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I
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a
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c
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in
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(
M
RI)
is
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t
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fy
b
ra
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d
iso
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rs,
p
a
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k
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p
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ss
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in
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n
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is
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rifi
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u
sin
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c
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u
ra
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n
siti
v
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n
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sp
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ifi
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rid
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tree
(S
VMBT
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y
iel
d
s
th
e
b
e
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p
e
rfo
rm
a
n
c
e
fo
r
str
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k
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les
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c
las
sifica
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n
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a
c
h
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th
e
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t
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ro
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k
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las
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e
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e
m
o
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stra
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ro
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g
p
o
ten
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a
l
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b
ra
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k
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l
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ls i
n
ti
m
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ly
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n
d
a
c
c
u
r
a
te as
se
s
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ts.
K
ey
w
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r
d
s
:
B
r
ain
d
is
o
r
d
er
s
im
ag
in
g
Ma
g
n
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r
eso
n
an
ce
im
a
g
es
Qu
alitativ
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lo
s
s
es
Stro
k
e
lesi
o
n
Su
s
ce
p
tib
ilit
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weig
h
ted
T
im
e
is
b
r
ain
T
h
is i
s
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rticle
u
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CC B
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SA
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.
C
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s
p
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A
uth
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r
:
No
r
h
ash
im
ah
Mo
h
d
Saad
Facu
lty
o
f
E
lectr
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ics an
d
C
o
m
p
u
ter
T
ec
h
n
o
lo
g
y
a
n
d
E
n
g
in
ee
r
in
g
,
Un
iv
e
r
s
iti T
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n
ik
al
M
alay
s
ia
Me
lak
a
Me
lak
a,
Ma
lay
s
ia
E
m
ail:
n
o
r
h
ash
im
ah
@
u
tem
.
e
d
u
.
m
y
1.
I
NT
RO
D
UCT
I
O
N
A
s
tr
o
k
e,
k
n
o
w
n
as
a
"c
er
eb
r
al
in
f
ar
ctio
n
,
"
u
s
u
ally
ca
u
s
e
s
p
ar
aly
s
is
r
esu
ltin
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ca
u
s
e
o
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d
ea
th
in
Ma
lay
s
ia,
with
at
least
3
2
d
e
ath
s
p
er
d
ay
,
an
d
p
o
s
es
a
m
ajo
r
ch
allen
g
e
t
o
Ma
lay
s
ia'
s
h
ea
lth
s
er
v
ices
[
1
]
.
A
r
ec
en
t
s
tu
d
y
s
h
o
we
d
th
at
a
p
atien
t'
s
ca
n
b
e
s
av
ed
if
th
e
y
r
ec
eiv
e
tr
ea
tm
e
n
t
with
in
s
ix
h
o
u
r
s
o
f
a
s
tr
o
k
e.
Un
f
o
r
tu
n
atel
y
,
Ma
lay
s
ia
is
f
a
cin
g
a
s
h
o
r
tag
e
o
f
n
eu
r
o
r
ad
i
o
lo
g
is
ts
,
h
am
p
e
r
in
g
ef
f
o
r
ts
t
o
tr
ea
t
its
g
r
o
win
g
n
u
m
b
er
o
f
s
tr
o
k
e
p
atien
ts
[
2
]
.
Ad
v
an
ce
d
im
ag
i
n
g
u
s
in
g
m
a
g
n
etic
r
eso
n
an
ce
im
a
g
in
g
(
M
R
I
)
h
as
g
ain
ed
m
o
r
e
atten
tio
n
th
an
co
n
v
en
tio
n
al
an
g
io
g
r
ap
h
y
in
t
h
e
d
iag
n
o
s
is
o
f
ac
u
te
s
tr
o
k
e
d
u
e
to
its
h
ig
h
s
p
atial
r
eso
lu
tio
n
an
d
f
ast
s
ca
n
tim
es.
T
r
ad
itio
n
ally
,
d
iag
n
o
s
is
was
m
ad
e
m
an
u
all
y
b
y
n
eu
r
o
r
ad
i
o
lo
g
is
ts
d
u
r
in
g
a
h
ig
h
ly
s
u
b
jectiv
e
Evaluation Warning : The document was created with Spire.PDF for Python.
I
n
t J E
lec
&
C
o
m
p
E
n
g
I
SS
N:
2088
-
8
7
0
8
C
la
s
s
ifica
tio
n
o
f b
r
a
in
s
tr
o
ke
b
a
s
ed
o
n
s
u
s
ce
p
tib
ilit
y
-
w
eig
h
ted
ima
g
in
g
…
(
S
h
a
a
r
mila
K
a
n
d
a
ya
)
1603
an
d
tim
e
-
c
o
n
s
u
m
in
g
task
[
3
]
.
Dete
ctin
g
s
tr
o
k
e
f
r
o
m
MRI
im
ag
es
is
a
ch
allen
g
i
n
g
task
d
u
e
to
t
h
e
p
r
esen
ce
o
f
n
o
is
e
an
d
ar
tifa
cts,
s
m
all
s
ize,
an
d
h
eter
o
g
en
e
o
u
s
s
tr
u
ctu
r
e
o
f
v
ess
els
[
4
]
.
T
h
e
p
r
esen
ce
o
f
b
lo
o
d
cir
c
u
latio
n
is
a
cr
itical
f
ac
to
r
in
th
e
p
ath
o
p
h
y
s
io
lo
g
y
o
f
ac
u
te
is
ch
e
m
ic
s
tr
o
k
e
as
it
s
er
v
es
as
an
alter
n
ativ
e
b
lo
o
d
s
u
p
p
ly
wh
en
th
e
p
r
i
m
ar
y
ar
ter
y
s
u
p
p
l
y
in
g
t
h
e
af
f
ec
ted
ar
ea
b
ec
o
m
es
b
lo
ck
ed
[
5
]
.
T
h
e
r
ec
r
u
itm
en
t
o
f
b
lo
o
d
cir
cu
latio
n
d
u
r
in
g
a
s
tr
o
k
e
v
ar
ies
f
r
o
m
p
e
r
s
o
n
to
p
er
s
o
n
an
d
h
as
an
im
p
ac
t
o
n
p
o
ten
tial
co
m
p
licat
io
n
s
,
h
o
w
th
e
is
ch
em
ic
in
f
ar
c
t
d
ev
elo
p
s
,
th
e
s
ize
o
f
th
e
in
f
ar
ct,
an
d
tr
ea
tm
en
t
o
u
tco
m
es
[
6
]
.
E
a
r
ly
s
tr
o
k
e
s
tatu
s
i
s
b
ec
o
m
in
g
m
o
r
e
wi
d
ely
r
ec
o
g
n
ized
as
a
p
r
o
m
i
s
in
g
b
io
m
ar
k
er
f
o
r
d
eter
m
in
in
g
h
o
w
a
s
tr
o
k
e
m
ay
p
r
o
g
r
ess
[
7
]
.
MRI
is
an
ad
v
an
ce
d
im
ag
i
n
g
m
o
d
ality
th
at
h
as
g
ain
ed
p
o
p
u
lar
ity
in
m
ed
ical
im
a
g
in
g
,
p
ar
t
icu
lar
ly
i
n
th
e
ass
es
s
m
en
t
o
f
ea
r
ly
s
tr
o
k
e.
T
h
is
is
d
u
e
to
its
lo
w
r
ad
i
atio
n
d
o
s
e,
s
h
o
r
ter
s
ca
n
n
in
g
t
im
e,
lo
w
co
s
t,
h
ig
h
s
p
atial
r
eso
lu
tio
n
a
n
d
ea
s
e
i
n
in
ter
p
r
etatio
n
[
8
]
.
T
y
p
icall
y
,
th
e
ev
alu
ati
o
n
o
f
ea
r
ly
s
tr
o
k
e
is
m
an
u
ally
co
n
d
u
cte
d
b
y
n
e
u
r
o
r
ad
io
lo
g
is
ts
,
is
a
t
im
e
-
co
n
s
u
m
in
g
an
d
s
u
b
jectiv
e
p
r
o
ce
s
s
.
