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Dr
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Ma
ch
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s
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C
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:
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Dep
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Dr
u
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wh
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m
ea
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th
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tak
in
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o
f
v
ar
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icted
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e
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d
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ts
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is
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n
e
o
f
th
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m
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s
t
m
alig
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t
p
r
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b
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s
f
o
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a
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.
I
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ca
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d
estro
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a
lif
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d
a
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s
ily
.
I
n
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d
ev
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n
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r
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ca
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b
ea
r
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ter
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.
Acc
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d
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to
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aily
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.
[
1
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.
Nea
r
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t
2
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icted
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[
2
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.
Dis
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J
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to
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.
Stay
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Ois
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R
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[
3
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.
E
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I
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,
Vo
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11
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No
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5
,
Octo
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2
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2
1
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4
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m
o
v
in
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in
s
id
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th
e
city
.
W
h
en
we
g
o
to
a
n
ew
p
lace
,
we
ca
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n
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t
f
in
d
o
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t
t
h
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wh
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ar
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n
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5
m
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p
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in
B
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T
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an
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v
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d
if
f
er
en
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cr
im
in
al
ac
tiv
ities
[
4
]
.
W
e
n
ee
d
to
k
ee
p
a
s
p
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ial
f
o
cu
s
s
o
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at
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th
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e
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icted
to
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r
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g
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.
Ma
ch
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n
e
lear
n
in
g
,
a
m
aj
o
r
b
r
an
ch
o
f
ar
tific
ial
in
tellig
en
ce
(
AI
)
ca
n
p
r
o
v
id
e
a
s
o
lu
tio
n
to
th
e
p
r
o
b
lem
ju
s
t
d
is
cu
s
s
ed
ab
o
v
e.
T
h
e
ap
p
licatio
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s
o
f
m
ac
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e
lear
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in
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v
ar
y
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if
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en
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n
d
o
m
ain
s
,
e.
g
.
ca
n
ce
r
p
r
e
d
ictio
n
[
5
]
,
s
o
f
twa
r
e
f
au
lt
p
r
e
d
ictio
n
[
6
]
,
d
e
r
m
ato
lo
g
ical
d
is
ea
s
e
d
etec
tio
n
[
7
]
,
an
d
r
is
k
p
r
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[
8
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a
n
d
s
o
o
n
.
L
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k
ewise,
d
if
f
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en
t
co
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ith
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s
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to
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s
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f
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th
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wo
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f
p
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d
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to
d
r
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s
an
d
alc
o
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o
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T
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tr
ies
to
an
tici
p
ate
i
n
ad
v
an
ce
if
s
o
m
eo
n
e
h
as
th
e
r
is
k
o
f
b
ec
o
m
in
g
a
d
d
icted
to
d
r
u
g
s
an
d
alco
h
o
l.
First,
we
r
ea
d
r
elev
an
t
ar
ticles
f
r
o
m
d
i
f
f
er
en
t
n
atio
n
al
an
d
in
ter
n
atio
n
al
jo
u
r
n
als,
co
n
f
er
en
ce
p
r
o
ce
ed
in
g
s
,
an
d
m
ag
az
in
es
a
n
d
wr
ite
-
u
p
s
f
r
o
m
d
if
f
er
e
n
t
web
s
ites
an
d
n
ewsp
ap
e
r
s
.
T
h
en
we
talk
to
d
o
cto
r
s
an
d
d
r
u
g
-
an
d
-
alco
h
o
l
-
ad
d
icte
d
p
eo
p
le
an
d
f
in
d
s
o
m
e
d
r
i
v
in
g
f
ac
to
r
s
f
o
r
ad
d
ictio
n
s
u
ch
as
ag
e,
g
en
d
er
,
p
r
o
f
ess
io
n
,
h
ea
lth
ab
ilit
y
,
m
en
tal
p
r
ess
u
r
e,
tr
au
m
a
,
f
am
i
ly
-
an
d
-
f
r
ien
d
s
’
h
is
t
o
r
y
,
life
-
c
h
an
g
in
g
in
ci
d
en
ts
.
C
o
llectin
g
r
aw
d
ata
f
r
o
m
b
o
th
ad
d
icted
an
d
n
o
n
-
a
d
d
ict
ed
p
eo
p
le.
W
e
m
ad
e
an
ar
d
u
o
u
s
en
d
ea
v
o
r
f
o
r
co
m
p
ar
in
g
o
u
r
r
esu
lts
with
th
e
r
esu
lts
o
f
s
im
ilar
r
esear
ch
wo
r
k
s
ev
en
th
o
u
g
h
n
o
wo
r
k
h
as
b
ee
n
o
b
s
er
v
ed
,
wh
ich
ad
d
r
ess
es th
e
p
r
o
b
lem
o
f
p
r
e
d
ictio
n
o
f
ad
d
ictio
n
to
d
r
u
g
s
an
d
alc
o
h
o
l
.
W
e
h
av
e
f
o
llo
wed
an
d
s
tu
d
ie
d
r
elate
d
wo
r
k
s
in
th
e
n
ea
r
p
ast
d
o
n
e
b
y
s
o
m
e
o
th
er
r
esea
r
ch
er
s
o
n
d
r
u
g
s
an
d
ad
d
ictio
n
p
r
ed
ictio
n
an
d
u
n
d
er
s
tan
d
t
h
e
p
r
o
ce
s
s
es
an
d
m
eth
o
d
s
ex
p
r
ess
ed
b
y
t
h
em
.
Her
e
ar
e
s
o
m
e
d
escr
ip
tio
n
s
o
f
r
ec
e
n
t
n
o
tab
le
r
esear
ch
wo
r
k
o
n
m
ac
h
in
e
l
ea
r
n
in
g
.
Dah
iwad
e
et
a
l.
[
9
]
p
r
o
p
o
s
ed
a
g
en
e
r
al
d
is
ea
s
e
p
r
ed
ictio
n
s
y
s
tem
,
wh
ich
was
b
ased
o
n
m
ac
h
in
e
lea
r
n
in
g
al
g
o
r
ith
m
s
.
He
g
az
y
et
a
l.
[
1
0
]
p
r
o
p
o
s
ed
a
m
o
d
el
f
o
r
s
to
ck
m
a
r
k
et
p
r
ed
ic
tio
n
with
m
a
ch
in
e
lear
n
in
g
te
ch
n
o
lo
g
y
.
Alo
n
zo
et
a
l
.
[
1
1
]
p
r
esen
ted
a
d
e
tailed
co
m
p
ar
is
o
n
b
etwe
en
v
ar
io
u
s
m
ac
h
in
e
lear
n
in
g
al
g
o
r
ith
m
s
u
s
ed
p
r
e
d
ictio
n
an
d
ass
ess
m
en
t
o
f
co
co
n
u
t
s
u
g
a
r
q
u
ality
.
Hag
h
ia
b
i
et
a
l
.
[
1
2
]
wo
r
k
ed
o
n
p
r
ed
ictin
g
wate
r
q
u
ality
in
t
h
e
m
ac
h
i
n
e
l
ea
r
n
in
g
a
p
p
r
o
ac
h
.
Z
h
an
g
et
a
l
.
[
1
3
]
p
r
o
p
o
s
ed
a
m
eth
o
d
f
o
r
p
r
ed
ic
tin
g
d
aily
s
m
o
k
in
g
b
eh
av
i
o
r
b
ased
o
n
th
e
m
ac
h
in
e
-
lear
n
in
g
alg
o
r
ith
m
.
T
h
ey
u
s
ed
th
e
ex
tr
em
e
g
r
ad
ie
n
t
b
o
o
s
tin
g
(
XGBo
o
s
t)
d
ec
is
io
n
tr
ee
alg
o
r
ith
m
an
d
f
o
u
n
d
th
e
b
est
ac
cu
r
ac
y
o
f
8
4
.
1
1
%
with
m
ax
im
u
m
d
ep
th
f
iv
e
.
Alaa
et
a
l
.
[
1
4
]
p
r
o
p
o
s
ed
a
m
ac
h
i
n
e
lear
n
i
n
g
-
b
ased
m
o
d
el
f
o
r
p
r
ed
ictin
g
d
is
ea
s
e
r
is
k
o
f
ca
r
d
io
v
ascu
lar
o
n
B
io
b
an
k
p
ar
ticip
an
ts
.
Z
h
u
et
a
l
.
[
1
5
]
wo
r
k
ed
o
n
p
r
e
-
s
y
m
p
to
m
atic
d
etec
tio
n
o
f
t
o
b
ac
co
d
is
ea
s
e
with
h
y
p
er
s
p
ec
tr
al
im
a
g
e
an
d
m
ac
h
in
e
-
lear
n
in
g
class
if
ier
s
.
Z
h
an
g
et
a
l
.
