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hl
i
g
h
t
i
n
g
t
h
e
ur
ge
n
c
y
o
f
pr
e
ve
n
t
i
ve
a
n
d
t
r
e
a
t
m
e
n
t
m
e
a
s
ur
e
s
[
15]
,
[
16]
.
I
n
c
o
un
t
r
i
e
s
s
uc
h
a
s
Gr
e
e
nl
a
n
d,
I
t
a
l
y
,
a
n
d
S
l
o
v
e
ni
a
,
a
de
c
r
e
a
s
e
i
n
l
u
n
g
c
a
n
c
e
r
i
n
c
i
d
e
n
c
e
h
a
s
b
e
e
n
o
b
s
e
r
v
e
d.
O
n
t
h
e
ot
h
e
r
h
a
n
d,
i
n
S
l
o
v
a
k
i
a
,
P
o
l
a
n
d,
a
n
d
t
h
e
Ne
t
h
e
r
l
a
n
d
s
,
a
n
i
n
c
r
e
a
s
e
i
n
t
h
e
i
nc
i
d
e
n
c
e
o
f
t
hi
s
d
i
s
e
a
s
e
h
a
s
b
e
e
n
r
e
c
o
r
de
d
[
17]
.
I
n
r
e
c
e
n
t
de
c
a
de
s
,
t
h
e
f
i
e
l
d
o
f
m
a
c
hi
ne
l
e
a
r
ni
n
g
(
ML
)
h
a
s
e
x
pe
r
i
e
n
c
e
d
gr
e
a
t
a
dv
a
n
c
e
s
i
n
t
h
e
de
v
e
l
o
p
m
e
n
t
o
f
s
o
phi
s
t
i
c
a
t
e
d
a
l
go
r
i
t
hm
s
a
n
d
da
t
a
pr
e
p
r
o
c
e
s
s
i
ng
[
18]
.
E
m
p
h
a
s
i
z
i
ng
t
h
e
i
m
po
r
t
a
n
c
e
o
f
r
e
s
e
a
r
c
h
e
r
s
t
a
k
i
n
g
a
d
v
a
n
t
a
ge
o
f
M
L
’
s
pr
e
d
i
c
t
i
ve
c
a
pa
bi
li
t
i
e
s
to
a
ddr
e
s
s
t
h
e
d
i
a
g
n
o
s
i
s
a
n
d
t
r
e
a
tm
e
n
t
o
f
d
i
s
e
a
s
e
s
,
us
i
n
g
m
a
t
h
e
m
a
t
i
c
a
l
m
o
de
l
s
t
o
i
de
n
t
i
f
y
pa
tt
e
r
n
s
i
n
t
h
e
da
t
a
[
19
]
-
[
21
]
.
T
h
e
r
e
a
r
e
4
t
y
pe
s
o
f
M
L
t
e
c
h
ni
que
s
,
whi
c
h
a
r
e
s
upe
r
vi
s
e
d
l
e
a
r
ni
ng,
un
s
up
e
r
vi
s
e
d
l
e
a
r
ni
ng,
s
e
mi
-
s
upe
r
vi
s
e
d
l
e
a
r
nin
g,
a
n
d
r
e
i
nf
o
r
c
e
m
e
n
t
l
e
a
r
ni
ng
[
22]
.
I
n
ge
n
e
r
a
l
,
M
L
m
o
d
e
l
s
pr
o
vi
de
s
y
s
t
e
m
s
w
i
t
h
t
he
a
bil
i
t
y
t
o
l
e
a
r
n
a
n
d
im
pr
o
v
e
t
h
r
o
ugh
tr
a
i
ni
ng,
w
i
t
h
o
ut
t
h
e
n
e
e
d
to
b
e
e
x
p
l
i
c
i
t
ly
pr
o
g
r
a
m
m
e
d
[
23]
,
[
24
]
.
S
i
m
il
a
r
ly
,
M
L
a
l
go
r
i
t
hms
s
e
e
k
to
a
uto
m
a
t
e
t
h
e
de
v
e
l
o
p
m
e
n
t
o
f
a
n
a
ly
t
i
c
a
l
m
o
de
l
s
to
pe
r
f
o
r
m
t
a
s
ks
r
e
l
a
t
e
d
to
t
h
e
de
t
e
c
t
i
o
n
o
r
p
r
e
di
c
t
i
o
n
o
f
o
bj
e
c
t
s
,
a
n
d
d
i
s
e
a
s
e
s
,
a
m
o
n
g
ot
h
e
r
s
[
25]
,
[
26]
.