B
y
lev
er
ag
in
g
MRI
im
ag
in
g
,
r
esear
ch
er
s
ca
n
in
v
esti
g
ate
th
e
ch
ar
ac
ter
is
tics
,
p
atter
n
s
,
an
d
f
u
n
ctio
n
al
s
ig
n
if
ican
c
e
o
f
ea
r
ly
s
tr
o
k
e,
co
n
tr
ib
u
tin
g
to
im
p
r
o
v
e
d
u
n
d
er
s
tan
d
in
g
,
d
iag
n
o
s
is
,
an
d
tr
e
atm
en
t
s
tr
ateg
ies
f
o
r
p
atien
ts
with
co
m
p
r
o
m
is
ed
b
lo
o
d
f
lo
w
[
9
]
.
T
h
is
r
esear
ch
d
em
o
n
s
tr
ated
a
n
ew
an
aly
s
is
f
r
am
ewo
r
k
t
o
class
if
y
ea
r
ly
s
tr
o
k
e
ac
cu
r
ately
f
o
r
is
ch
em
ic
s
tr
o
k
e
p
atien
ts
in
to
th
r
ee
class
es:
g
o
o
d
im
p
r
o
v
em
e
n
t,
m
o
d
er
ate
im
p
r
o
v
em
en
t
an
d
p
o
o
r
im
p
r
o
v
em
e
n
t
p
atien
ts
b
ased
o
n
p
r
e
an
d
p
o
s
t
s
tr
o
k
e
p
atien
ts
’
d
ata
[
1
0
]
.
T
h
is
s
tu
d
y
p
r
o
p
o
s
es
to
an
aly
ze
b
r
ai
n
s
tr
o
k
e
d
iag
n
o
s
is
b
ased
o
n
b
r
ai
n
MRI
u
s
in
g
m
ac
h
in
e
lear
n
in
g
.
Ad
v
an
ce
d
im
a
g
in
g
with
MRI
h
as
g
ain
ed
m
o
r
e
atten
tio
n
th
an
c
o
n
v
e
n
tio
n
al
a
n
g
io
g
r
a
p
h
y
in
ac
u
te
s
tr
o
k
e
d
i
ag
n
o
s
is
d
u
e
to
its
h
ig
h
s
p
atial
r
eso
lu
tio
n
an
d
f
ast
s
ca
n
tim
e
[
1
1
]
.
T
r
a
d
itio
n
ally
,
d
iag
n
o
s
is
was
m
ad
e
m
an
u
ally
b
y
n
eu
r
o
r
ad
io
lo
g
is
ts
d
u
r
in
g
a
h
ig
h
ly
s
u
b
jectiv
e
an
d
tim
e
-
co
n
s
u
m
i
n
g
task
.
T
h
u
s
,
th
e
aim
is
to
d
is
co
v
er
th
e
u
tili
za
tio
n
o
f
m
ac
h
in
e
lear
n
in
g
tech
n
iq
u
es
to
au
to
m
ate
th
e
class
if
icatio
n
o
f
ea
r
ly
s
tr
o
k
e
d
iag
n
o
s
is
o
n
M
R
I
im
ag
es.
Ma
ch
in
e
lear
n
in
g
h
as
a
h
u
g
e
b
e
n
ef
it
o
v
er
co
n
v
en
tio
n
al
tech
n
iq
u
es
in
th
at
it
ca
n
lear
n
n
o
n
-
lin
e
ar
m
ass
iv
e
d
ata
s
am
p
les
wh
ile
also
r
ed
u
cin
g
th
e
co
m
p
lex
ity
o
f
t
h
e
p
r
o
ce
s
s
[
1
2
]
.
I
t
is
ex
p
ec
ted
to
ass
is
t
d
o
cto
r
s
in
g
i
v
in
g
p
r
ec
is
e
d
ec
is
io
n
,
r
ed
u
cin
g
d
iag
n
o
s
is
tim
e,
an
d
d
eliv
er
in
g
f
ast
tr
ea
tm
en
t
to
s
tr
o
k
e
p
atien
ts
.
I
n
p
r
o
v
id
in
g
b
etter
h
ea
lth
ca
r
e
s
o
l
u
tio
n
s
th
r
o
u
g
h
a
n
in
tellig
en
t sy
s
tem
,
th
e
r
esu
lts
o
f
th
is
r
esear
ch
co
u
ld
s
er
v
e
t
o
im
p
r
o
v
e
t
h
e
h
ea
lth
ca
r
e
o
f
t
h
e
co
m
m
u
n
ity
[
1
3
]
.
2.
L
I
T
E
R
AT
U
RE
R
E
VI
E
W
T
h
e
h
u
m
an
b
r
ain
is
a
c
o
m
p
l
ex
o
r
g
an
th
at
is
f
u
n
ctio
n
s
f
o
r
co
n
t
r
o
llin
g
an
d
co
o
r
d
in
atin
g
v
ar
i
o
u
s
b
o
d
ily
f
u
n
ctio
n
s
,
as
well
as
en
ab
lin
g
co
g
n
itiv
e
p
r
o
ce
s
s
es
an
d
b
eh
a
v
io
r
[
1
4
]
.
I
t
is
d
iv
id
e
d
in
to
s
ev
er
al
m
ajo
r
r
eg
io
n
s
,
ea
ch
with
its
o
wn
s
p
e
cif
ic
f
u
n
ctio
n
s
.
T
h
e
ce
r
eb
r
u
m
is
th
e
lar
g
est
p
ar
t
o
f
th
e
b
r
ain
an
d
is
d
iv
id
e
d
in
to
two
h
em
is
p
h
er
es,
th
e
le
f
t
an
d
r
ig
h
t
h
em
is
p
h
e
r
es
[
1
5
]
.
E
ac
h
h
em
is
p
h
er
e
is
f
u
r
th
er
d
iv
id
ed
in
to
f
o
u
r
lo
b
es:
th
e
f
r
o
n
tal
lo
b
e,
p
ar
ietal
lo
b
e,
tem
p
o
r
al
lo
b
e
,
an
d
o
cc
ip
ital lo
b
e
.
Fig
u
r
e
1
id
en
tifie
s
is
ch
em
ic
s
tr
o
k
e
w
h
ich
ca
te
g
o
r
ize
in
f
iv
e
s
tag
e
[
1
6
]
,
th
o
s
e
ar
e
ea
r
ly
h
y
p
er
ac
u
te
(0
–
6
h
o
u
r
s
)
,
late
h
y
p
er
ac
u
te
(
6
–
2
4
h
o
u
r
s
)
,
ac
u
te
(
2
4
h
o
u
r
s
–
1
wee
k
)
,
s
u
b
ac
u
te
(
1
-
3
wee
k
s
)
,
an
d
ch
r
o
n
i
c
(
>
th
r
ee
wee
k
s
)
.
I
n
s
u
f
f
icie
n
t
ar
ter
ial
p
r
ess
u
r
e
to
m
ee
t
m
etab
o
lic
d
em
an
d
s
lead
s
to
b
r
ai
n
is
ch
em
ia,
ca
u
s
in
g
ce
r
eb
r
al
h
y
p
er
ten
s
io
n
o
r
a
d
ep
letio
n
o
f
o
x
y
g
e
n
in
th
e
b
r
ain
,
r
esu
ltin
g
in
b
r
ai
n
tis
s
u
e
d
ea
th
o
r
is
ch
em
ic
s
tr
o
k
e
[
1
7
]
.
I
s
ch
em
ic
s
tr
o
k
e
in
th
e
b
r
ain
ca
n
in
d
u
ce
in
f
lam
m
atio
n
,
af
f
ec
tin
g
n
eu
r
o
n
al
an
d
g
lial
f
u
n
ctio
n
,
alo
n
g
wit
h
v
ascu
lar
ch
a
n
g
es
[
1
8
]
.
T
h
e
o
n
g
o
in
g
s
u
p
p
ly
o
f
o
x
y
g
e
n
an
d
n
u
tr
ien
ts
is
c
r
u
cial
f
o
r
n
eu
r
o
n
al
f
u
n
ctio
n
.
I
n
ter
r
u
p
tio
n
o
f
th
is
s
u
p
p
ly
le
ad
s
to
u
n
co
n
s
cio
u
s
n
ess
,
an
d
p
r
o
lo
n
g
ed
d
e
p
r
iv
atio
n
ca
u
s
es
ir
r
ev
er
s
ib
le
b
r
ain
d
am
ag
e
[
1
9
]
.