[
1
6
]
wo
r
k
ed
to
p
r
ed
ict
h
u
m
an
im
m
u
n
o
d
ef
icien
c
y
v
ir
u
s
es (
HI
V)
p
r
o
g
n
o
s
is
an
d
m
o
r
tality
with
s
m
o
k
in
g
-
ass
o
ciate
d
d
eo
x
y
r
ib
o
n
u
cleic
ac
id
(
DNA)
an
d
m
ac
h
in
e
lear
n
in
g
class
if
ier
s
.
Gr
an
er
o
et
a
l
.
[
1
7
]
p
r
o
p
o
s
ed
a
m
o
d
el
f
o
r
p
r
ed
ic
tin
g
ex
ac
e
r
b
atio
n
s
o
f
o
b
s
tr
u
c
tiv
e
p
u
lm
o
n
ar
y
d
is
ea
s
e
with
m
ac
h
in
e
lear
n
i
n
g
f
ea
tu
r
es.
Fra
n
k
,
Hab
ac
h
,
an
d
Seetan
[
1
8
]
wo
r
k
ed
o
n
s
m
o
k
i
n
g
s
tatu
s
p
r
ed
ictio
n
with
m
ac
h
in
e
lear
n
in
g
an
d
s
tatis
t
ical
an
aly
s
is
.
L
o
g
is
tic
r
eg
r
ess
io
n
h
ad
th
e
b
est
p
er
f
o
r
m
an
ce
with
8
3
.
4
4
%
ac
cu
r
ac
y
,
8
3
%
p
r
ec
is
io
n
,
8
3
.
4
%
r
ec
all
an
d
8
3
.
2
%
F
-
m
e
asu
r
e
in
th
eir
wo
r
k
.
L
ee
et
a
l
.
[
1
9
]
wo
r
k
e
d
with
a
m
o
d
el
th
at
p
r
ed
icts
alco
h
o
l
u
s
e
d
is
o
r
d
er
b
y
c
h
ec
k
in
g
t
h
e
tr
ea
tm
en
t
-
s
ee
k
in
g
s
tatu
s
with
a
m
ac
h
in
e
lear
n
in
g
class
if
ier
.
T
h
eir
co
llected
d
ata
d
o
m
ain
s
wer
e
co
g
n
itiv
e,
m
o
o
d
,
im
p
u
ls
iv
ity
,
p
er
s
o
n
ality
,
ag
g
r
ess
io
n
,
an
d
ea
r
ly
life
s
tr
es
s
an
d
ch
ild
h
o
o
d
tr
au
m
a.
Kin
r
eic
h
et
a
l
.
[
2
0
]
p
r
o
p
o
s
ed
a
m
o
d
el
o
n
p
r
ed
ictin
g
th
e
r
is
k
o
f
alco
h
o
l
u
s
e
d
is
o
r
d
er
(
AUD)
u
s
in
g
m
ac
h
in
e
-
lear
n
i
n
g
tech
n
o
lo
g
y
.
Ku
m
ar
i
et
a
l
.
[
2
1
]
p
r
o
p
o
s
e
d
a
m
o
d
el
o
f
p
r
e
d
ictin
g
alc
o
h
o
l
ab
u
s
ed
u
s
in
g
m
ac
h
in
e
lear
n
in
g
tech
n
o
lo
g
y
.
T
h
ey
c
o
n
s
id
er
ed
a
g
e,
g
en
d
er
,
co
u
n
tr
y
,
eth
n
icity
,
e
d
u
c
atio
n
,
n
eu
r
o
ticis
m
,
o
p
en
n
ess
to
ex
p
er
ie
n
ce
,
ex
tr
a
v
er
s
io
n
,
ag
r
ee
a
b
len
ess
,
co
n
s
cien
tio
u
s
n
ess
,
im
p
u
ls
iv
e,
s
en
s
atio
n
s
ee
in
g
as
th
eir
m
o
d
els
'
f
ea
tu
r
e.
T
h
ese
f
ea
tu
r
es
co
n
s
id
er
ed
i
n
ANN
-
D
an
d
d
ay
,
wee
k
,
m
o
n
t
h
,
y
ea
r
,
d
e
ca
d
e
co
n
s
id
er
e
d
in
ANN
-
C
.
Hab
ib
et
a
l
.
[
2
2
]
h
a
d
d
o
n
e
a
s
tu
d
y
o
n
Pap
ay
a
d
is
ea
s
e
r
ec
o
g
n
itio
n
b
ased
o
n
a
m
ac
h
in
e
lear
n
in
g
class
if
icatio
n
tech
n
iq
u
e.
T
h
is
p
ap
er
is
o
r
g
an
ized
as
f
o
llo
ws
:
Sec
tio
n
1
d
escr
ib
es
th
e
in
tr
o
d
u
ctio
n
.
Sectio
n
2
g
iv
es
a
s
h
o
r
t
r
ev
iew
o
f
t
h
e
r
esear
c
h
m
et
h
o
d
.
Sectio
n
3
ex
p
lain
s
th
e
r
esu
lt
an
d
d
is
cu
s
s
io
n
.
Sectio
n
4
co
n
tain
s
th
e
co
n
clu
s
io
n
.
2.
RE
S
E
ARCH
M
E
T
H
O
D
T
h
e
s
y
s
tem
ar
ch
itectu
r
e
o
f
t
h
e
p
r
ed
ictio
n
o
f
ad
d
ictio
n
to
d
r
u
g
s
an
d
alco
h
o
l
is
as
d
em
o
n
s
tr
ated
in
Fi
g
u
r
e
1
.
He
r
e
a
u
s
er
h
as
to
a
n
s
wer
th
e
q
u
esti
o
n
s
th
r
o
u
g
h
a
web
ap
p
licatio
n
.
T
h
e
in
f
o
r
m
a
tio
n
co
llected
f
r
o
m
th
e
u
s
er
will
g
o
to
th
e
s
er
v
er
an
d
f
r
o
m
th
er
e
to
t
h
e
e
x
p
er
t
s
y
s
tem
.
T
h
e
o
u
tco
m
e
will
b
e
d
eter
m
in
ed
b
ased
o
n
th
e
in
p
u
t
r
ec
eiv
ed
b
y
ap
p
ly
in
g
a
lo
g
is
tic
r
eg
r
ess
io
n
alg
o
r
ith
m
o
n
th
e
p
r
o
ce
s
s
ed
d
ata.
A
d
e
f
in
ite
r
esu
lt
will
b
e
p
r
ep
ar
e
d
in
ter
m
s
o
f
th
e
o
u
tp
u
t o
b
tain
ed
f
r
o
m
th
e
m
o
d
el.
T
h
e
r
esu
lts
o
b
tain
ed
th
r
o
u
g
h
s
p
e
cif
ic
f
o
r
m
attin
g
ca
n
b
e
v
iewe
d
th
r
o
u
g
h
th
e
we
b
ap
p
licatio
n
.
W
e
h
av
e
co
llected
5
1
0
d
ata
o
f
b
o
th
ad
d
icted
an
d
n
o
n
-
ad
d
icted
p
eo
p
le
am
o
n
g
t
h
em
8
0
%
h
as
b
ee
n
tr
ea
ted
as
tr
ain
in
g
d
ata
an
d
2
0
%
as
test
d
ata.
Ou
r
d
ata
co
llectio
n
an
d
d
ata
p
r
ep
r
o
ce
s
s
in
g
tech
n
iq
u
es’
lay
o
u
t
will
b
e
s
h
o
wn
in
th
e
n
ex
t
s
ec
tio
n
.
W
e
h
av
e
u
s
ed
n
in
e
m
ac
h
in
e
-
lear
n
i
n
g
alg
o
r
ith
m
s
m
en
tio
n
ed
ea
r
lier
.
W
e
h
av
e
ca
lcu
lated
th
e
ac
cu
r
ac
y
th
r
ee
tim
es.
T
h
e
f
ir
s
t
tim
e
ac
cu
r
ac
y
was
Evaluation Warning : The document was created with Spire.PDF for Python.
I
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n
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f a
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d
ictio
n
to
d
r
u
g
s
a
n
d
a
lco
h
o
l
u
s
in
g
ma
ch
i
n
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lcu
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b
ef
o
r
e
u
s
in
g
p
r
i
n
ci
p
al
co
m
p
o
n
en
t
an
aly
s
is
(
PC
A)
o
n
th
e
p
r
o
ce
s
s
ed
d
ata,
an
d
t
h
en
th
e
s
ec
o
n
d
tim
e
it
was
ca
lcu
lated
af
ter
u
s
in
g
PC
A
an
d
f
in
ally
,
th
e
ac
cu
r
ac
ies
wer
e
ca
lcu
lated
u
s
in
g
th
e
alg
o
r
it
h
m
o
n
th
e
u
n
p
r
o
ce
s
s
ed
d
ata.
W
e
h
av
e
e
v
alu
ated
th
e
class
if
ier
s
b
ased
o
n
ac
cu
r
ac
y
a
n
d
o
th
er
m
etr
i
cs
lik
e
s
en
s
itiv
ity
,
s
p
ec
if
icity
,
p
r
ec
is
io
n
,
r
ec
all
,
an
d
F
1
-
s
co
r
e.