H
o
we
v
e
r
,
to
a
c
hi
e
v
e
gr
e
a
t
e
r
a
c
c
ur
a
c
y
i
n
pr
e
d
i
c
t
i
o
ns
,
a
l
a
r
ge
a
m
o
un
t
o
f
da
t
a
r
e
l
a
t
e
d
to
t
h
e
s
ubj
e
c
t
o
f
s
t
ud
y
i
s
r
e
qu
i
r
e
d
[
27]
.
T
hi
s
s
t
ud
y
a
i
m
s
to
i
de
n
t
i
f
y
t
h
e
M
L
a
l
go
r
i
t
hm
w
i
t
h
t
h
e
b
e
s
t
pe
r
f
o
r
m
a
n
c
e
i
n
pr
e
d
i
c
t
i
n
g
l
u
n
g
c
a
n
c
e
r
.
T
h
e
a
l
go
r
i
t
hm
s
t
h
a
t
we
r
e
c
o
n
tr
a
s
t
e
d
we
r
e
l
o
g
i
s
t
i
c
r
e
gr
e
s
s
i
o
n
(
L
R
)
,
de
c
i
s
i
o
n
t
r
e
e
(
DT
)
,
k
-
n
e
a
r
e
s
t
n
e
i
g
hb
o
r
s
(
K
NN
)
,
g
a
us
s
i
a
n
Na
i
ve
B
a
y
e
s
(
GN
B
)
,
m
u
l
t
i
n
o
mi
a
l
Na
i
ve
B
a
y
e
s
(
M
N
B
)
,
s
uppor
t
v
e
c
to
r
c
l
a
s
s
if
i
e
r
(
S
VC
)
,
r
a
n
do
m
f
o
r
e
s
t
(
R
F
)
,
e
x
t
r
e
m
e
gr
a
d
i
e
n
t
b
oo
s
t
i
n
g
(
XG
B
oo
s
t
)
,
m
u
l
t
il
a
y
e
r
pe
r
c
e
pt
r
o
n
(
M
L
P
)
a
n
d
gr
a
di
e
n
t
b
o
o
s
t
i
n
g
(
GB
)
.
I
n
a
ddi
t
i
o
n
,
t
h
e
a
r
t
i
c
l
e
i
s
s
t
r
uc
t
ur
e
d
i
n
6
s
e
c
t
i
o
ns
.
I
n
s
e
c
t
i
o
n
1
i
n
t
r
o
duc
t
i
o
n
,
t
h
e
pr
o
bl
e
m
o
f
t
h
e
c
a
s
e
s
t
ud
y
i
s
de
t
a
i
l
e
d.
S
e
c
t
i
o
n
2
b
i
b
li
o
gr
a
p
hi
c
r
e
view
de
s
c
r
i
b
e
s
t
h
e
s
t
udi
e
s
r
e
l
a
t
e
d
to
t
hi
s
r
e
s
e
a
r
c
h
.
S
e
c
t
i
o
n
3
m
e
t
h
o
do
l
o
g
y
i
s
de
v
o
t
e
d
to
t
h
e
r
e
s
e
a
r
c
h
m
e
t
h
o
do
l
o
g
y
,
whi
c
h
i
s
d
i
vi
de
d
i
n
t
o
t
w
o
pa
r
t
s
:
3.
1
.
d
e
s
c
r
i
pt
i
o
n
o
f
t
h
e
M
L
m
o
de
l
s
a
n
d
3.
2.
c
a
s
e
s
t
udy
.
S
e
c
t
i
o
n
4
r
e
s
u
l
t
s
,
pr
e
s
e
n
t
s
t
h
e
m
o
de
l
t
r
a
i
ni
ng
r
e
s
u
l
t
s
.
T
h
e
l
a
s
t
s
e
c
t
i
o
n
s
a
r
e
5
d
i
s
c
us
s
i
o
ns
a
n
d
6
c
o
n
c
l
us
i
o
ns
,
wh
e
r
e
t
h
e
o
b
t
a
i
n
e
d
r
e
s
u
l
t
s
a
r
e
di
s
c
u
s
s
e
d
a
n
d
c
o
n
c
l
ude
d.