Ap
p
r
o
x
im
ately
4
to
1
5
%
o
f
all
is
ch
em
ic
s
tr
o
k
es
ar
e
attr
ib
u
ted
to
ac
u
te
in
ter
n
al
ca
r
o
tid
ar
ter
y
o
cc
lu
s
io
n
as th
e
p
r
im
a
r
y
ca
u
s
e
[
2
0
]
.
Fig
u
r
e
1
.
I
s
ch
e
m
ic
b
r
ain
s
tr
o
k
e
Evaluation Warning : The document was created with Spire.PDF for Python.
I
SS
N
:
2
0
8
8
-
8
7
0
8
I
n
t J E
lec
&
C
o
m
p
E
n
g
,
Vo
l.
15
,
No
.
2
,
Ap
r
il
20
25
:
1
6
0
2
-
1
6
1
1
1604
W
o
m
en
f
ac
e
a
h
ig
h
er
s
u
s
ce
p
t
ib
ilit
y
to
s
tr
o
k
e
-
r
elate
d
c
o
n
d
it
io
n
s
th
an
m
en
,
with
s
tatis
tics
r
ev
ea
lin
g
th
at
6
o
u
t
o
f
1
0
in
d
iv
id
u
als
af
f
ec
ted
b
y
s
tr
o
k
e
ar
e
wo
m
en
[
2
1
]
.
T
h
is
u
n
d
er
s
co
r
es
th
e
n
ee
d
f
o
r
g
e
n
d
er
-
s
p
ec
if
ic
co
n
s
id
er
atio
n
s
in
s
tr
o
k
e
p
r
e
v
e
n
tio
n
,
d
iag
n
o
s
is
,
an
d
tr
ea
tm
en
t
ap
p
r
o
ac
h
es.
W
h
ile
th
r
o
m
b
ec
to
m
y
,
a
p
r
o
ce
d
u
r
e
aim
ed
at
r
em
o
v
in
g
b
lo
o
d
clo
t
s
f
r
o
m
b
lo
c
k
ed
a
r
ter
ies,
ca
r
r
ie
s
in
h
er
en
t
r
is
k
s
,
th
ese
r
is
k
s
ar
e
p
r
im
ar
ily
r
ele
v
an
t
to
p
atien
ts
with
s
p
ec
if
ic
ch
ar
ac
ter
is
tics
[
2
2
]
.
Fo
r
in
s
tan
ce
,
in
d
iv
id
u
als
with
a
s
m
all
in
f
ar
ctio
n
b
u
t
a
lar
g
e
p
en
u
m
b
r
a
an
d
e
x
ce
llen
t
co
ll
ater
al
cir
cu
latio
n
a
r
e
co
n
s
id
er
ed
s
u
itab
le
ca
n
d
id
ates
f
o
r
t
h
r
o
m
b
ec
t
o
m
y
[
2
3
]
.
I
d
en
tify
in
g
s
u
ch
p
atien
ts
ac
cu
r
ately
is
cr
u
cial
to
en
s
u
r
e
th
at
th
e
b
en
ef
its
o
f
th
e
p
r
o
ce
d
u
r
e
o
u
tweig
h
p
o
te
n
tial
r
is
k
s
.
E
ar
ly
d
etec
tio
n
o
f
war
n
in
g
s
ig
n
s
is
v
ital
in
m
in
im
izin
g
th
e
im
p
ac
t
o
f
a
s
tr
o
k
e,
an
d
p
u
b
lic
awa
r
e
n
ess
ca
m
p
aig
n
s
an
d
e
d
u
ca
tio
n
p
r
o
g
r
am
s
ar
e
em
p
h
asized
to
en
h
an
ce
s
tr
o
k
e
awa
r
en
ess
[
2
4
]
.
T
ak
in
g
i
n
to
ac
c
o
u
n
t
th
e
h
ig
h
er
s
tr
o
k
e
r
is
k
in
wo
m
en
,
th
e
ap
p
r
o
p
r
iaten
ess
o
f
th
r
o
m
b
ec
to
m
y
b
ased
o
n
p
atien
t
ch
ar
ac
ter
is
tics
,
an
d
th
e
im
p
o
r
tan
ce
o
f
ea
r
ly
d
etec
tio
n
,
h
ea
lth
ca
r
e
p
r
o
v
id
er
s
an
d
r
esear
ch
er
s
ca
n
f
o
r
m
u
late
tar
g
eted
s
tr
ateg
ies
f
o
r
s
tr
o
k
e
p
r
ev
en
ti
o
n
,
p
r
ec
is
e
p
ati
en
t
s
elec
tio
n
f
o
r
th
r
o
m
b
ec
to
m
y
,
an
d
tim
ely
in
ter
v
en
tio
n
s
.
T
h
is
co
m
p
r
eh
en
s
iv
e
ap
p
r
o
ac
h
aim
s
to
allev
iate
th
e
b
u
r
d
e
n
o
f
s
tr
o
k
e
-
r
elate
d
d
is
ea
s
es a
n
d
en
h
an
ce
o
u
tco
m
es f
o
r
th
o
s
e
at
r
is
k
[
2
5
]
.
3.
M
E
T
H
O
D
T
h
is
p
ar
t
d
is
cu
s
s
es
th
e
cla
s
s
if
icatio
n
an
aly
s
is
u
s
in
g
m
ac
h
in
e
lear
n
in
g
tech
n
iq
u
es.
Fro
m
th
e
class
if
icatio
n
,
th
e
p
er
f
o
r
m
an
c
e
an
aly
s
is
was
co
n
d
u
cted
b
ased
o
n
ac
cu
r
ac
y
,
s
p
ec
if
icity
,
a
n
d
s
en
s
itiv
ity
.
T
h
e
r
esu
lts
p
r
o
v
id
e
in
s
ig
h
ts
in
to
th
e
m
o
d
el'
s
ab
ilit
y
to
co
r
r
ec
tly
class
if
y
d
ata
wh
ile
m
in
im
izin
g
f
alse
p
o
s
itiv
es
an
d
f
alse
n
eg
ativ
es.
T
h
is
ev
alu
a
tio
n
h
ig
h
lig
h
ts
th
e
s
tr
en
g
th
s
an
d
lim
itatio
n
s
o
f
th
e
a
p
p
lied
tech
n
iq
u
es
in
ad
d
r
ess
in
g
th
e
p
r
o
b
lem
.
3
.
1
.
Cla
s
s
if
ica
t
io
n
a
na
ly
s
is
us
i
ng
m
a
chine le
a
rning
t
ec
hn
iqu
e
s
C
las
s
if
icatio
n
tech
n
iq
u
e
is
p
r
o
p
o
s
ed
to
class
if
y
th
e
ty
p
e
o
f
s
tr
o
k
es
b
ased
o
n
th
e
f
ea
tu
r
es
th
at
ar
e
ex
tr
ac
ted
f
r
o
m
th
e
b
est
s
eg
m
en
tatio
n
r
esu
lt.
T
h
is
s
tu
d
y
p
r
o
p
o
s
es
f
o
u
r
tech
n
iq
u
e
s
wh
ich
ar
e
lin
ea
r
d
is
cr
im
in
an
t
an
al
y
s
is
,
s
u
p
p
o
r
t
v
ec
to
r
m
ac
h
in
e,
b
ag
g
ed
tr
ee
class
if
ier
an
d
h
y
b
r
i
d
s
u
p
p
o
r
t
v
ec
to
r
m
ac
h
in
e
an
d
b
ag
g
ed
tr
ee
(
SVMBT)
.
On
th
e
b
asis
o
f
th
e
f
ea
tu
r
es
th
at
ar
e
r
etr
iev
ed
f
r
o
m
th
e
b
est
s
eg
m
en
tatio
n
r
esu
lt,
a
class
if
icatio
n
tech
n
iq
u
e
is
g
iv
en
to
ca
teg
o
r
ies
th
e
d
if
f
er
en
t t
y
p
es o
f
s
tr
o
k
es.
3
.
1
.
1
.
L
inea
r
dis
cr
im
ina
nt
a
na
ly
s
is
L
in
ea
r
d
is
cr
im
in
an
t
an
al
y
s
is
(
L
DA)
,
a
s
u
p
er
v
is
ed
m
ac
h
in
e
lear
n
in
g
m
eth
o
d
,
is
r
ec
o
g
n
iz
ed
f
o
r
its
ef
f
ec
tiv
e
a
p
p
r
o
ac
h
t
o
f
ea
t
u
r
e
ex
tr
ac
tio
n
a
n
d
d
im
en
s
io
n
r
e
d
u
ctio
n
[
2
6
]
.