T
h
ese
wo
r
k
i
n
g
p
r
o
ce
s
s
es
h
av
e
b
ee
n
d
escr
ib
ed
in
th
e
f
o
llo
win
g
f
lo
w
d
iag
r
am
in
Fig
u
r
e
2
.
Fig
u
r
e
1
.
T
h
e
s
y
s
tem
ar
ch
itect
u
r
e
o
f
t
h
e
p
r
e
d
ictio
n
o
f
ad
d
ictio
n
to
d
r
u
g
s
an
d
alco
h
o
l
Fig
u
r
e
2
.
T
h
e
m
eth
o
d
o
lo
g
y
a
p
p
lied
f
o
r
p
r
ed
ictin
g
th
e
ad
d
ictio
n
to
d
r
u
g
s
an
d
alco
h
o
l
W
e
h
av
e
r
u
n
n
i
n
e
m
ac
h
in
e
-
le
ar
n
in
g
alg
o
r
ith
m
s
o
n
p
r
o
ce
s
s
e
d
d
ata
s
et
wh
er
e
th
e
n
u
m
b
er
o
f
f
ea
tu
r
es
was
2
3
.
T
h
en
we
h
av
e
u
s
ed
th
e
PC
A
th
at
is
a
k
in
d
o
f
f
e
atu
r
e
ex
tr
ac
tio
n
m
eth
o
d
to
g
r
ab
th
e
u
n
d
er
ly
i
n
g
v
ar
ian
ce
o
f
d
ata
in
o
r
th
o
g
o
n
al
lin
ea
r
p
r
o
jectio
n
s
.
T
h
e
i
n
d
ep
en
d
en
t
u
s
ed
v
ar
iab
le
o
f
a
m
o
d
el
is
k
n
o
wn
as
th
e
d
im
en
s
io
n
ality
o
f
th
at
m
o
d
el.
T
h
e
n
u
m
b
e
r
o
f
v
a
r
iab
les
ca
n
b
e
r
ed
u
ce
d
u
s
in
g
a
PC
A;
o
n
ly
th
e
im
p
o
r
tan
t
v
ar
iab
les
wer
e
s
elec
ted
f
o
r
th
e
n
ex
t
task
.
Fig
u
r
e
3
h
as
s
h
o
wn
th
e
s
cr
ee
p
lo
t
wh
er
e
a
v
ar
ian
ce
is
ex
p
lain
ed
in
th
e
y
-
ax
is
a
n
d
n
u
m
b
er
f
ea
tu
r
e
s
s
h
o
wed
in
th
e
x
-
ax
is
.
Usi
n
g
th
e
s
cr
ee
p
lo
t
an
d
9
0
%
v
ar
ia
n
ce
ex
p
lain
ed
as
a
th
r
esh
o
ld
,
we
h
av
e
ca
lcu
lated
o
u
r
p
r
in
ci
p
al
co
m
p
o
n
e
n
t
n
u
m
b
er
an
d
th
e
n
u
m
b
er
is
1
4
.
N
o
r
m
ally
it
co
m
b
in
es
h
ig
h
ly
c
o
r
r
elate
d
v
ar
iab
les to
b
u
ild
u
p
a
s
h
o
r
t a
r
t
if
icial
s
et
o
f
v
ar
iab
les [
2
3
]
.
k
-
NN
is
a
s
im
p
le
s
u
p
er
v
is
ed
m
ac
h
in
e
-
lear
n
i
n
g
alg
o
r
ith
m
.
k
-
NN
alg
o
r
ith
m
g
r
ab
s
s
im
ilar
t
h
in
g
s
th
at
ex
is
t
in
a
clo
s
e
n
eig
h
b
o
r
h
o
o
d
[
2
4
]
.
Min
k
o
wsk
i
d
is
tan
c
e
b
etwe
en
th
e
q
u
er
y
p
o
in
ts
to
o
th
er
p
o
in
ts
is
d
eter
m
in
ed
b
y
u
s
in
g
(
1
)
.
(
∑
(
|
−
|
)
=
1
)
1
⁄
(
1
)
Su
p
p
o
r
t
v
ec
to
r
m
ac
h
in
e
is
a
s
u
p
er
v
is
ed
m
ac
h
in
e
-
lea
r
n
in
g
alg
o
r
ith
m
.
Data
item
s
ar
e
p
lace
d
in
n
-
d
im
en
s
io
n
al
s
p
ac
e
an
d
th
e
v
alu
es
o
f
th
e
f
ea
tu
r
es
p
r
esen
t
th
e
p
ar
ticu
lar
co
o
r
d
in
ate
[
2
4
]
.
SVM
b
u
ild
s
a
m
ax
im
u
m
m
a
r
g
in
s
ep
ar
at
o
r
,
wh
ich
is
u
s
ed
f
o
r
m
ak
in
g
d
ec
i
s
io
n
b
o
u
n
d
ar
ies
with
th
e
lar
g
e
s
t
p
o
s
s
ib
le
d
is
tan
ce
.
W
is
f
o
r
weig
h
t v
ec
to
r
a
n
d
X
i
s
f
o
r
is
th
e
s
et
o
f
p
o
in
ts
.
B
y
u
s
in
g
(
2
)
,
we
ca
n
f
in
d
o
u
t th
e
s
e
p
ar
ato
r
.
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.
11
,
No
.
5
,
Octo
b
e
r
2
0
2
1
:
4
4
7
1
-
4
4
8
0
4474
W.X
+ b
= 0
(
2
)
L
o
g
is
tic
r
eg
r
ess
io
n
u
s
es
lo
g
is
t
ic
f
u
n
ctio
n
an
d
th
is
lo
g
is
tic
f
u
n
ctio
n
s
er
v
es
as
a
s
ig
m
o
id
f
u
n
ctio
n
.
An
s
-
s
h
ap
ed
cu
r
v
e
tak
es th
e
r
ea
l
v
alu
es a
n
d
p
u
t t
h
em
b
etwe
en
0
to
1
[
2
4
]
.
T
h
e
lo
g
is
tic
f
u
n
ctio
n
is
g
iv
en
as (
3
)
:
(
)
=
1
1
+
−
(
3
)
Naïv
e
B
ay
es
is
o
n
e
o
f
th
e
o
l
d
est
alg
o
r
ith
m
s
o
f
m
ac
h
in
e
lea
r
n
in
g
.
T
h
is
alg
o
r
ith
m
is
b
ased
o
n
B
ay
es
th
eo
r
em
an
d
b
asic
s
tatis
tic
s
.
I
t
ex
ten
d
s
attr
ib
u
tes
u
s
in
g
Gau
s
s
ian
d
is
tr
ib
u
tio
n
[
2
3
]
.
T
h
e
Ga
u
s
s
ian
d
is
tr
ib
u
tio
n
with
m
ea
n
an
d
s
tan
d
ar
d
d
ev
iat
io
n
is
d
escr
ib
ed
in
(
4
)
.
(
=
|
)
=
1
√
2
2
ℯ
−
(
−
)
2
2
2
(
4
)
ML
P
m
ea
n
s
m
u
ltil
ay
er
p
er
ce
p
tio
n
.
ML
P
h
as
a
co
m
b
in
atio
n
o
f
m
u
ltil
ay
er
n
eu
r
o
n
s
.
T
h
e
f
ir
s
t
lay
er
is
th
e
in
p
u
t
lay
e
r
,
th
e
s
ec
o
n
d
lay
er
ca
lls
th
e
h
id
d
e
n
la
y
er
,
an
d
th
e
th
ir
d
lay
er
is
ca
lled
th
e
o
u
tp
u
t
lay
er
.
I
t
tak
es
in
p
u
t d
ata
th
r
o
u
g
h
th
e
in
p
u
t la
y
er
an
d
g
iv
es th
e
o
u
tp
u
t f
r
o
m
th
e
o
u
tp
u
t la
y
er
[
2
4
]
.
C
AR
T
i
s
a
d
is
tr
ib
u
tio
n
-
f
r
ee
d
ec
is
io
n
tr
ee
lear
n
in
g
tech
n
iq
u
e.
T
h
e
d
ec
is
io
n
tr
ee
is
a
tr
ee
-
b
ased
m
o
d
el.
T
h
e
d
iv
id
e
-
an
d
-
co
n
q
u
er
m
et
h
o
d
is
u
s
ed
f
o
r
m
ak
in
g
th
e
t
r
ee
d
ia
g
r
am
.
T
h
e
Gin
i
in
d
ex
is
ap
p
lied
in
C
AR
T
wh
er
e
Gin
i
in
d
ex
f
in
d
s
o
u
t
t
h
e
im
p
u
r
ity
o
f
D
,
D
r
e
p
r
esen
ts
th
e
tr
ain
in
g
tu
p
les [
2
3
]
.