2.
B
I
B
L
I
OG
RA
P
HI
C
RE
VI
E
W
I
n
t
hi
s
s
e
c
t
i
o
n
,
r
e
l
a
t
e
d
w
o
r
k
r
e
l
a
t
e
d
to
t
h
e
c
a
s
e
s
tud
y
i
s
de
s
c
r
i
be
d.
R
a
d
hi
ka
e
t
al.
[
28
]
,
e
v
a
l
ua
t
e
t
h
e
pe
r
f
o
r
m
a
n
c
e
o
f
NB
,
s
uppor
t
v
e
c
t
or
m
a
c
hi
ne
(
S
VM
)
,
D
T
,
a
n
d
L
R
a
l
go
r
i
t
hm
s
f
o
r
e
a
r
l
y
d
i
a
g
n
o
s
is
o
f
l
u
n
g
c
a
nc
e
r
.
T
w
o
da
t
a
s
e
t
s
we
r
e
us
e
d
f
o
r
t
r
a
i
ni
ng
t
h
e
a
l
go
r
i
t
hm
s
.
T
h
e
r
e
s
u
l
t
s
o
f
t
h
e
s
t
ud
y
s
h
o
we
d
t
h
a
t
,
w
i
t
h
t
h
e
f
i
r
s
t
da
t
a
s
e
t,
L
R
o
b
t
a
i
ne
d
t
h
e
hi
g
h
e
s
t
a
c
c
ur
a
c
y
w
i
t
h
a
v
a
l
ue
o
f
0.
969.
W
i
t
h
t
h
e
s
e
c
o
n
d
da
t
a
s
e
t,
S
VM
a
c
hi
e
ve
d
t
h
e
hi
g
he
s
t
a
c
c
ur
a
c
y
w
i
t
h
a
v
a
l
ue
o
f
0.
992,
f
o
l
l
o
we
d
by
DT
w
i
t
h
0.
9
a
n
d
NB
w
i
t
h
0.
8787.
On
t
h
e
ot
h
e
r
h
a
n
d,
Dr
i
t
s
a
s
a
n
d
T
r
i
gka
[
29]
c
o
m
p
a
r
e
d
t
h
e
pe
r
f
o
r
m
a
n
c
e
o
f
N
B
,
B
a
y
e
s
i
a
n
n
e
t
wor
k
(
B
a
y
e
s
Ne
t
)
,
s
to
c
h
a
s
t
i
c
gr
a
d
i
e
n
t
de
s
c
e
n
t
(
S
GD
)
,
S
VM
,
L
R
,
a
r
t
i
f
i
c
i
a
l
n
e
ur
a
l
n
e
t
wo
r
k
(
A
NN
)
,
K
NN
,
J
48
,
l
o
g
i
s
t
i
c
mo
de
l
t
r
e
e
(
L
M
T
)
,
R
F
,
r
a
n
do
m
t
r
e
e
(
R
T
)
,
r
e
duc
e
d
e
r
r
o
r
pr
uni
ng
t
r
e
e
(
R
e
pT
r
e
e
)
,
r
ot
a
t
i
o
n
f
o
r
e
s
t
(
R
ot
F
)
a
n
d
a
da
pt
i
v
e
b
o
o
s
t
i
n
g
(
A
da
B
o
o
s
t
M
1)
a
l
go
r
i
t
hm
s
.
I
n
a
dd
i
t
i
o
n
,
t
h
e
i
r
m
e
t
h
o
do
l
o
gy
e
m
p
l
o
y
e
d
t
h
e
S
M
OT
E
t
e
c
h
ni
qu
e
a
n
d
t
h
e
c
r
o
s
s
-
v
a
l
i
d
a
t
i
o
n
m
e
t
h
o
d
f
o
r
da
t
a
p
r
o
c
e
s
s
i
ng.
T
h
e
s
t
udy
po
s
i
t
i
o
n
e
d
R
ot
F
a
s
t
h
e
m
o
s
t
e
f
f
i
c
i
e
n
t
a
l
go
r
i
t
hm
,
a
c
hi
e
vi
ng
a
n
a
c
c
ur
a
c
y
a
n
d
pr
e
c
i
s
i
o
n
o
f
0.