T
h
is
tech
n
iq
u
e
e
m
p
lo
y
s
a
p
r
e
d
ictiv
e
eq
u
atio
n
b
ased
o
n
r
e
g
io
n
o
f
in
ter
est
(
R
OI
)
ch
ar
ac
ter
is
tics
to
class
if
y
s
tr
o
k
e
ty
p
es.
T
h
e
d
is
cr
ete
d
ep
en
d
e
n
t
v
ar
iab
les
r
ep
r
esen
tin
g
R
OI
f
ea
tu
r
es
ar
e
p
lo
tted
o
n
a
s
ca
t
ter
p
lo
t.
L
DA
aim
s
to
id
e
n
tify
a
co
n
cise
s
et
o
f
f
ea
tu
r
es
th
at
ca
n
g
en
e
r
ate
a
r
o
b
u
s
t
p
r
e
d
ictiv
e
m
o
d
el
f
o
r
d
is
tin
g
u
is
h
in
g
b
etwe
en
d
if
f
er
en
t
s
tr
o
k
e
ty
p
es.
T
h
is
is
ac
h
iev
ed
b
y
ca
lcu
latin
g
ax
es
th
at
m
ax
im
ize
th
e
s
ep
ar
atio
n
b
etwe
en
d
iv
er
s
e
s
tr
o
k
e
ca
t
eg
o
r
ies
[
2
7
]
.
T
h
e
tech
n
iq
u
e
p
r
o
jects
th
e
f
ea
tu
r
e
s
p
ac
e
o
n
to
a
s
m
aller
s
u
b
s
p
ac
e
wh
ile
r
etain
in
g
c
r
u
c
ial
d
is
cr
im
in
ato
r
y
in
f
o
r
m
atio
n
f
o
r
ea
c
h
s
tr
o
k
e.
I
n
ea
ch
s
tr
o
k
e
ty
p
e,
t
h
e
ch
ar
ac
t
er
is
tics
(
)
ar
e
m
u
ltip
lied
b
y
t
h
e
s
tr
o
k
e
ty
p
e
(
)
,
co
n
tr
ib
u
tin
g
to
th
e
cr
ea
tio
n
o
f
a
s
ca
tter
p
lo
t.
Scatter
m
atr
ices
ar
e
ass
ig
n
ed
to
ca
lcu
late
th
e
m
ea
n
v
ec
to
r
,
i,
f
o
llo
win
g
th
e
f
u
n
d
am
en
tal
th
e
o
r
y
ex
p
r
ess
ed
b
y
(
1
)
.
=
[
1
1
1
2
⋯
2
1
2
2
⋯
⋯
⋯
⋯
⋯
⋯
]
,
=
[
1
⋯
]
,
=
×
(
1
)
wh
er
e
=
th
e
n
u
m
b
e
r
o
f
s
am
p
l
es
in
ea
ch
ty
p
e
o
f
s
tr
o
k
e
lesi
o
n
,
=
th
e
f
ea
tu
r
es
o
f
th
e
R
OI
,
a
n
d
=
th
e
ty
p
e
o
f
s
tr
o
k
e.
3
.
1
.
2
.
Su
pp
o
rt
v
ec
t
o
r
m
a
chi
ne
Su
p
p
o
r
t
v
ec
to
r
m
ac
h
in
e
(
SV
M)
s
tan
d
s
o
u
t
as
th
e
o
p
tim
al
class
if
ier
f
o
r
ef
f
ec
tiv
ely
ca
teg
o
r
izin
g
m
u
ltip
le
ca
teg
o
r
ies
[
2
8
]
.
R
e
co
g
n
ized
as
a
lin
ea
r
m
o
d
el
ap
p
licab
le
to
b
o
th
class
if
icatio
n
an
d
r
eg
r
ess
io
n
ch
allen
g
es,
SVM
d
em
o
n
s
tr
ates
p
r
o
f
icien
cy
in
ad
d
r
ess
in
g
a
wid
e
r
an
g
e
o
f
r
ea
l
-
wo
r
ld
p
r
o
b
lem
s
,
en
co
m
p
ass
in
g
b
o
th
lin
ea
r
an
d
n
o
n
-
lin
ea
r
s
ce
n
ar
io
s
[
2
9
]
.
I
n
th
e
co
n
tex
t o
f
s
tr
o
k
e
lesi
o
n
ty
p
es,
ea
ch
b
in
a
r
y
lear
n
er
is
lin
k
ed
to
a
s
p
ec
if
ic
ty
p
e
o
f
s
tr
o
k
e,
d
e
n
o
ted
as
,
with
in
a
m
at
r
ix
e
lem
en
t
ter
m
ed
a
co
d
i
n
g
d
esig
n
.
T
o
s
im
p
lif
y
class
if
icatio
n
in
s
ce
n
ar
io
s
in
v
o
lv
in
g
m
u
ltip
le
class
es,
th
e
o
n
e
-
v
er
s
u
s
-
o
n
e
co
d
i
n
g
d
esig
n
is
im
p
lem
en
ted
.
T
h
is
co
d
in
g
d
esig
n
o
p
er
ates a
s
(
2
)
:
(
−
1
)
/
2
(
2
)
Evaluation Warning : The document was created with Spire.PDF for Python.
I
n
t J E
lec
&
C
o
m
p
E
n
g
I
SS
N:
2088
-
8
7
0
8
C
la
s
s
ifica
tio
n
o
f b
r
a
in
s
tr
o
ke
b
a
s
ed
o
n
s
u
s
ce
p
tib
ilit
y
-
w
eig
h
ted
ima
g
in
g
…
(
S
h
a
a
r
mila
K
a
n
d
a
ya
)
1605
E
ac
h
b
in
ar
y
lear
n
er
is
ex
clu
s
iv
ely
d
esig
n
ated
t
o
m
atch
with
o
n
e
ty
p
e
o
f
s
tr
o
k
e
f
o
r
p
o
s
itiv
e
b
in
ar
y
c
o
r
r
elatio
n
,
an
o
th
er
ty
p
e
o
f
s
tr
o
k
e
f
o
r
n
e
g
ativ
e
co
r
r
elatio
n
,
an
d
th
e
r
em
a
in
in
g
ty
p
es a
r
e
d
is
r
eg
ar
d
ed
[
3
0
]
.
I
n
lo
s
s
-
weig
h
ted
d
ec
o
d
in
g
,
th
e
p
r
ed
icte
d
ty
p
e
o
f
s
tr
o
k
e
f
o
r
a
n
o
b
s
er
v
atio
n
is
d
eter
m
in
ed
b
y
th
e
s
tr
o
k
e
ty
p
e
th
at
r
esu
lts
in
th
e
s
m
allest av
er
ag
e
o
f
b
in
a
r
y
lo
s
s
es a
cr
o
s
s
th
e
b
in
ar
y
lear
n
e
r
s
,
ex
p
r
ess
ed
as
(
3
)
:
̑
=
∑
|
|
,
=
1
∑
|
|
=
1
(
3
)
wh
er
e
is
an
elem
en
t
o
f
t
h
e
(
)
o
f
t
h
e
b
in
a
r
y
lea
r
n
er
l
th
at
c
o
r
r
esp
o
n
d
s
t
o
th
e
ty
p
e
o
f
s
tr
o
k
e,
.
B
e
th
e
b
in
ar
y
lo
s
s
f
u
n
ctio
n
,
a
n
d
let
b
e
th
e
lear
n
er
'
s
s
co
r
e
f
o
r
a
b
in
a
r
y
o
b
s
er
v
atio
n
.
3
.
1
.
3
.
B
a
g
g
ed
t
re
e
B
ag
g
ed
tr
ee
e
n
s
em
b
le
lear
n
in
g
m
eth
o
d
g
en
er
ates
a
s
u
b
s
tan
tial
n
u
m
b
er
o
f
d
ec
is
io
n
tr
ee
s
d
u
r
in
g
th
e
tr
ain
in
g
p
h
ase
a
n
d
p
r
o
d
u
ce
s
t
h
e
s
tr
o
k
e
ty
p
e
th
at
r
ep
r
esen
ts
th
e
m
o
d
e
am
o
n
g
th
e
in
d
iv
i
d
u
al
tr
ee
s
'
s
tr
o
k
e
ty
p
es
[
3
1
]
.