Gin
i in
d
ex
is
d
ef
in
ed
as b
el
o
w:
G
i
ni
(
D
)
=
1
-
∑
2
=
1
(
5)
Fre
u
n
d
a
n
d
Sch
a
p
ir
e
p
r
o
p
o
s
ed
Ad
aBo
o
s
t
in
1
9
9
6
.
I
t
m
a
k
es
a
class
if
ier
with
a
co
m
b
in
atio
n
o
f
m
u
ltip
le
p
o
o
r
ly
p
e
r
f
o
r
m
in
g
cl
ass
if
ier
.
I
t se
ts
th
e
weig
h
t o
f
class
if
ier
s
an
d
tr
ain
s
th
e
d
ata
in
ea
ch
iter
atio
n
[
2
3
]
.
B
y
u
s
in
g
(
6
)
,
we
ca
n
co
m
p
u
te
th
e
er
r
o
r
r
ate
o
f
ea
ch
tu
p
le.
err
o
r
(
M
i
)
=
∑
×
(
)
=
(
6)
R
an
d
o
m
f
o
r
est
m
ak
es
a
lar
g
e
co
llectio
n
o
f
d
e
-
co
r
r
elate
d
tr
ee
s
f
o
r
p
r
ed
ictio
n
p
u
r
p
o
s
es.
I
t
p
er
f
o
r
m
s
s
p
lit
-
v
ar
iab
le
r
an
d
o
m
izatio
n
.
T
h
e
r
an
d
o
m
f
o
r
est
h
as
a
s
m
aller
f
ea
tu
r
e
s
ea
r
ch
s
p
ac
e
at
ea
ch
tr
ee
s
p
lit
[
2
3
]
.
Gr
ad
ien
t
b
o
o
s
tin
g
m
ac
h
in
e
b
u
ild
s
an
en
s
em
b
le
o
f
s
h
allo
w
tr
ee
s
with
tr
ee
lear
n
in
g
an
d
im
p
r
o
v
in
g
tech
n
iq
u
e.
GB
M
wo
r
k
s
with
th
e
p
r
in
cip
le
o
f
b
o
o
s
tin
g
wea
k
lear
n
er
s
iter
ativ
ely
b
y
s
h
if
tin
g
f
o
cu
s
to
war
d
s
p
r
o
b
lem
atic
o
b
s
er
v
atio
n
.
I
t
p
r
e
p
ar
es
a
s
ta
g
e
-
wis
e
f
ash
io
n
m
o
d
el
lik
e
o
th
er
b
o
o
s
tin
g
m
eth
o
d
s
an
d
n
o
r
m
alize
s
th
em
with
ar
b
itra
r
y
d
if
f
er
e
n
tiab
le
f
u
n
ctio
n
s
[
2
5
]
.
Fig
u
r
e
3
.
Scr
ee
p
lo
t w
h
er
e
t
h
e
n
u
m
b
e
r
o
f
p
r
in
cip
al
c
o
m
p
o
n
e
n
ts
is
s
h
o
wn
in
r
ed
co
l
o
r
W
e
n
o
t
o
n
ly
ca
lcu
lated
th
e
ac
cu
r
ac
y
o
f
s
ev
er
al
alg
o
r
ith
m
s
b
u
t
also
ca
lcu
lated
s
en
s
itiv
ity
,
s
p
ec
if
icity
,
p
r
ec
is
io
n
,
r
ec
all,
F
1
-
s
co
r
e
,
an
d
R
OC
cu
r
v
e
an
d
c
o
n
f
u
s
io
n
m
atr
ix
o
f
ea
c
h
alg
o
r
ith
m
.
I
n
th
e
ca
s
e
o
f
m
o
d
el
ev
o
lu
tio
n
,
ce
r
tain
class
if
ier
s
h
av
e
b
ee
n
m
ea
s
u
r
e
d
b
ased
o
n
th
e
test
d
ata
s
et
f
o
r
b
etter
m
ea
s
u
r
em
en
t.
Sen
s
itiv
ity
is
th
e
tr
u
e
p
o
s
itiv
e
r
ate.
I
t
is
th
e
r
atio
o
f
h
o
w
m
an
y
p
o
s
itiv
e
tu
p
les
ar
e
co
r
r
ec
tly
d
iag
n
o
s
ed
.
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:
2
0
8
8
-
8
7
0
8
P
r
ed
ictio
n
o
f a
d
d
ictio
n
to
d
r
u
g
s
a
n
d
a
lco
h
o
l
u
s
in
g
ma
ch
i
n
e
lea
r
n
in
g
:
A
c
a
s
e
…
(
Md
.
A
r
ifu
l I
s
la
m
A
r
if
)
4475
Sp
ec
if
icity
is
th
e
tr
u
e
n
eg
ati
v
e
r
ate.
I
t
is
th
e
r
atio
o
f
h
o
w
m
an
y
n
eg
ativ
e
t
u
p
les
ar
e
co
r
r
ec
tl
y
d
iag
n
o
s
ed
.
Pre
cisi
o
n
is
th
e
m
ea
s
u
r
em
en
t
o
f
ex
ac
tn
ess
.
I
t
is
th
e
r
atio
o
f
tr
u
e
p
o
s
itiv
e
v
alu
e
a
n
d
p
r
ed
ic
ted
p
o
s
itiv
e
v
alu
e.
T
h
e
r
ec
all
is
th
e
m
ea
s
u
r
em
en
t
o
f
co
m
p
leten
ess
.
I
t
is
th
e
r
atio
o
f
tr
u
e
p
o
s
itiv
e
v
alu
e
an
d
t
r
u
e
n
eg
ativ
e
v
al
u
e.
F
1
-
s
co
r
e
is
th
e
m
ea
s
u
r
em
en
t o
f
th
e
h
ar
m
o
n
ic
m
ea
n
o
f
r
ec
all
an
d
p
r
ec
is
io
n
.
I
t c
o
n
s
id
er
s
b
o
th
f
alse p
o
s
itiv
e
an
d
f
alse
n
eg
ativ
e
v
alu
es
f
o
r
ca
lcu
latio
n
[
2
3
]
.
T
h
e
c
o
n
f
u
s
io
n
m
at
r
ix
is
o
n
e
o
f
th
e
m
o
s
t
im
p
o
r
ta
n
t
p
er
f
o
r
m
an
ce
s
o
f
m
ea
s
u
r
em
en
t
tech
n
iq
u
es
f
o
r
m
ac
h
in
e
lear
n
in
g
class
if
icat
io
n
.
I
t
ca
n
p
er
f
o
r
m
o
n
th
e
class
if
icatio
n
m
o
d
els
with
th
e
s
et
o
f
test
d
ata
an
d
p
r
o
v
id
e
th
e
tr
u
e
p
o
s
itiv
e
v
alu
e
a
n
d
t
r
u
e
n
e
g
ativ
e
v
al
u
e,
f
alse
-
p
o
s
itiv
e
v
alu
e
an
d
f
alse
-
n
eg
ativ
e
v
al
u
e
in
a
tab
u
lar
f
o
r
m
at
[
2
3
]
.
A
f
ea
tu
r
e
s
et
is
d
ev
elo
p
e
d
b
y
an
al
y
zin
g
t
h
e
m
ain
ca
u
s
es
o
f
d
r
u
g
ad
d
ictio
n
,
th
r
o
u
g
h
wh
ic
h
it
is
p
o
s
s
ib
le
to
id
en
tify
th
e
p
er
s
o
n
ad
d
icted
t
o
d
r
u
g
s
.
T
h
e
f
ea
tu
r
e
lis
ts
o
f
d
r
u
g
ad
d
ictio
n
ar
e
s
h
o
wn
in
T
a
b
le
1
.
=
+
×
100%
(7
)
=
+
×
1
0
0
%
(8
)
=
+
×
100%
(9
)
=
+
×
1
0
0
%
(1
0
)
1
s
core
=
2
+
×
100%
(1
1
)
T
o
id
e
n
tify
th
e
r
is
k
o
f
b
ec
o
m
in
g
a
d
d
icted
to
d
r
u
g
s
we
h
av
e
co
n
s
id
er
e
d
ea
c
h
o
f
th
ese
f
a
cto
r
s
.
W
e
h
av
e
f
o
u
n
d
o
u
t
a
b
o
u
t
th
ese
f
a
cto
r
s
b
y
talk
in
g
to
v
ar
io
u
s
p
h
y
s
ician
s
,
web
s
ites
[
2
6
]
-
[
3
0
]
,
a
n
d
ar
ticles.
T
h
e
d
ata
s
et
is
a
lar
g
e
co
llectio
n
o
f
n
e
ce
s
s
ar
y
an
d
r
elata
b
le
co
o
r
d
in
ates
th
at
ca
n
ea
s
ily
b
e
ac
ce
s
s
ed
an
d
c
h
an
g
e
d
.