971,
a
n
d
a
n
a
r
e
a
un
de
r
o
f
c
ur
v
e
(
A
UC
)
v
a
l
ue
o
f
0.
993.
On
th
e
ot
h
e
r
h
a
n
d,
L
R
a
c
hi
e
v
e
d
a
n
a
c
c
ur
a
c
y
a
n
d
pr
e
c
i
s
i
o
n
of
0.
963,
whil
e
K
NN
a
n
d
R
F
o
b
t
a
i
n
e
d
a
n
a
c
c
ur
a
c
y
a
nd
pr
e
c
i
s
i
o
n
o
f
0.
952.
S
i
m
il
a
r
ly
,
S
i
ng
h
a
n
d
Gupt
a
[
30]
a
n
e
f
f
i
c
i
e
n
t
a
ppr
o
a
c
h
f
o
r
l
u
n
g
c
a
nc
e
r
de
t
e
c
t
i
o
n
a
n
d
c
l
a
s
s
if
i
c
a
t
i
o
n
b
a
s
e
d
o
n
i
m
a
ge
s
r
e
l
a
t
e
d
to
t
hi
s
d
i
s
e
a
s
e
i
s
pr
e
s
e
n
t
e
d,
a
n
d
K
N
N,
S
VM
,
DT
,
M
NB
,
S
GD
,
R
F
,
a
n
d
M
L
P
a
l
go
r
i
t
hm
s
we
r
e
a
n
a
ly
z
e
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NB
,
S
VC
,
R
F
,
XG
B
oo
s
t,
M
L
P
,
a
n
d
GB
)
a
r
e
de
s
c
r
i
be
d.
I
n
t
h
e
s
e
c
o
n
d
pa
r
t
,
t
h
e
c
a
s
e
s
t
ud
y
i
s
de
s
c
r
i
b
e
d
.
3.
1.
De
s
c
r
ip
t
ion
of
t
h
e
M
L
s
m
od
e
l
s
3.
1
.
1.
L
ogis
t
ic
r
e
g
r
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s
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ion
T
h
e
L
R
m
o
de
l
i
s
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ne
o
f
t
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m
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t
w
i
de
ly
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s
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a
lgo
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i
t
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m
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d
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c
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ne
b
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a
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s
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o
f
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s
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f
u
l
ne
s
s
i
n
m
u
l
t
i
va
r
i
a
bl
e
m
o
de
li
ng
[
43]
,
[
44]
.
On
e
o
f
t
h
e
m
o
s
t
o
b
vi
o
u
s
a
dv
a
n
t
a
ge
s
i
s
i
t
s
a
bil
i
t
y
to
c
o
n
v
e
r
t
c
o
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f
f
i
c
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e
n
t
s
i
n
t
o
pr
o
p
o
r
t
i
o
n
a
l
o
dds
[
45]
.
I
n
a
dd
i
t
i
o
n
,
L
R
pr
o
v
i
de
s
us
w
i
t
h
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c
hni
que
t
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t
gua
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t
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l
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s
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d
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na
r
y
f
o
r
m
,
w
i
t
h
va
l
u
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s
o
f
0
a
n
d
1
[
46]
.
T
h
e
m
a
t
he
m
a
t
i
c
a
l
e
qua
t
i
o
n
o
f
t
h
e
L
R
m
o
de
l
i
s
e
x
pr
e
s
s
e
d
in
(
1)
.
(
)
=
1
1
+
−
(
0
+
1
1
+
2
2
+
⋯
+
)
,
(
1)
3.
1
.
2
.
De
c
is
ion
t
r
e
e
T
h
e
DT
m
o
de
l
i
s
a
s
im
p
l
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too
l
t
h
a
t
c
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s
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r
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da
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o
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w
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t
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o
f
c
l
a
s
s
if
i
c
a
t
i
o
n
r
u
l
e
s
[
47]
.