T
h
e
u
n
i
v
er
s
al
b
ag
g
in
g
lear
n
er
tech
n
iq
u
e
is
em
p
lo
y
e
d
in
t
h
e
b
a
g
g
ed
tr
ee
t
r
ain
in
g
alg
o
r
ith
m
.
I
n
th
is
alg
o
r
ith
m
,
a
r
an
d
o
m
s
am
p
le
w
ith
r
ep
lace
m
e
n
t
o
f
th
e
tr
ain
in
g
s
et,
d
en
o
ted
as
=
1
,...,
(
r
ep
r
esen
ti
n
g
th
e
s
tr
o
k
e
ty
p
es
with
r
esp
o
n
s
e
=
1
,...,
,
wh
ich
ar
e
t
h
e
f
ea
t
u
r
es
o
f
s
tr
o
k
e
lesi
o
n
s
)
,
is
r
ep
ea
ted
l
y
ch
o
s
en
.
T
h
e
lear
n
er
s
ar
e
tr
ain
ed
u
s
in
g
r
esa
m
p
led
co
p
ies
o
f
t
h
e
d
ata
in
b
ag
g
in
g
(
B
)
.
T
h
e
co
m
m
o
n
r
es
am
p
lin
g
m
eth
o
d
in
th
is
p
r
o
ce
s
s
is
b
o
o
ts
tr
ap
p
in
g
,
wh
er
e
a
s
p
ec
if
ic
n
u
m
b
er
o
f
s
tr
o
k
e
f
ea
t
u
r
es
(
)
ar
e
ch
o
s
en
,
w
ith
r
ep
lace
m
en
t,
f
r
o
m
a
lar
g
er
s
et
o
f
s
tr
o
k
e
f
ea
t
u
r
es (
o
b
s
er
v
atio
n
s
)
f
o
r
ea
ch
n
ew
lear
n
er
.
Du
r
in
g
th
e
tr
ain
in
g
,
ea
ch
tr
e
e
in
th
e
en
s
em
b
le
h
as
th
e
ab
ilit
y
to
r
an
d
o
m
l
y
s
elec
t
p
r
ed
icto
r
s
f
o
r
d
ec
is
io
n
s
p
lits
[
3
2
]
.
T
h
e
class
if
ier
co
m
b
i
n
es
p
r
ed
ictio
n
s
f
r
o
m
m
u
ltip
le
tr
ee
s
to
d
eter
m
in
e
th
e
ex
p
ec
ted
s
tr
o
k
e
ty
p
e
f
o
r
a
tr
ai
n
in
g
e
n
s
em
b
le.
Fo
r
class
if
icatio
n
tr
ee
s
,
p
r
ed
i
ctio
n
s
f
o
r
u
n
s
ee
n
s
am
p
les
(
)
ca
n
b
e
m
a
d
e
af
ter
tr
ain
in
g
th
r
o
u
g
h
a
m
ajo
r
ity
v
o
te,
as
r
ep
r
esen
ted
in
th
e
eq
u
atio
n
wh
er
e
f
b
d
e
n
o
te
s
th
e
b
ag
g
ed
tr
ee
class
if
icatio
n
lear
n
er
.
̂
=
1
∑
(
)
=
1
(
4
)
T
h
e
n
u
m
b
er
o
f
s
tr
o
k
e
f
ea
tu
r
es,
s
elec
ted
at
r
an
d
o
m
f
o
r
ev
er
y
d
ec
is
io
n
s
p
lit
is
s
elec
t
ed
.
T
h
is
r
an
d
o
m
s
elec
tio
n
is
m
ad
e
f
o
r
e
v
er
y
s
p
l
it,
an
d
ev
er
y
d
ee
p
tr
ee
in
v
o
lv
e
s
m
an
y
s
p
lits
.
3
.
1
.
4
.
H
y
brid
co
m
bin
a
t
io
n o
f
s
up
po
rt
v
ec
t
o
r
m
a
chine a
nd
ba
g
g
ed
t
re
e
B
o
th
b
ag
g
ed
tr
ee
s
an
d
SVM
c
an
ac
h
iev
e
h
ig
h
ac
c
u
r
ac
y
i
n
cl
ass
if
icatio
n
task
s
.
B
ag
g
ed
tr
ee
s
ex
ce
l
in
th
eir
r
o
b
u
s
tn
ess
to
o
v
er
f
itti
n
g
an
d
f
lex
ib
ilit
y
,
w
h
ile
SVMs
p
er
f
o
r
m
well
in
h
ig
h
-
d
im
e
n
s
io
n
al
s
p
ac
es
an
d
o
f
f
er
f
in
e
-
tu
n
in
g
o
p
ti
o
n
s
f
o
r
c
o
n
tr
o
llin
g
m
o
d
el
c
o
m
p
lex
ity
.
Ho
we
v
er
,
SVMs c
an
b
e
co
m
p
u
tatio
n
ally
ex
p
e
n
s
iv
e
an
d
r
eq
u
ir
e
ca
r
ef
u
l
p
ar
am
eter
t
u
n
i
n
g
,
wh
ile
b
ag
g
ed
tr
ee
s
m
ay
s
ac
r
if
ice
in
ter
p
r
etab
ilit
y
an
d
f
a
ce
ch
allen
g
es
with
h
ig
h
v
a
r
ian
ce
.
T
h
e
ch
o
ice
b
et
wee
n
th
em
d
e
p
en
d
s
o
n
th
e
s
p
ec
if
ic
r
eq
u
ir
em
e
n
ts
an
d
c
o
n
s
tr
ain
ts
as
well.
SVM
is
a
d
is
cr
im
in
ativ
e
class
if
ier
th
at
f
in
d
s
th
e
o
p
tim
al
h
y
p
er
p
l
an
e
to
s
ep
ar
ate
class
es,
wh
er
e
as
b
ag
g
e
d
tr
ee
s
a
r
e
b
ased
o
n
e
n
s
em
b
le
lear
n
in
g
u
s
in
g
d
ec
is
io
n
tr
ee
s
.
T
h
en
,
SVM
tr
ies
to
f
in
d
th
e
h
y
p
er
p
la
n
e
th
at
m
ax
im
izes
th
e
m
ar
g
in
b
etwe
en
class
es,
wh
ile
d
ec
is
io
n
tr
ee
s
cr
ea
te
p
iece
wis
e
co
n
s
tan
t
d
ec
is
io
n
b
o
u
n
d
ar
ies.
Ad
d
itio
n
ally
,
SVM
r
eq
u
ir
es
tu
n
i
n
g
o
f
p
ar
a
m
eter
s
lik
e
th
e
ch
o
ice
o
f
k
er
n
el
an
d
r
eg
u
lar
izatio
n
p
ar
am
ete
r
,
wh
ile
b
ag
g
e
d
tr
ee
s
ar
e
r
elativ
ely
s
im
p
le
to
u
s
e
with
o
u
t m
u
c
h
p
ar
a
m
eter
tu
n
i
n
g
.
3
.
2
.
P
er
f
o
rma
nce
a
na
ly
s
is
f
o
r
cl
a
s
s
if
ica
t
io
n t
ec
hn
iqu
e
I
n
th
e
r
ea
lm
o
f
m
ac
h
i
n
e
lear
n
in
g
,
a
co
n
f
u
s
io
n
m
atr
ix
s
e
r
v
es
as
a
tab
le
u
tili
ze
d
to
a
s
s
es
s
th
e
p
er
f
o
r
m
an
ce
o
f
a
class
if
icatio
n
m
o
d
el.
I
t
ac
h
iev
es
th
is
b
y
c
o
n
tr
asti
n
g
th
e
p
r
e
d
icted
class
if
icatio
n
s
m
ad
e
b
y
th
e
m
o
d
el
with
th
e
ac
t
u
al
cl
ass
if
icatio
n
s
p
r
esen
t
in
t
h
e
d
ata
[
3
3
]
.
T
h
is
m
atr
ix
p
r
o
v
id
e
s
a
co
m
p
r
eh
e
n
s
iv
e
s
u
m
m
ar
y
o
f
th
e
ac
c
u
r
ate
an
d
in
ac
cu
r
ate
p
r
ed
ictio
n
s
m
a
d
e
b
y
th
e
m
o
d
el
o
n
a
test
in
g
d
ataset.