W
e
h
av
e
s
ee
n
s
o
m
eo
n
e
ar
o
u
n
d
u
s
tak
in
g
d
r
u
g
s
b
u
t
it
was
a
s
ec
r
et,
an
d
at
th
e
tr
ai
n
s
tatio
n
an
d
b
u
s
s
tatio
n
d
r
u
g
ad
d
icts
r
ef
u
s
ed
to
h
elp
.
T
h
e
n
we
h
av
e
d
ec
id
ed
to
g
o
to
th
e
d
r
u
g
ad
d
ictio
n
ce
n
ter
an
d
r
eh
ab
ilit
atio
n
ce
n
ter
.
W
e
h
av
e
also
co
llec
ted
in
f
o
r
m
atio
n
f
r
o
m
s
o
m
e
p
r
iv
ate
r
e
h
ab
ilit
atio
n
ce
n
ter
s
an
d
clin
ics.
New
Mu
k
ti
C
lin
ic
[
3
1
]
an
d
B
r
ain
an
d
Min
d
Ho
s
p
ital
[
3
2
]
h
elp
ed
u
s
with
th
e
in
f
o
r
m
atio
n
.
I
n
a
d
d
itio
n
t
o
p
r
o
v
id
i
n
g
in
f
o
r
m
atio
n
,
we
ca
n
lear
n
f
r
o
m
th
eir
co
n
s
u
lta
n
ts
an
d
d
o
cto
r
s
ab
o
u
t
m
an
y
m
o
r
e
im
p
o
r
tan
t
f
ac
to
r
s
.
T
h
u
s
,
we
wer
e
ab
le
to
co
llect
d
ata
o
f
5
1
0
p
e
o
p
le
b
a
s
ed
o
n
2
3
f
ac
to
r
s
.
T
h
e
r
e
ar
e
3
0
5
-
d
r
u
g
a
d
d
icts
’
in
f
o
r
m
atio
n
an
d
2
0
5
h
ea
lth
y
p
eo
p
le'
s
in
f
o
r
m
atio
n
.
W
e
h
a
v
e
also
co
llected
o
u
r
d
ata
f
r
o
m
Daf
f
o
d
il
I
n
ter
n
atio
n
al
Un
iv
er
s
ity
,
Sy
lh
et
E
n
g
in
ee
r
in
g
C
o
lleg
e,
B
eg
u
m
R
o
k
ey
a
U
n
iv
er
s
ity
,
New
M
u
k
ti
C
lin
ic,
B
r
ain
a
n
d
Min
d
Ho
s
p
ital
,
an
d
s
o
m
e
o
th
er
p
lace
s
.
Data
co
llectio
n
was
th
e
m
o
s
t
ch
allen
g
in
g
task
f
o
r
u
s
.
Nev
er
th
eless
,
we
m
an
ag
ed
to
co
llect
s
o
m
e
d
ata
wh
er
e
th
er
e
wer
e
s
o
m
e
m
is
s
in
g
d
ata,
ca
teg
o
r
ical
d
at
a
,
n
u
m
er
ical
an
d
tex
t
d
ata.
T
h
en
we
h
av
e
d
ec
id
e
d
th
at
th
r
o
u
g
h
d
ata
p
r
o
ce
s
s
in
g
we
wo
u
ld
m
ak
e
th
is
d
ata
s
u
itab
le
f
o
r
d
if
f
er
e
n
t
alg
o
r
it
h
m
s
.
Fig
u
r
e
4
h
as
d
escr
ib
ed
o
u
r
d
ata
p
r
ep
r
o
ce
s
s
in
g
wo
r
k
.
First,
we
s
tar
ted
th
e
wo
r
k
o
f
d
ata
clea
n
in
g
.
T
ab
le
1
.
Featu
r
es f
o
r
d
r
u
g
s
an
d
alco
h
o
l
p
r
ed
ictio
n
F
e
a
t
u
r
e
N
a
me
Ev
i
d
e
n
c
e
B
a
se
d
-
on
F
e
a
t
u
r
e
N
a
me
Ev
i
d
e
n
c
e
B
a
se
d
-
on
F
e
a
t
u
r
e
N
a
me
Ev
i
d
e
n
c
e
B
a
se
d
-
on
Li
v
i
n
g
w
i
t
h
f
a
mi
l
y
[
2
7
]
S
t
a
y
a
l
o
n
e
[
2
8
]
S
t
a
y
o
u
t
si
d
e
a
t
n
i
g
h
t
[
3
0
]
I
n
t
e
r
e
st
i
n
n
o
r
m
a
l
a
c
t
i
v
i
t
i
e
s
[
2
7
]
H
o
w
m
u
c
h
y
o
u
c
a
r
e
a
b
o
u
t
y
o
u
r
se
l
f
[
2
7
]
Th
i
n
k
t
h
a
t
d
r
u
g
a
d
d
i
c
t
i
o
n
c
a
n
b
e
a
s
o
l
u
t
i
o
n
[
2
8
]
A
g
e
[
2
9
]
Jo
b
l
o
si
n
g
[
3
0
]
Lo
si
n
g
w
e
i
g
h
t
[
2
7
]
R
e
si
d
i
n
g
a
d
d
r
e
ss
[
3
0
]
S
e
x
u
a
l
h
a
r
a
ssm
e
n
t
[
2
7
]
H
a
v
e
a
d
d
i
c
t
e
d
f
r
i
e
n
d
s
[
2
9
]
P
r
o
f
e
ssi
o
n
[
3
0
]
G
e
n
d
e
r
[
2
9
]
R
e
a
s
o
n
t
o
b
e
c
o
me
a
d
d
i
c
t
e
d
[
2
9
]
D
i
st
a
n
c
e
w
i
t
h
f
r
i
e
n
d
s
a
n
d
f
a
m
i
l
y
[
2
8
]
H
a
v
i
n
g
o
d
d
s
l
e
e
p
p
a
t
t
e
r
n
[
3
0
]
A
n
a
d
d
i
c
t
e
d
p
e
r
s
o
n
a
t
h
o
me
[
2
6
]
W
o
r
k
i
n
g
e
f
f
i
c
i
e
n
c
y
[
2
9
]
F
a
c
e
d
a
n
y
t
r
a
u
ma
[
2
6
]
S
t
r
e
ss c
o
n
t
r
o
l
l
i
n
g
s
k
i
l
l
s
[
3
0
]
R
e
l
a
t
i
o
n
s
h
i
p
p
r
o
b
l
e
m
[
2
6
]
Ec
o
n
o
mi
c
s
t
a
t
u
s
[
2
9
]
W
e
h
av
e
ch
ec
k
e
d
if
th
er
e
is
a
n
u
ll
v
al
u
e
in
th
e
d
ata
s
et.
W
e
h
av
e
th
en
e
n
co
d
e
d
th
e
lev
el
th
at
co
n
v
er
ts
th
e
tex
t
d
ata
to
n
u
m
er
ical
d
ata.
W
e
h
av
e
s
o
lv
e
d
t
h
e
m
is
s
in
g
v
al
u
e
p
r
o
b
lem
u
s
i
n
g
th
e
im
p
u
ter
an
d
m
ed
ian
.
T
h
en
we
h
av
e
ch
ec
k
e
d
if
th
er
e
is
a
n
o
is
y
v
alu
e
in
th
e
d
ata
s
et
u
s
in
g
a
b
o
x
p
lo
t.
W
e
h
av
e
f
o
u
n
d
s
o
m
e
n
o
is
y
v
alu
es
in
o
u
r
d
ata
s
et.
Ou
r
d
ata
s
et
h
a
d
a
n
o
is
y
v
alu
e
i
n
th
e
‘
ag
e’
f
ea
tu
r
e
a
n
d
we
s
o
l
v
ed
th
e
n
o
is
y
v
alu
e
p
r
o
b
lem
with
an
o
u
tlier
q
u
an
ti
le
.
T
h
en
we
h
av
e
d
r
o
p
p
ed
o
u
r
o
u
tco
m
e
f
ea
tu
r
e,
th
at
was,
th
e
ad
d
icted
co
lu
m
n
.
W
e
f
in
ally
h
av
e
th
e
p
r
o
ce
s
s
ed
d
ata
s
et
in
o
u
r
h
an
d
s
.
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.
11
,
No
.
5
,
Octo
b
e
r
2
0
2
1
:
4
4
7
1
-
4
4
8
0
4476
Fig
u
r
e
4
.
Step
s
o
f
d
ata
p
r
e
p
r
o
ce
s
s
in
g
o
f
g
ath
er
e
d
d
ata
3.
RE
SU
L
T
S
A
ND
D
I
SCU
SS
I
O
N
T
h
is
s
ec
tio
n
will
d
is
cu
s
s
th
e
r
esu
lts
o
f
o
u
r
r
esear
ch
wo
r
k
in
d
etail.