DT
i
s
b
a
s
e
d
o
n
t
h
e
d
i
vi
de
-
a
n
d
-
c
o
n
que
r
s
t
r
a
t
e
gy
a
n
d
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s
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o
m
po
s
e
d
o
f
l
e
a
f
n
o
de
s
t
h
a
t
a
r
e
c
onn
e
c
t
e
d,
f
o
r
m
i
ng
a
hi
e
r
a
r
c
hi
c
a
l
s
t
r
uc
t
ur
e
[
48
]
.
S
i
n
c
e
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t
i
s
a
c
l
a
s
s
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f
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c
a
t
i
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o
de
l
,
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t
c
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n
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e
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pp
l
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e
d
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v
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r
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o
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e
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d
s
,
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nc
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t
a
m
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n
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ng
a
n
d
c
l
a
s
s
i
f
i
c
a
t
i
o
n
[
49]
,
[
5
0]
.
DT
c
a
n
b
e
r
e
pr
e
s
e
n
t
e
d
i
n
(
2)
.
W
h
e
r
e
E
de
n
ot
e
s
t
h
e
e
n
t
r
o
py
,
s
i
s
t
h
e
s
a
m
p
l
e
,
Py
i
s
t
he
pr
o
b
a
bil
i
t
y
o
f
o
c
c
ur
r
e
n
c
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o
f
t
h
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S
I
e
v
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n
t
a
n
d
Pn
i
s
t
h
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pr
o
b
a
bil
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t
y
o
f
o
c
c
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c
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o
f
t
h
e
NO
e
v
e
n
t
.
(
)
=
∑
(
)
−
=
0
∗
log
2
,
(
2
)
3.
1
.
3
.
K
-
n
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ar
e
s
t
n
e
igh
b
or
T
h
e
K
NN
m
o
de
l
i
s
w
i
d
e
l
y
r
e
c
o
gni
z
e
d
f
o
r
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t
s
e
f
f
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c
t
i
ve
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s
s
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n
da
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s
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pa
r
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t
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o
n
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n
d
c
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b
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us
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f
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l
w
h
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s
t
udy
da
t
a
pr
e
s
e
n
t
a
m
bi
gu
i
t
i
e
s
[
51]
.
F
ur
t
h
e
r
m
o
r
e
,
K
NN
gr
o
ups
da
t
a
i
n
t
o
c
o
h
e
r
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n
t
s
ubs
e
t
s
a
n
d
l
a
b
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l
s
n
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w
da
t
a
a
c
c
o
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di
n
g
t
o
t
h
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r
s
i
mi
l
a
r
i
t
y
t
o
t
h
e
t
r
a
i
ni
ng
r
e
s
u
l
t
s
[
52]
.
T
h
e
m
o
de
l
i
s
a
n
o
n
pa
r
a
m
e
t
r
i
c
a
l
go
r
i
t
hm
,
i
.
e
.
,
t
h
e
r
e
i
s
n
o
f
i
xe
d
n
u
m
be
r
o
f
pa
r
a
m
e
t
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r
s
i
n
de
pe
n
de
n
t
o
f
t
h
e
da
t
a
s
i
z
e
[
53]
.
T
h
e
E
uc
l
i
de
a
n
e
qua
t
i
o
n
i
n
t
hi
s
m
o
de
l
i
s
s
h
o
w
i
n
(
3)
.
(
,
)
=
√
∑
(
−
)
2
=
1
,
(
3
)
3.
1
.
4
.
Gau
s
s
ian
Naive
B
aye
s
T
h
e
GN
B
m
o
de
l
i
s
a
pr
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b
a
bi
li
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c
c
l
a
s
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if
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c
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go
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t
h
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t
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s
m
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l
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p
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l
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c
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t
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s
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s
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l
a
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d
m
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d
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c
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d
i
a
g
n
o
s
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s
[
54]
.
GN
B
us
e
s
B
a
y
e
s
’
r
u
l
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s
a
n
d
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s
b
a
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a
s
s
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p
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de
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f
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pr
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by
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l
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t
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m
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us
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pe
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s
if
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c
a
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ns
e
f
f
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c
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n
t
l
y
[
5
5]
.
I
n
(
4)
de
s
c
r
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be
s
t
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de
n
s
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t
y
f
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wh
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X|Y
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[
56]
.
Evaluation Warning : The document was created with Spire.PDF for Python.
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52
P
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f
or
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of
10
mac
hine
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mod
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in
lung
c
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(
J
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(
|
=
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=
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2
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3.