T
h
e
f
ig
u
r
es
with
in
th
e
c
o
n
f
u
s
io
n
m
atr
i
x
s
er
v
e
as
t
h
e
b
asis
f
o
r
c
o
m
p
u
tin
g
d
iv
er
s
e
p
er
f
o
r
m
a
n
c
e
m
etr
ics,
in
clu
d
in
g
ac
cu
r
ac
y
,
s
en
s
itiv
ity
,
an
d
s
p
ec
if
icity
.
T
h
ese
m
etr
ics
p
r
o
v
id
e
n
u
m
e
r
ical
ass
ess
m
en
t
s
o
f
t
h
e
m
o
d
el'
s
p
er
f
o
r
m
an
ce
o
n
a
test
d
ataset,
ea
ch
with
its
d
ef
in
ed
in
ter
p
r
et
atio
n
.
Acc
u
r
ac
y
r
ef
lects th
e
cl
ass
if
icatio
n
m
o
d
el'
s
ca
p
ab
ilit
y
to
ac
cu
r
ately
ca
teg
o
r
ize
in
s
tan
ce
s
.
A
c
c
ura
c
y
=
T
r
ue
Po
s
i
t
i
v
e
+
T
r
ue
N
eg
at
i
v
e
T
o
t
al
n
umb
er
of
s
am
p
l
e
s
(
5
)
Sp
ec
if
icity
p
er
tain
s
to
th
e
ca
p
ac
ity
o
f
a
class
if
icatio
n
m
o
d
el
to
ac
cu
r
ately
r
ec
o
g
n
ize
n
eg
at
iv
e
in
s
tan
ce
s
.
I
t
is
d
eter
m
in
ed
b
y
th
e
r
atio
o
f
tr
u
e
n
eg
ativ
e
p
r
ed
ictio
n
s
(
in
s
tan
ce
s
co
r
r
ec
tly
id
en
tifie
d
as
n
eg
ativ
e)
to
th
e
to
tal
n
u
m
b
er
o
f
ac
t
u
al
n
eg
ativ
e
i
n
s
tan
ce
s
in
th
e
test
d
ataset.
Evaluation Warning : The document was created with Spire.PDF for Python.
I
SS
N
:
2
0
8
8
-
8
7
0
8
I
n
t J E
lec
&
C
o
m
p
E
n
g
,
Vo
l.
15
,
No
.
2
,
Ap
r
il
20
25
:
1
6
0
2
-
1
6
1
1
1606
Sp
e
c
ifi
c
ity
=
T
r
ue
N
eg
at
i
v
e
T
r
ue
N
eg
at
i
v
e
+
F
al
s
e
Po
s
i
t
i
v
e
(
6
)
Sen
s
itiv
ity
,
also
r
ef
er
r
ed
to
as
r
ec
all,
s
ig
n
if
ies
th
e
p
r
o
f
i
cien
cy
o
f
a
class
if
icatio
n
m
o
d
el
in
ac
cu
r
ately
r
ec
o
g
n
izin
g
p
o
s
itiv
e
in
s
tan
ce
s
.
I
t
is
ca
lcu
lated
as
th
e
r
atio
o
f
tr
u
e
p
o
s
itiv
e
p
r
ed
ictio
n
s
(
in
s
tan
ce
s
co
r
r
ec
tly
id
en
tifie
d
as p
o
s
itiv
e)
to
t
h
e
to
tal
n
u
m
b
er
o
f
ac
tu
al
p
o
s
itiv
e
i
n
s
tan
ce
s
p
r
esen
t in
th
e
test
d
at
aset.
Se
n
s
itivit
y
=
T
r
ue
Po
s
i
t
i
v
e
T
r
ue
Po
s
i
t
i
v
e
+
F
al
s
e
N
eg
at
i
v
e
(
7
)
4.
RE
SU
L
T
S AN
D
D
I
SCU
SS
I
O
N
A
class
if
icatio
n
m
eth
o
d
o
lo
g
y
em
p
lo
y
in
g
L
DA,
SVM
an
d
b
ag
g
ed
tr
ee
class
if
ier
h
as
b
ee
n
d
ev
is
ed
to
ca
teg
o
r
ize
s
tr
o
k
e
lesi
o
n
s
in
S
W
I
im
ag
es.
T
h
e
in
p
u
t
f
ea
t
u
r
es
u
tili
ze
d
b
y
th
ese
class
if
ier
s
ar
e
d
er
iv
ed
f
r
o
m
R
OI
im
ag
es,
ex
tr
ac
ted
th
r
o
u
g
h
t
h
e
o
p
tim
al
s
eg
m
en
tatio
n
tech
n
i
q
u
e
p
r
o
p
o
s
ed
b
y
a
d
ap
tiv
e
th
r
es
h
o
ld
s
eg
m
en
tatio
n
m
eth
o
d
.
C
o
n
s
is
ten
t
o
u
tco
m
es
ar
e
o
b
s
er
v
ed
ac
r
o
s
s
all
s
ca
tter
p
lo
t
d
iag
r
am
s
f
o
r
ea
ch
f
ea
tu
r
e,
d
ep
ictin
g
co
r
r
ec
t
an
d
in
co
r
r
ec
t
class
if
icatio
n
s
.
Me
an
b
o
u
n
d
ar
y
a
n
d
s
tan
d
ar
d
d
ev
iatio
n
s
ca
tter
p
lo
t
d
iag
r
a
m
s
ar
e
in
clu
d
ed
to
ass
es
s
th
e
p
er
f
o
r
m
an
ce
o
f
ea
c
h
class
if
ier
.
T
h
e
d
etailed
ass
ess
m
en
t
o
f
th
e
s
tr
o
k
e
p
atien
t
m
o
d
el'
s
clas
s
if
icatio
n
p
er
f
o
r
m
an
ce
o
n
b
o
t
h
th
e
tr
ain
in
g
an
d
test
in
g
d
atasets
is
p
r
esen
ted
th
r
o
u
g
h
th
e
c
o
n
f
u
s
io
n
m
atr
ix
.
T
h
is
m
atr
ix
f
ac
ilit
ates
a
th
o
r
o
u
g
h
a
n
aly
s
is
o
f
th
e
m
o
d
el'
s
ac
cu
r
ac
y
an
d
er
r
o
r
s
with
in
in
d
iv
i
d
u
al
class
es,
o
f
f
er
in
g
i
n
s
ig
h
ts
in
to
co
r
r
ec
t a
n
d
in
c
o
r
r
ec
t c
lass
if
icatio
n
s
.
4
.
1
.
Cla
s
s
if
ica
t
io
n
a
na
ly
s
is
us
ing
m
a
chine le
a
rning
t
ec
hn
i
qu
es
T
h
e
co
m
p
r
eh
e
n
s
iv
e
ass
ess
m
en
t
o
f
th
e
s
tr
o
k
e
p
atien
t’
s
m
o
d
el
class
if
icatio
n
p
er
f
o
r
m
an
ce
o
n
b
o
th
th
e
tr
ain
in
g
an
d
test
in
g
d
atasets
is
illu
s
tr
ated
th
r
o
u
g
h
th
e
c
o
n
f
u
s
io
n
m
atr
i
x
.
T
h
is
m
atr
ix
en
ab
les
a
d
etailed
s
cr
u
tin
y
o
f
th
e
m
o
d
el'
s
ac
cu
r
ate
an
d
in
ac
c
u
r
ate
ca
teg
o
r
izatio
n
s
with
in
ea
ch
class
.
Ad
d
iti
o
n
ally
,
it
f
ac
ilit
ates
th
e
ca
lcu
latio
n
o
f
k
e
y
m
etr
ics
s
u
ch
as
p
r
ec
is
io
n
,
r
ec
all,
an
d
F1
-
s
co
r
e,
o
f
f
er
i
n
g
a
d
ee
p
e
r
u
n
d
er
s
tan
d
in
g
o
f
th
e
m
o
d
el’
s
p
r
e
d
ictiv
e
ca
p
a
b
ilit
ies.
Su
ch
an
aly
s
is
is
cr
u
cial
f
o
r
id
en
tify
in
g
ar
ea
s
f
o
r
im
p
r
o
v
e
m
en
t
an
d
en
s
u
r
in
g
r
eliab
le
p
er
f
o
r
m
an
ce
i
n
r
ea
l
-
w
o
r
ld
s
ce
n
ar
io
s
.