Fo
r
ea
s
e
o
f
u
n
d
er
s
tan
d
in
g
,
we
will
p
r
esen
t
o
u
r
wo
r
k
d
ata
with
th
e
h
elp
o
f
s
o
m
e
g
r
a
p
h
s
an
d
tab
les
in
two
s
ec
tio
n
s
.
Her
e
we
will
p
r
o
v
id
e
a
b
r
ief
co
m
p
ar
is
o
n
with
th
e
wo
r
k
o
f
o
th
er
s
as we
ll.
3
.
1
.
E
x
perim
ent
a
l e
v
a
lua
t
io
n
A
d
ata
s
et
is
p
r
ep
ar
ed
b
y
g
ath
er
in
g
5
1
0
p
e
o
p
les'
d
ata.
T
h
e
s
tatis
tic
s
h
av
e
s
h
o
wn
th
at
2
0
9
p
eo
p
le
ar
e
ad
d
icted
b
ec
au
s
e
o
f
th
eir
f
r
i
en
d
s
an
d
9
8
p
eo
p
le
ar
e
ad
d
i
cted
to
d
r
u
g
s
f
o
r
cu
r
i
o
s
ity
.
T
ab
le
2
s
h
o
ws
th
e
co
r
r
elatio
n
b
etwe
en
th
e
f
ea
t
u
r
es.
Data
ar
e
h
ig
h
ly
co
n
n
ec
ted
b
y
a
p
o
s
itiv
e
v
alu
e
an
d
th
e
n
e
g
ativ
e
v
alu
e
m
ea
n
s
th
at
th
e
d
ata
is
n
e
g
ativ
ely
co
n
n
ec
ted
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N
a
ï
v
e
B
a
y
e
s
D
i
st
r
i
b
u
t
i
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:
G
a
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ssi
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d
i
st
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i
b
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t
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,
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)
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1
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(
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2
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M
e
a
n
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=
1
∑
(
)
V
a
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i
a
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e
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=
∑
(
−
̅
)
2
=
1
C
A
R
T
D
i
st
r
i
b
u
t
i
o
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m
e
a
s
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r
e
:
G
i
n
i
i
n
d
e
x
,
Gi
n
i(
D)
=
1
-
∑
2
=
1
M
a
x
i
m
u
m
d
e
p
t
h
=
0
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i
n
i
m
u
m
sam
p
l
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s s
p
l
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t
=
2
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d
a
B
o
o
st
N
u
mb
e
r
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f
e
s
t
i
m
a
t
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r
s =
1
0
0
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a
r
n
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n
g
r
a
t
e
=
1
.
0
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u
mb
e
r
o
f
r
a
n
d
o
m s
t
a
t
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s
=
1
0
2
R
a
n
d
o
m f
o
r
e
s
t
N
u
mb
e
r
o
f
e
s
t
i
m
a
t
o
r
s =
1
0
0
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a
x
i
m
u
m
d
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p
t
h
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u
mb
e
r
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f
r
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d
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m s
t
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=
0
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LP
A
l
p
h
a
=
0
.
0
0
0
1
N
e
t
w
o
r
k
a
r
c
h
i
t
e
c
t
u
r
e
:
2
3
-
5
-
2
-
1
N
u
mb
e
r
o
f
r
a
n
d
o
m s
t
a
t
e
s
=
9
4
G
B
M
N
u
mb
e
r
o
f
e
s
t
i
m
a
t
o
r
s =
2
Le
a
r
n
i
n
g
r
a
t
e
=
0
.
1
5
M
a
x
i
m
u
m
d
e
p
t
h
=
5
W
e
h
av
e
also
ca
lcu
lated
th
e
ac
cu
r
ac
y
with
an
u
n
p
r
o
ce
s
s
ed
d
ata
s
et.
k
-
NN
h
as
ac
h
iev
e
d
8
1
.
3
7
%
ac
cu
r
ac
y
,
SVM
h
as
ac
h
iev
ed
5
9
.
0
1
%
ac
cu
r
ac
y
,
lo
g
is
tic
r
eg
r
ess
io
n
h
as
ac
h
iev
ed
5
8
.
8
2
%
ac
cu
r
ac
y
,
n
aïv
e
B
ay
es
h
as
ac
h
iev
ed
5
7
.
8
4
%
a
cc
u
r
ac
y
,
th
e
r
an
d
o
m
f
o
r
est
h
a
s
ac
h
iev
ed
7
3
.
5
2
%
ac
cu
r
ac
y
,
C
AR
T
h
as
ac
h
iev
ed
5
7
.
8
4
%
ac
cu
r
ac
y
,
Ad
aBo
o
s
t
h
as
ac
h
iev
ed
7
1
.
5
6
%
ac
c
u
r
ac
y
,
ML
P
h
as
ac
h
iev
ed
5
8
.
8
2
%
ac
cu
r
ac
y
an
d
GB
M
h
as a
ch
iev
ed
7
3
.
5
2
% a
cc
u
r
ac
y
with
th
e
u
n
p
r
o
ce
s
s
ed
d
ata
s
et.
3
.
2
.
Co
m
pa
ra
t
iv
e
a
na
ly
s
is
o
f
r
esu
lt
T
o
ev
alu
ate
th
e
g
o
o
d
n
ess
o
f
o
u
r
p
r
o
p
o
s
ed
a
d
d
ictio
n
p
r
ed
i
ctio
n
s
y
s
tem
,
we
n
ee
d
to
co
m
p
ar
e
o
u
r
wo
r
k
with
s
o
m
e
r
ec
en
t
an
d
r
elev
an
t
r
esear
ch
wo
r
k
s
.
W
e
s
h
o
u
ld
tak
e
it
in
to
ac
co
u
n
t
th
at
th
e
p
r
esu
m
p
tio
n
ad
o
p
ted
b
y
t
h
e
r
esear
ch
e
r
s
in
co
llectin
g
s
am
p
les
an
d
r
ep
o
r
ti
n
g
r
esu
lts
o
f
th
eir
r
es
ea
r
ch
ac
tiv
ities
in
p
r
o
ce
s
s
in
g
th
o
s
e
s
am
p
les
wi
ll
h
av
e
an
in
ten
s
e
in
d
icatio
n
o
f
o
u
r
en
d
ea
v
o
r
f
o
r
co
m
p
a
r
ativ
e
p
er
f
o
r
m
an
ce
ev
alu
atio
n
.
W
e
h
av
e
s
tr
iv
ed
to
co
m
p
ar
e
o
u
r
wo
r
k
with
th
e
o
th
er
’
s
b
ased
o
n
s
o
m
e
o
f
t
h
e
p
a
r
am
eter
s
lik
e
y
y
Evaluation Warning : The document was created with Spire.PDF for Python.
I
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2
0
8
8
-
8
7
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I
n
t J E
lec
&
C
o
m
p
E
n
g
,
Vo
l.
11
,
No
.
5
,
Octo
b
e
r
2
0
2
1
:
4
4
7
1
-
4
4
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0
4478
s
am
p
le
s
iz
e,
s
ize
o
f
f
ea
tu
r
e
s
et,
alg
o
r
ith
m
,
a
n
d
ac
cu
r
ac
y
.
T
ab
le
5
s
h
o
ws
a
co
m
p
ar
ativ
e
o
v
er
v
iew
o
f
o
th
e
r
wo
r
k
s
an
d
o
u
r
w
o
r
k
.
Z
h
an
g
et
a
l
.
[
1
3
]
p
er
f
o
r
m
ed
a
p
r
ed
ictio
n
o
n
d
aily
s
m
o
k
in
g
b
eh
a
v
io
r
with
f
iv
e
f
ea
t
u
r
es
af
ter
co
llectin
g
d
ata
f
r
o
m
1
5
,
0
9
5
p
eo
p
le.
Z
h
u
et
a
l
.
[
1
5
]
w
o
r
k
ed
o
n
to
b
ac
co
d
is
ea
s
e
d
etec
tio
n
with
1
8
0
h
y
p
er
s
p
ec
tr
al
im
ag
es
with
3
2
f
ea
tu
r
es.
I
n
p
ap
er
[
1
6
]
,
p
r
ed
ictio
n
o
f
HI
V
p
r
o
g
n
o
s
is
an
d
m
o
r
tality
wit
h
s
m
o
k
in
g
-
ass
o
ciate
d
DNA
was
d
o
n
e
with
r
o
u
g
h
ly
0
.
7
8
AUC.
Pre
d
ictio
n
o
n
th
e
s
m
o
k
i
n
g
s
tatu
s
b
y
co
llectin
g
p
atie
n
ts
’
b
lo
o
d
test
s
an
d
h
ea
lt
h
ass
o
ciate
d
v
ital
r
ea
d
in
g
s
was
d
o
n
e
in
[
1
8
]
.
L
ee
et
a
l.