1
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5
.
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in
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aye
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M
NB
m
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[
57]
.
M
NB
c
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s
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f
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c
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[
58]
.
T
h
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r
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f
o
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,
t
h
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m
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de
l
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s
s
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bl
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f
o
r
c
a
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o
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do
c
um
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t
s
[
59]
.
T
h
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m
a
t
h
e
m
a
t
i
c
a
l
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qua
t
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f
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L
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m
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pr
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s
s
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d
in
(
5
).
W
h
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r
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|
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|
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po
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to
t
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|
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(
5)
3.
1.
6.
S
u
p
p
or
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c
t
or
c
l
a
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s
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f
ie
r
Th
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VC
m
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a
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r
r
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g
r
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s
s
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n
[
60]
.
S
VC
i
s
a
ge
n
e
r
i
c
c
l
a
s
s
if
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t
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t
[
61]
.
T
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f
t
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3.
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Th
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M
L
[
62]
,
[
63]
.
R
F
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m
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[
64]
.
L
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w
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[
65]
.
3.
1.
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[
66]
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[
67]
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[
68]
,
[
69]
.
T
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(
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[
70]
.
M
L
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hi
dde
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[
71]
.
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ly
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[
72]
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t
h
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h
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m
pt
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n
d
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nd
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e
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a
nc
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r
.
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i
n
a
ll
y
,
a
l
l
o
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t
h
e
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l
go
r
i
t
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s
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r
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ly
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e
us
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f
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l
too
l
s
f
o
r
l
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n
g
can
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e
r
pr
e
di
c
t
i
o
n
.
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t
h
o
ugh
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h
e
m
o
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l
s
a
c
hi
e
ve
d
o
u
t
s
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n
d
i
n
g
m
e
t
r
i
c
s
,
i
t
i
s
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e
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o
m
m
e
n
de
d
f
o
r
f
ut
ur
e
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s
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r
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to
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x
p
l
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m
o
de
l
s
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h
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s
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NN
s
or
r
e
c
ur
r
e
n
t
n
e
ur
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l
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e
t
wo
r
ks
(
R
NN
s
)
to
s
e
e
i
f
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o
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m
pr
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pr
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d
i
c
t
i
o
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c
c
ur
a
c
y
.
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n
a
dd
i
t
i
o
n
,
i
t
w
o
ul
d
b
e
be
n
e
f
i
c
i
a
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o
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t
e
a
dd
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t
i
o
na
l
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a
r
ge
r
,
m
o
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v
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s
e
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o
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l
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t
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da
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s
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n
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ge
o
g
r
a
phi
c
r
e
g
i
o
n
s
,
to
de
t
e
r
m
i
ne
t
h
e
ge
n
e
r
a
l
i
z
a
bil
i
t
y
a
n
d
pe
r
f
o
r
m
a
n
c
e
o
f
m
o
de
l
s
i
n
d
if
f
e
r
e
n
t
c
o
n
t
e
x
t
s
,
a
l
l
o
w
i
ng
t
h
e
m
to
b
e
tes
t
e
d
f
o
r
e
f
f
e
c
t
i
v
e
n
e
s
s
.
RE
F
E
R
E
NC
E
S
[
1]
W
o
r
ld
H
e
a
lt
h
O
r
ga
ni
z
a
ti
o
n,
“
L
ung
c
a
n
c
e
r
,”
W
H
O
,
20
23.
ht
tp
s
:/
/ww
w
.w
ho
.i
nt
/n
e
w
s
-
r
oo
m/
f
a
c
t
-
s
he
e
ts
/d
e
ta
il
/l
ung
-
c
a
nc
e
r
(
a
c
c
e
s
s
e
d D
e
c
. 03, 2023)
.
[
2]
L
.
A
.
T
o
r
r
e
,
F
.
B
r
a
y
,
R
.
L
.
S
i
e
ge
l,
J
.
F
e
r
la
y
,
J
.
L
o
r
t
e
t‐
T
i
e
ul
e
nt
,
a
nd
A
.
J
e
ma
l,
“
G
lo
ba
l
c
a
n
c
e
r
s
ta
ti
s
ti
c
s
,
2012,”
C
A
:
A
C
a
nc
e
r
J
our
nal
f
o
r
C
li
ni
c
ia
ns
, v
o
l.
