell.
I
n
f.
M
a
n
a
g
.
,
v
o
l.
3
,
p
p
.
2
5
2
-
2
6
1
,
2
0
1
1
.
B
I
O
G
RAP
H
I
E
S
O
F
AUTH
O
RS
Nu
r
A
rifah
M
o
h
d
No
r
re
c
e
iv
e
d
h
e
r
b
a
c
h
e
lo
r’s
d
e
g
re
e
o
f
In
telli
g
e
n
t
S
y
ste
m
in
2
0
1
8
f
ro
m
Un
iv
e
rsiti
T
e
k
n
o
lo
g
i
M
A
R
A
(Ui
T
M
).
Cu
rre
n
tl
y
,
sh
e
is
a
m
a
ste
r’s
d
e
g
re
e
stu
d
e
n
t
o
f
In
f
o
rm
a
ti
o
n
S
y
st
e
m
(In
telli
g
e
n
t
S
y
ste
m
s)
a
t
th
e
Ce
n
ter
o
f
In
f
o
rm
a
ti
o
n
S
y
ste
m
S
tu
d
ies
,
Un
iv
e
rsiti
T
e
k
n
o
lo
g
i
M
a
ra
(UiT
M
)
S
h
a
h
A
la
m
,
S
e
lan
g
o
r
,
M
a
lay
sia
.
A
z
li
n
a
h
M
o
h
a
m
e
d
is
a
P
ro
f
e
ss
o
r
in
th
e
In
f
o
rm
a
ti
o
n
S
y
ste
m
s
Ce
n
ter
o
f
S
tu
d
ies
a
t
th
e
Un
iv
e
rsiti
o
f
T
e
k
n
o
lo
g
i
M
A
RA
o
f
M
a
la
y
sia
.
S
h
e
h
o
l
d
s
a
M
.
S
c
.
(A
rti
f
ic
ial
In
telli
g
e
n
c
e
)
f
ro
m
Bristo
l
Un
iv
e
rsit
y
a
n
d
P
h
D
(De
c
isio
n
S
u
p
p
o
rt
S
y
ste
m
s)
f
ro
m
Na
ti
o
n
a
l
Un
iv
e
rsit
y
o
f
M
a
la
y
sia
.
P
rio
r
to
th
is
sh
e
w
a
s
a
tu
to
r
in
Un
iv
e
rsity
o
f
Bristo
l
a
n
d
a
Re
se
a
rc
h
F
e
ll
o
w
in
Na
ti
o
n
a
l
Un
iv
e
rsity
o
f
M
a
la
y
sia
.
He
r
c
u
rre
n
t
re
se
a
rc
h
in
tere
sts
a
re
in
th
e
a
re
a
s
o
f
Big
Da
ta,
S
o
f
t
Co
m
p
u
ti
n
g
,
A
rti
f
icia
l
In
telli
g
e
n
c
e
,
a
n
d
W
e
b
-
b
a
se
d
De
c
isio
n
S
u
p
p
o
rt
S
y
ste
m
s
u
sin
g
in
telli
g
e
n
t
a
g
e
n
ts
in
e
lec
tro
n
ic
g
o
v
e
rn
m
e
n
t
a
p
p
li
c
a
ti
o
n
s.
S
h
e
h
a
s
p
u
b
li
s
h
e
d
w
e
ll
o
v
e
r
1
4
0
p
e
e
r
-
re
fe
re
e
d
jo
u
rn
a
ls,
c
o
n
f
e
re
n
c
e
p
u
b
li
c
a
ti
o
n
s
a
n
d
b
o
o
k
c
h
a
p
ters
in
tern
a
ti
o
n
a
ll
y
a
n
d
l
o
c
a
ll
y
.
Be
sid
e
s
th
a
t,
sh
e
h
a
s
a
lso
c
o
n
tr
ib
u
ted
a
s
a
n
e
x
a
m
in
e
r
a
n
d
re
v
ie
w
e
r
to
m
a
n
y
c
o
n
fe
re
n
c
e
s,
jo
u
rn
a
ls
a
n
d
u
n
iv
e
rsiti
e
s
a
c
a
d
e
m
i
c
a
c
ti
v
it
ies
.
I
n
a
d
d
it
i
o
n
,
sh
e
h
a
d
a
lso
h
e
ld
a
d
m
in
istratio
n
p
o
st
p
e
rtain
in
g
t
o
a
c
a
d
e
m
ic
d
e
v
e
lo
p
m
e
n
t
a
t
th
e
u
n
iv
e
rsity
le
v
e
l
a
s
H
e
a
d
o
f
A
c
a
d
e
m
ic
De
v
e
lo
p
m
e
n
t,
S
p
e
c
ial
O
ffice
r
o
n
Ac
a
d
e
m
ic
Affa
irs
a
n
d
De
v
e
lo
p
m
e
n
t
to
t
h
e
V
ice
Ch
a
n
c
e
l
lo
r
a
n
d
De
a
n
o
f
th
e
F
a
k
u
lt
i
S
a
in
s Ko
m
p
u
ter
d
a
n
M
a
tem
a
ti
k
f
o
r
7
y
e
a
r
s.
S
o
f
ian
it
a
M
u
talib
is a se
n
io
r
lec
tu
re
r
o
f
In
f
o
r
m
a
ti
o
n
S
y
ste
m
in
Un
iv
e
rsiti
T
e
k
n
o
lo
g
i
M
A
R
A
.
S
h
e
re
c
e
iv
e
d
a
m
a
ste
r’s
d
e
g
r
e
e
in
In
fo
rm
a
ti
o
n
T
e
c
h
n
o
lo
g
y
f
ro
m
th
e
N
a
ti
o
n
a
l
Un
iv
e
rsity
o
f
M
a
la
y
sia
in
1
9
9
8
.
S
h
e
tea
c
h
e
s
c
o
u
rse
s
re
la
ted
to
In
telli
g
e
n
t
S
y
ste
m
s
su
c
h
a
s
in
telli
g
e
n
t
s
y
ste
m
d
e
v
e
lo
p
m
e
n
t,
d
e
c
isio
n
su
p
p
o
rt
sy
ste
m
s
a
n
d
d
a
ta
m
in
in
g
.
He
r
p
rima
r
y
re
se
a
rc
h
in
tere
sts
in
v
o
lv
e
in
telli
g
e
n
t
sy
ste
m
s,
d
a
ta
m
in
in
g
a
s w
e
ll
a
s
m
a
c
h
in
e
lea
rn
in
g
.
Evaluation Warning : The document was created with Spire.PDF for Python.