4
.
1
.
1
.
P
o
o
r
im
pro
v
e
m
ent
s
t
r
o
k
e
pa
t
ient
T
h
e
p
er
ce
n
tag
e
o
f
p
er
f
o
r
m
an
ce
ev
alu
atio
n
is
to
v
er
if
y
th
e
co
m
p
u
tatio
n
al
ac
cu
r
ac
y
ta
k
e
n
b
y
ea
ch
class
if
icatio
n
tech
n
iq
u
e.
Fig
u
r
e
2
id
en
tifie
s
th
e
p
e
r
f
o
r
m
an
ce
ev
alu
atio
n
f
o
r
class
if
icatio
n
tech
n
iq
u
e
b
ased
o
n
p
o
o
r
im
p
r
o
v
e
m
en
t
s
tr
o
k
e
p
ati
en
t.
Fro
m
th
e
tab
le,
ca
n
s
ee
th
at
SVMBT
ac
h
iev
ed
h
ig
h
est
ac
cu
r
ac
y
wh
ich
is
9
9
.
5
%
at
tr
ain
in
g
a
n
d
1
0
0
%
at
test
in
g
.
C
o
n
tin
u
ed
b
y
b
a
g
g
ed
tr
ee
wh
ich
p
r
o
d
u
ce
d
9
9
.
1
%
at
tr
ain
in
g
an
d
9
7
.
3
%
at
test
in
g
an
d
SVM
o
b
tain
ed
7
9
%
at
tr
ain
in
g
an
d
8
6
.
7
%
at
test
in
g
.
T
h
e
least
a
cc
u
r
ac
y
o
b
tain
e
d
b
y
L
DA
is
6
9
.
6
% a
t tr
ain
in
g
a
n
d
8
4
.
9
% a
t te
s
tin
g
.
Fig
u
r
e
2
.
Per
f
o
r
m
an
c
e
ev
alu
at
io
n
f
o
r
class
if
icatio
n
tech
n
iq
u
e
b
ased
o
n
p
o
o
r
im
p
r
o
v
em
e
n
t
s
tr
o
k
e
p
atien
t
4
.
1
.
2
.
M
o
dera
t
e
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Evaluation Warning : The document was created with Spire.PDF for Python.
I
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C
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I
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N:
2088
-
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Evaluation Warning : The document was created with Spire.PDF for Python.
I
SS
N
:
2
0
8
8
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I
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e
d
l
y
o
v
e
r
4
2
y
e
a
r
s
a
s
a
r
i
s
k
f
a
c
t
o
r
f
o
r
i
sc
h
e
mi
c
st
r
o
k
e
:
t
h
e
H
U
N
T
st
u
d
y
,
”
N
u
t
ri
e
n
t
s
,
v
o
l
.
1
5
,
n
o
.
5
,
p
.
1
2
3
2
,
F
e
b
.
2
0
2
3
,
d
o
i
:
1
0
.
3
3
9
0
/
n
u
1
5
0
5
1
2
3
2
.
[
3
7
]
T.
Li
a
n
g
e
t
a
l
.
,
“
A
g
e
a
t
sm
o
k
i
n
g
i
n
i
t
i
a
t
i
o
n
a
n
d
smo
k
i
n
g
c
e
ssa
t
i
o
n
i
n
f
l
u
e
n
c
e
t
h
e
i
n
c
i
d
e
n
c
e
o
f
s
t
r
o
k
e
i
n
C
h
i
n
a
:
a
1
0
-
y
e
a
r
f
o
l
l
o
w
-
u
p
st
u
d
y
,
”
J
o
u
r
n
a
l
o
f
T
h
ro
m
b
o
si
s
a
n
d
T
h
ro
m
b
o
l
y
si
s
,
v
o
l
.
5
6
,
n
o
.
1
,
p
p
.
1
7
5
–
1
8
7
,
A
p
r
.
2
0
2
3
,
d
o
i
:
1
0
.
1
0
0
7
/
s
1
1
2
3
9
-
023
-
0
2
8
1
2
-
y.
[
3
8
]
S
.
Zh
a
n
g
,
H
.
L
i
u
,
a
n
d
T
.
S
h
i
,
“
A
s
so
c
i
a
t
i
o
n
b
e
t
w
e
e
n
mi
g
r
a
i
n
e
a
n
d
r
i
s
k
o
f
st
r
o
k
e
:
a
s
y
s
t
e
ma
t
i
c
r
e
v
i
e
w
a
n
d
met
a
-
a
n
a
l
y
s
i
s,”
N
e
u
ro
l
o
g
i
c
a
l
S
c
i
e
n
c
e
s
,
v
o
l
.
4
3
,
n
o
.
8
,
p
p
.
4
8
7
5
–
4
8
8
9
,
A
u
g
.
2
0
2
2
,
d
o
i
:
1
0
.
1
0
0
7
/
s
1
0
0
7
2
-
0
2
2
-
0
6
0
7
4
-
z.
[
3
9
]
M
.
D
e
w
a
n
,
A
.
K
.
P
a
n
d
i
t
,
a
n
d
L.
G
o
y
a
l
,
“
A
ss
o
c
i
a
t
i
o
n
o
f
p
e
r
i
o
d
o
n
t
i
t
i
s
a
n
d
g
i
n
g
i
v
i
t
i
s
w
i
t
h
st
r
o
k
e
:
A
s
y
st
e
ma
t
i
c
r
e
v
i
e
w
a
n
d
me
t
a
-
a
n
a
l
y
si
s
,
”
D
e
n
t
a
l
a
n
d
Me
d
i
c
a
l
Pro
b
l
e
m
s
,
v
o
l
.
6
1
,
n
o
.
3
,
p
p
.
4
0
7
–
4
1
5
,
Ja
n
.
2
0
2
3
,
d
o
i
:
1
0
.
1
7
2
1
9
/
d
mp
/
1
5
8
7
9
3
.
B
I
O
G
RAP
H
I
E
S O
F
AUTH
O
RS
S
h
a
a
r
m
il
a
K
a
n
d
a
y
a
re
c
e
iv
e
d
th
e
b
a
c
h
e
l
o
r
o
f
e
lec
tri
c
a
l
e
n
g
i
n
e
e
rin
g
a
n
d
m
a
ste
r
in
e
lec
tri
c
a
l
e
n
g
in
e
e
rin
g
u
n
d
e
r
p
o
we
r
sy
ste
m
fro
m
Un
i
v
e
rsiti
Te
k
n
i
k
a
l
M
a
lay
sia
M
e
lak
a
(UTe
M
)
a
n
d
th
e
P
h
D
d
e
g
re
e
in
m
e
d
ica
l
ima
g
in
g
with
t
h
e
re
se
a
rc
h
a
c
c
o
rd
i
n
g
to
a
n
a
l
y
sis
o
f
b
ra
in
str
o
k
e
d
iag
n
o
sis
b
a
se
d
o
n
b
ra
i
n
m
a
g
n
e
ti
c
re
so
n
a
n
c
e
ima
g
in
g
(
M
RI)
u
s
in
g
m
a
c
h
in
e
lea
rn
i
n
g
.
S
h
e
re
g
istere
d
with
Bo
a
rd
o
f
En
g
i
n
e
e
r
M
a
lay
sia
(BEM
)
.
S
h
e
c
a
n
b
e
c
o
n
tac
ted
a
t
e
m
a
il
:
sh
a
a
rm
il
a
k
a
n
d
a
y
a
@y
a
h
o
o
.
c
o
m
.
No
r
h
a
shi
m
a
h
M
o
h
d
S
a
a
d
is
a
se
n
io
r
lec
tu
re
r
a
t
De
p
a
rtme
n
t
o
f
El
e
c
tro
n
ic
a
n
d
Co
m
p
u
ter
E
n
g
in
e
e
rin
g
Tec
h
n
o
lo
g
y
,
F
a
c
u
l
ty
o
f
El
e
c
tri
c
a
l
a
n
d
E
lec
tro
n
ic
E
n
g
in
e
e
rin
g
Tec
h
n
o
l
o
g
y
,
Un
i
v
e
rsiti
Tek
n
ik
a
l
M
a
lay
sia
M
e
lak
a
(UTe
M
).
S
h
e
re
c
e
iv
e
d
h
e
r
B.
En
g
.
i
n
m
e
d
ica
l
e
lec
tro
n
ics
(2
0
0
1
),
M.