[
1
9
]
p
r
ed
icted
alco
h
o
l
u
s
e
d
is
o
r
d
er
b
y
c
h
ec
k
in
g
th
e
tr
ea
tm
en
t
-
s
ee
k
in
g
s
tatu
s
o
f
p
a
tien
ts
an
d
th
ey
d
id
n
o
t
m
e
n
tio
n
th
e
ac
c
u
r
ac
y
o
f
th
eir
wo
r
k
.
I
n
th
e
p
ap
er
[
2
0
]
also
,
p
r
ed
ict
io
n
o
n
th
e
r
is
k
o
f
alco
h
o
l
u
s
e
d
is
o
r
d
er
with
d
if
f
er
en
t
t
y
p
es
o
f
d
ata
w
er
e
d
o
n
e
y
et
th
ey
d
id
n
o
t
m
en
tio
n
th
e
class
if
ier
an
d
ac
cu
r
ac
y
.
Pre
d
ictio
n
o
n
alco
h
o
l
ab
u
s
e
with
ANN
was
s
ee
n
in
th
e
wo
r
k
p
e
r
f
o
r
m
ed
b
y
Ku
m
ar
i
et
a
l
.
[
2
1
]
,
an
d
it
s
h
o
wed
an
ac
cu
r
ac
y
o
f
9
8
.
7
%.
C
o
n
ce
r
n
in
g
th
e
o
v
er
all
p
ictu
r
e
d
ep
icted
in
th
is
s
ec
tio
n
,
o
u
r
attain
ed
ac
cu
r
ac
y
o
f
m
o
r
e
th
an
9
7
.
9
1
%.
h
as
tu
r
n
ed
o
u
t
to
b
e
g
o
o
d
as
well
as
p
r
o
m
is
in
g
en
o
u
g
h
.
T
h
e
r
ea
s
o
n
b
e
h
in
d
o
u
r
p
r
o
p
o
s
ed
s
o
lu
tio
n
to
ac
h
iev
e
a
v
e
r
y
h
ig
h
ac
cu
r
ac
y
is
t
h
at
th
e
f
ea
tu
r
es
d
ep
l
o
y
ed
ar
e
co
m
p
u
tatio
n
ally
s
im
p
le
to
ca
lcu
lat
e
an
d
h
av
e
v
er
y
h
ig
h
d
is
cr
im
in
ato
r
y
in
f
o
r
m
atio
n
to
p
r
e
d
ict
th
e
r
is
k
o
f
b
ec
o
m
in
g
ad
d
icted
to
d
r
u
g
s
.
As
we
h
a
v
e
m
en
tio
n
ed
b
ef
o
r
e,
m
o
s
t
o
f
th
e
o
th
e
r
wo
r
k
s
ar
e
n
o
t
v
er
y
clo
s
e
to
o
u
r
s
.
So
it
wo
u
ld
n
o
t
b
e
wis
e
to
ex
p
lic
it
ly
co
m
p
a
r
e
th
e
wo
r
th
in
ess
o
f
o
u
r
ap
p
r
o
ac
h
with
o
th
er
wo
r
k
s
.
Fig
u
r
e
5
.
C
o
m
p
a
r
is
o
n
o
f
ac
cu
r
ac
y
b
etwe
en
b
e
f
o
r
e
a
n
d
af
te
r
PC
A
T
ab
le
5
.
R
esu
lts
o
f
th
e
co
m
p
ar
is
o
n
o
f
o
u
r
wo
r
k
an
d
o
th
e
r
wo
r
k
s
M
e
t
h
o
d
/
W
o
r
k
D
o
n
e
A
d
d
i
c
t
i
o
n
D
e
a
l
t
w
i
t
h
P
r
o
b
l
e
m D
o
ma
i
n
S
a
mp
l
e
S
i
z
e
S
i
z
e
o
f
F
e
a
t
u
r
e
S
e
t
A
l
g
o
r
i
t
h
m
A
c
c
u
r
a
c
y
Th
i
s
w
o
r
k
D
r
u
g
s
a
n
d
a
l
c
o
h
o
l
(
r
i
sk
)
P
r
e
d
i
c
t
i
o
n
5
1
0
23
Lo
g
i
s
t
i
c
r
e
g
r
e
ss
i
o
n
9
7
.
9
1
%
Zh
a
n
g
e
t
a
l
.
[
1
3
]
S
mo
k
i
n
g
b
e
h
a
v
i
o
r
P
r
e
d
i
c
t
i
o
n
1
5
0
9
5
5
X
G
b
o
o
st
8
4
.
1
1
%
Zh
u
e
t
a
l
.
[
1
5
]
To
b
a
c
c
o
d
i
se
a
ses
D
e
t
e
c
t
i
o
n
1
8
0
32
ELM
9
8
.
3
%
Zh
a
n
g
e
t
a
l
.
[
1
6
]
H
I
V
p
r
o
g
n
o
si
s wi
t
h
smo
k
i
n
g
-
a
sso
c
i
a
t
e
d
D
N
A
P
r
e
d
i
c
t
i
o
n
1
1
3
7
6
9
8
G
LM
N
ET
0
.
7
8
AUC
F
r
a
n
k
e
t
a
l
.
[
1
8
]
S
mo
k
i
n
g
st
a
t
u
s
P
r
e
d
i
c
t
i
o
n
5
3
4
3
Lo
g
i
s
t
i
c
r
e
g
r
e
ss
i
o
n
8
3
.
4
4
%
Le
e
e
t
a
l
.
[
1
9
]
A
l
c
o
h
o
l
u
s
e
d
i
s
o
r
d
e
r
(
t
r
e
a
t
me
n
t
s
e
e
k
i
n
g
)
P
r
e
d
i
c
t
i
o
n
7
7
8
10
Lo
g
i
s
t
i
c
r
e
g
r
e
ss
i
o
n
NM
1
K
i
n
r
e
i
c
h
e
t
a
l
.
[
2
0
]
A
l
c
o
h
o
l
u
s
e
d
i
s
o
r
d
e
r
(
r
i
s
k
)
P
r
e
d
i
c
t
i
o
n
6
5
6
3
NM
1
NM
1
K
u
mari
e
t
a
l
.
[
2
1
]
A
l
c
o
h
o
l
a
b
u
s
e
P
r
e
d
i
c
t
i
o
n
1
8
8
5
12
ANN
9
8
.
7
%
1
NM
:
N
o
t
M
e
n
t
i
o
n
e
d
.
4.
CO
NCLU
SI
O
N
I
n
th
is
p
ap
er
,
we
h
av
e
p
er
f
o
r
m
ed
an
in
-
d
e
p
th
ex
p
lo
r
ato
r
y
wo
r
k
f
o
r
p
r
ed
ictin
g
th
e
r
is
k
o
f
b
ec
o
m
in
g
ad
d
icted
to
d
r
u
g
s
an
d
alco
h
o
l
u
s
in
g
d
if
f
er
e
n
t
m
ac
h
in
e
lear
n
in
g
tech
n
iq
u
es.
First,
we
h
av
e
f
o
r
m
e
d
th
e
b
asis
,
i.e
.
f
ea
tu
r
e
s
et
f
o
r
th
is
p
r
ed
ic
tiv
e
wo
r
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as
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s
a
p
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tial
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ata
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-
a
d
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icted
p
eo
p
le
as r
eq
u
ir
e
d
f
o
r
B
an
g
lad
esh
.
Evaluation Warning : The document was created with Spire.PDF for Python.
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RE
F
E
R
E
NC
E
S
[1
]
Co
n
tr
o
l
o
f
Dru
g
Ab
u
se
i
s
a
M
u
st
,
[On
li
n
e
].
Av
a
i
b
le:
h
tt
p
s:/
/www
.
th
e
d
a
il
y
sta
r.
n
e
t/
h
e
a
lt
h
/h
e
a
lt
h
-
a
lert/
c
o
n
tr
o
l
-
d
r
u
g
-
a
b
u
se
-
m
u
st
-
1
5
1
5
8
7
4
.
[2
]
M
.
N.
S
h
a
z
z
a
d
,
S
.
J
Ab
d
a
l,
M
.
S
.
M
.
M
a
ju
m
d
e
r,
J.
Ul
Ala
m
S
o
h
e
l,
S
.
M
.
M
.
Ali
,
a
n
d
S
.
Ah
m
e
d
,
“
Dru
g
Ad
d
ictio
n
in
Ba
n
g
la
d
e
sh
a
n
d
it
s
E
ffe
c
t
,”
in
M
e
d
icin
e
T
o
d
a
y
,
v
o
l.
2
5
,
n
o
.
2
,
p
p
.
8
4
-
8
9
,
2
0
1
4
,
d
o
i:
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0
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3
3
2
9
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d
to
d
a
y
.
v
2
5
i
2
.
1
7
9
2
7
.
[3
]
Re
stricte
d
,
sh
e
k
il
led
p
a
re
n
ts,
[
On
li
n
e
].
A
v
a
ib
le:
h
t
tp
s://
ww
w.t
h
e
d
a
il
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sta
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n
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tri
c
ted
-
sh
e
-
k
il
led
-
p
a
re
n
ts
p
a
re
n
ts.