E
n
g
.
in
tele
c
o
m
m
u
n
ica
ti
o
n
(2
0
0
4
)
a
n
d
P
h
D
i
n
d
ig
it
a
l
ima
g
e
p
ro
c
e
ss
in
g
(
2
0
1
5
)
fro
m
Un
i
v
e
rsiti
Tek
n
o
l
o
g
i
M
a
lay
sia
(UTM
).
He
r
re
se
a
r
c
h
a
re
a
in
v
o
l
v
e
d
d
ig
it
a
l
ima
g
e
a
n
d
sig
n
a
l
p
r
o
c
e
ss
in
g
,
c
o
m
p
u
ter
v
isi
o
n
a
n
d
m
e
d
ica
l
ima
g
in
g
.
S
h
e
re
g
istere
d
wi
th
Bo
a
rd
o
f
E
n
g
i
n
e
e
r
M
a
lay
sia
(BEM
),
M
a
lay
sia
Bo
a
rd
o
f
Tec
h
n
o
lo
g
ist
(M
BOT)
,
In
stit
u
te
fo
r
El
e
c
tri
c
a
l
a
n
d
El
e
c
tro
n
ics
En
g
in
e
e
r
(IE
EE
),
S
ig
n
a
l
P
r
o
c
e
ss
in
g
S
o
c
iety
(
S
P
S
),
I
EE
E
En
g
i
n
e
e
rin
g
i
n
M
e
d
ica
l
a
n
d
B
io
lo
g
y
S
o
c
iety
(E
M
BS
)
a
n
d
I
n
tern
a
ti
o
n
a
l
As
so
c
iati
o
n
o
f
En
g
i
n
e
e
rs (IAENG
).
S
h
e
c
a
n
b
e
c
o
n
tac
ted
a
t
e
m
a
il
:
n
o
r
h
a
sh
ima
h
@
u
tem
.
e
d
u
.
m
y
.
Abd
u
l
Ra
h
im
Ab
d
u
ll
a
h
is
an
a
ss
o
c
iate
p
ro
fe
ss
o
r
a
t
Un
iv
e
rsiti
Tek
n
i
k
a
l
M
a
lay
sia
M
e
lak
a
(UTe
M
),
M
a
lay
sia
.
He
h
o
ld
s
P
h
D
a
n
d
P
.
En
g
.
in
e
lec
tri
c
a
l
e
n
g
i
n
e
e
rin
g
,
Ce
rti
fie
d
En
e
rg
y
M
a
n
a
g
e
r,
CEM
(AE
M
AS)
with
sp
e
c
ializa
ti
o
n
i
n
sig
n
a
l
p
ro
c
e
ss
in
g
a
n
d
ima
g
e
p
ro
c
e
ss
in
g
fo
r
p
o
we
r
q
u
a
li
ty
.
Ab
d
u
l
Ra
h
im
A
b
d
u
ll
a
h
re
c
e
iv
e
d
h
is
B.
E
n
g
.
in
e
lec
tri
c
a
l
e
n
g
in
e
e
rin
g
(2
0
0
1
),
m
a
ste
r’s
d
e
g
re
e
in
e
lec
tri
c
a
l
e
n
g
in
e
e
rin
g
(2
0
0
4
)
a
n
d
P
h
D
i
n
p
o
we
r
e
lec
tro
n
ic
a
n
d
d
i
g
it
a
l
si
g
n
a
l
p
r
o
c
e
ss
in
g
(
2
0
1
1
)
fro
m
Un
iv
e
rsiti
Tek
n
o
l
o
g
i
M
a
lay
sia
(UTM
)
.
He
is
c
u
rre
n
tl
y
a
n
a
ss
o
c
iate
p
ro
fe
ss
o
r
in
t
h
e
De
p
a
rtme
n
t
o
f
P
o
we
r
El
e
c
tro
n
ic
a
n
d
Dri
v
e
,
F
a
c
u
lt
y
o
f
El
e
c
tri
c
a
l
En
g
i
n
e
e
rin
g
,
Un
iv
e
rsit
i
Tek
n
ik
a
l
M
a
lay
sia
M
e
lak
a
(
U
TeM)
a
n
d
h
e
a
d
o
f
a
d
v
a
n
c
e
d
ig
it
a
l
si
g
n
a
l
p
ro
c
e
ss
in
g
(AD
S
P
)
Lab
.
His
field
o
f
s
p
e
c
ializa
ti
o
n
in
c
l
u
d
e
s,
a
d
v
a
n
c
e
d
i
g
it
a
l
sig
n
a
l
p
ro
c
e
ss
in
g
,
re
h
a
b
il
i
tatio
n
e
n
g
in
e
e
rin
g
,
a
ss
isti
v
e
tec
h
n
o
l
o
g
y
a
n
d
p
o
we
r
e
lec
tro
n
ics
a
n
d
d
riv
e
.
He
c
a
n
b
e
c
o
n
tac
ted
a
t
e
m
a
il
:
a
b
d
u
lr@
u
tem
.
e
d
u
.
m
y
.
E
z
r
e
e
n
F
a
r
i
n
a
S
h
a
i
r
s
t
a
r
t
e
d
w
o
r
k
i
n
g
a
s
a
le
c
t
u
r
e
r
a
t
U
n
i
v
e
r
s
i
t
i
T
e
k
n
i
k
a
l
M
a
l
a
y
s
i
a
M
e
l
a
k
a
(
U
T
e
M
)
s
i
n
c
e
2
0
1
1
.
S
h
e
h
o
l
d
s
P
h
D
a
n
d
P
.
E
n
g
.
i
n
e
l
e
c
t
r
o
n
i
c
e
n
g
i
n
e
e
r
i
n
g
.
A
m
e
m
b
e
r
o
f
A
d
v
a
n
c
e
d
D
i
g
i
t
a
l
S
i
g
n
a
l
P
r
o
c
e
s
s
i
n
g
R
e
s
e
a
r
c
h
L
a
b
(
A
D
S
P
L
a
b
U
T
e
M
)
,
R
e
h
a
b
i
l
i
t
a
t
i
o
n
a
n
d
A
ss
i
s
t
i
v
e
T
e
c
h
n
o
l
o
g
y
R
e
s
e
a
r
c
h
G
r
o
u
p
(
R
E
A
T
UT
e
M
)
a
n
d
Ce
n
t
r
e
o
f
R
o
b
o
t
i
c
s
a
n
d
I
n
d
u
s
t
r
i
a
l
A
u
t
o
m
a
t
i
o
n
(
C
e
R
IA
U
T
e
M
)
.
S
h
e
h
a
s
s
k
i
l
l
s
i
n
s
y
s
te
m
i
d
e
n
t
i
f
i
c
a
t
i
o
n
,
M
A
T
L
A
B
s
i
m
u
l
a
t
i
o
n
,
c
o
n
t
r
o
l
a
n
d
i
n
s
t
r
u
m
e
n
t
a
t
i
o
n
,
m
o
d
e
l
i
n
g
a
n
d
s
i
m
u
l
a
t
i
o
n
,
e
l
e
c
t
r
o
m
y
o
g
r
a
p
h
y
,
d
i
g
i
t
a
l
s
i
g
n
a
l
p
r
o
c
e
ss
i
n
g
,
b
i
o
m
e
d
i
c
a
l
e
n
g
i
n
e
e
r
i
n
g
,
r
e
h
a
b
i
l
i
t
a
t
i
o
n
e
n
g
i
n
e
e
ri
n
g
,
b
i
o
s
i
g
n
a
l
p
r
o
c
e
s
s
i
n
g
,
e
x
o
s
k
e
l
e
t
o
n
r
o
b
o
t
i
c
s
,
e
x
o
s
k
e
l
e
t
o
n
s
,
w
a
v
e
le
t
t
ra
n
s
f
o
r
m
,
e
l
e
c
t
r
i
c
a
l
e
n
g
i
n
e
e
r
i
n
g
,
t
i
m
e
-
f
r
e
q
u
e
n
c
y
a
n
a
l
y
s
i
s
,
c
o
n
t
r
o
l
s
y
s
t
e
m
s
e
n
g
i
n
e
e
r
i
n
g
a
n
d
t
i
m
e
-
f
re
q
u
e
n
c
y
.
S
h
e
c
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Evaluation Warning : The document was created with Spire.PDF for Python.