[4
]
4
3
%
o
f
t
h
e
u
n
e
m
p
l
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y
e
d
p
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n
a
d
d
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d
t
o
d
ru
g
s,
[O
n
li
n
e
].
Av
a
i
b
le:
h
tt
p
s:
//
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w.d
h
a
k
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tri
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-
of
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u
lati
o
n
-
a
d
d
icte
d
-
to
-
d
r
u
g
s.
[5
]
J.
A.
Cru
z
a
n
d
D.
S
.
Wi
s
h
a
rt,
“
Ap
p
li
c
a
ti
o
n
s
o
f
M
a
c
h
i
n
e
Lea
rn
in
g
in
Ca
n
c
e
r
P
re
d
ictio
n
a
n
d
P
r
o
g
n
o
sis,
”
in
Ca
n
c
e
r
In
fo
rm
a
t
ics
,
v
o
l.
p
p
.
5
9
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7
7
,
2
0
0
6
,
d
o
i
:
1
0
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1
1
7
7
/
1
1
7
6
9
3
5
1
0
6
0
0
2
0
0
0
3
0
.
[6
]
C.
Ca
tal
a
n
d
B.
Diri,
“
A
s
y
ste
m
a
ti
c
re
v
iew
o
f
so
ftwa
re
fa
u
lt
p
re
d
ictio
n
stu
d
ies
,”
Exp
e
rt
S
y
ste
ms
wit
h
A
p
p
li
c
a
ti
o
n
s
,
v
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l.
3
6
,
n
o
.
4
,
p
p
.
7
3
4
6
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3
5
4
,
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0
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9
,
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0
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6
/j
.
e
sw
a
.
2
0
0
8
.
1
0
.
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2
7
.
[7
]
V.
B.
Ku
m
a
r,
S
.
S
.
Ku
m
a
r
a
n
d
V.
S
a
b
o
o
,
"
De
rm
a
to
lo
g
ica
l
d
ise
a
se
d
e
tec
ti
o
n
u
si
n
g
ima
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e
p
ro
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e
ss
in
g
a
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d
m
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e
lea
rn
in
g
,
"
2
0
1
6
T
h
ir
d
I
n
ter
n
a
ti
o
n
a
l
Co
n
fer
e
n
c
e
o
n
Arti
fi
c
i
a
l
In
te
ll
ig
e
n
c
e
a
n
d
Pa
t
ter
n
Rec
o
g
n
it
io
n
(AI
PR
)
,
Lo
d
z
,
P
o
lan
d
,
2
0
1
6
,
p
p
.
1
-
6
,
d
o
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1
0
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1
1
0
9
/
ICAIPR
.
2
0
1
6
.
7
5
8
5
2
1
7
.
[8
]
E.
W.
S
tey
e
rb
e
r
g
,
T.
V.
D.
P
l
o
e
g
,
a
n
d
B.
V.
Ca
lster,
“
Risk
p
r
e
d
ictio
n
wit
h
m
a
c
h
in
e
lea
rn
in
g
a
n
d
re
g
re
ss
io
n
m
e
th
o
d
s,”
i
n
Bi
o
me
trica
l
J
o
u
rn
a
l
,
v
o
l.
5
6
,
n
o
.
4
,
p
p
.
6
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0
6
,
2
0
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4
,
d
o
i:
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0
.
1
0
0
2
/
b
imj.
2
0
1
3
0
0
2
9
7
.
[9
]
D.
Da
h
iwa
d
e
,
G
.
P
a
tl
e
a
n
d
E.
M
e
sh
ra
m
,
"
De
sig
n
i
n
g
Dise
a
se
P
re
d
icti
o
n
M
o
d
e
l
Us
in
g
M
a
c
h
in
e
Lea
rn
in
g
Ap
p
ro
a
c
h
,
"
2
0
1
9
3
rd
I
n
ter
n
a
ti
o
n
a
l
Co
n
fer
e
n
c
e
o
n
Co
m
p
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t
in
g
M
e
th
o
d
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g
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a
n
d
Co
mm
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n
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t
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n
(ICC
M
C)
,
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,
I
n
d
ia,
2
0
1
9
,
p
p
.
1
2
1
1
-
1
2
1
5
,
d
o
i:
1
0
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1
1
0
9
/ICCM
C.
2
0
1
9
.
8
8
1
9
7
8
2
.
[1
0
]
O.
He
g
a
z
y
,
O.
S
.
S
o
li
m
a
n
,
a
n
d
M
.
A
.
S
a
lam
,
“
A
M
a
c
h
in
e
Lea
rn
in
g
M
o
d
e
l
fo
r
S
to
c
k
M
a
r
k
e
t
P
re
d
icti
o
n
,
”
In
ter
n
a
t
io
n
a
l
J
o
u
rn
a
l
o
f
C
o
mp
u
ter
S
c
ien
c
e
a
n
d
T
e
lec
o
mm
u
n
ica
ti
o
n
s
,
v
o
l
.
4
,
n
o
.
1
2
,
p
p
.
1
7
-
2
3
,
2
0
1
3
.
[1
1
]
L.
M
.
B
.
Al
o
n
z
o
,
F
.
B.
Ch
i
o
so
n
,
H.
S
.
Co
,
N.
T
.
B
u
g
tai
a
n
d
R
.
G
.
Ba
ld
o
v
i
n
o
,
"
A
M
a
c
h
i
n
e
Lea
rn
in
g
A
p
p
r
o
a
c
h
f
o
r
Co
c
o
n
u
t
S
u
g
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r
Qu
a
li
ty
As
se
ss
m
e
n
t
a
n
d
P
re
d
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o
n
,
"
2
0
1
8
IEE
E
1
0
th
In
ter
n
a
ti
o
n
a
l
Co
n
fer
e
n
c
e
o
n
Hu
ma
n
o
i
d
,
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n
o
tec
h
n
o
lo
g
y
,
In
f
o
rm
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ti
o
n
T
e
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h
n
o
l
o
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y
,
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o
mm
u
n
ica
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o
n
a
n
d
C
o
n
tro
l,
E
n
v
iro
n
me
n
t
a
n
d
M
a
n
a
g
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me
n
t
(HNICEM
)
,
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g
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io
Cit
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,
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ICEM
.
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0
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8
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6
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3
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5
.
.
[1
2
]
A.
H.
Ha
g
h
iab
i,
A.
H.
Na
sro
la
h
i,
a
n
d
A.
P
a
rsa
ie,
“
Wate
r
q
u
a
li
t
y
p
re
d
ictio
n
u
sin
g
m
a
c
h
in
e
lea
r
n
in
g
m
e
th
o
d
s
,”
W
a
ter
Qu
a
li
ty R
e
se
a
rc
h
J
o
u
rn
a
l,
v
o
l.
5
3
,
n
o
.
1
,
p
p
.
3
-
1
3
,
2
0
1
8
,
d
o
i
:
1
0
.
2
1
6
6
/wq
rj
.
2
0
1
8
.
0
2
5
.
[1
3
]
Y.
Zh
a
n
g
,
J.
Li
u
,
Z.
Z
h
a
n
g
a
n
d
J.
Hu
a
n
g
,
"
P
re
d
icti
o
n
o
f
Da
il
y
S
m
o
k
i
n
g
Be
h
a
v
io
r
Ba
se
d
o
n
De
c
isio
n
Tree
M
a
c
h
in
e
Lea
rn
in
g
Alg
o
rit
h
m
,
"
2
0
1
9
IE
EE
9
th
In
ter
n
a
ti
o
n
a
l
Co
n
fer
e
n
c
e
o
n
El
e
c
tro
n
ics
In
f
o
rm
a
ti
o
n
a
n
d
Eme
rg
e
n
c
y
Co
mm
u
n
ica
ti
o
n
(ICEI
EC)
,
Be
ij
i
n
g
,
Ch
in
a
,
2
0
1
9
,
p
p
.
3
3
0
-
3
3
3
,
d
o
i:
1
0
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1
0
9
/IC
EIE
C.
2
0
1
9
.
8
7
8
4
6
9
8
.
[1
4
]
A.
M
.
Ala
a
,
T.
Bo
lt
o
n
,
E
.
D.
An
g
e
lan
t
o
n
i
o
,
J.
H.
F
.
R
u
d
d
,
a
n
d
M
.
V.
D.
S
c
h
a
a
r,
“
Ca
rd
io
v
a
sc
u
lar
d
ise
a
se
risk
p
re
d
ictio
n
u
sin
g
a
u
to
m
a
ted
m
a
c
h
in
e
lea
rn
i
n
g
:
A
p
ro
s
p
e
c
ti
v
e
st
u
d
y
o
f
4
2
3
,
6
0
4
U
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Bio
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9
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J.
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4
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.
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Je
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re
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[2
6
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Dr
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,
[
On
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].
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