65, n
o
. 2, pp. 87
–
108, M
a
r
. 2015, do
i:
10.3322/
c
a
a
c
.21262.
[
3]
N
. H
o
w
la
d
e
r
e
t
a
l.
, “
T
h
e
e
f
f
e
c
t
of
a
d
v
a
nc
e
s
i
n l
ung
-
c
a
nc
e
r
tr
e
a
t
me
nt
o
n
p
o
pul
a
ti
o
n m
o
r
ta
li
t
y
,”
N
e
w
E
ngl
and J
our
nal
of
M
e
di
c
in
e
,
vo
l.
383, n
o
. 7, pp. 640
–
649, Aug. 2020, d
o
i:
10.1056/n
e
jm
o
a
1
916623.
[
4]
W
.
D
.
T
r
a
v
is
,
E
.
B
r
a
mbi
ll
a
,
A
.
P
.
B
u
r
k
e
,
A
.
M
a
r
x
,
a
nd
A
.
G
.
N
ic
h
o
ls
o
n,
“
I
n
tr
o
du
c
ti
o
n
t
o
th
e
2015
W
o
r
ld
H
e
a
lt
h
O
r
ga
ni
z
a
ti
o
n
c
la
s
s
if
i
c
a
ti
o
n
of
t
um
o
r
s
of
t
h
e
l
ung, pl
e
u
r
a
, t
h
y
mus
, a
nd he
a
r
t,
”
J
our
nal
of
T
hor
a
c
ic
O
nc
ol
ogy
, v
o
l.
10, n
o
. 9, pp. 1240
–
1242,
S
e
p.
2015, do
i:
10.1097/J
T
O
.0000000000000663.
[
5]
W
.
D
.
T
r
a
v
is
e
t
al
.
,
“
T
h
e
2015
W
o
r
ld
H
e
a
lt
h
O
r
g
a
ni
z
a
ti
o
n
c
la
s
s
if
i
c
a
ti
o
n
of
lu
ng
tu
m
or
s
:
im
pa
c
t
of
g
e
n
e
ti
c
,
c
li
ni
c
a
l
a
nd
r
a
di
o
l
o
gi
c
a
dv
a
nc
e
s
s
in
c
e
th
e
2004
c
la
s
s
if
i
c
a
ti
o
n,”
J
our
nal
of
T
hor
ac
ic
O
nc
ol
ogy
,
vo
l.
10,
n
o
.
9,
pp.
1243
–
1260,
S
e
p.
20
15,
do
i:
10.1097/J
T
O
.0000000000000630.
[
6]
K
.
C
.
T
ha
ndr
a
,
A
.
B
a
r
s
o
uk,
K
.
S
a
gi
na
la
,
J
.
S
.
A
lu
r
u,
a
nd
A
.
B
a
r
s
o
uk,
“
E
pi
de
mi
o
l
o
g
y
of
lu
ng
c
a
n
c
e
r
,”
W
s
pol
c
z
e
s
na
O
nk
ol
ogi
a
,
vo
l.
25, n
o
. 1, pp. 45
–
52, 2021, d
o
i:
10.5114/w
o
.2021.103829.
[
7]
M
.
B
.
S
c
ha
ba
th
a
nd
M
.
L
.
C
o
t
e
,
“
C
a
nc
e
r
pr
o
gr
e
s
s
a
nd
pr
io
r
it
i
e
s
:
l
ung
c
a
nc
e
r
,”
C
anc
e
r
E
pi
de
m
io
lo
gy
B
io
m
ar
k
e
r
s
and
P
r
e
v
e
nt
io
n
,
vo
l.
28, n
o
. 10, pp. 1563
–
1579, O
c
t.
2019, d
o
i:
10.1158/1055
-
9
965.E
P
I
-
19
-
0221.
[
8]
R
.
N
oo
r
e
ld
e
e
n
a
nd
H
.
B
a
c
h,
“
C
ur
r
e
nt
a
nd
f
ut
ur
e
d
e
ve
l
o
pm
e
nt
in
lu
ng
c
a
nc
e
r
di
a
gn
o
s
is
,”
I
nt
e
r
nat
io
nal
J
our
nal
of
M
ol
e
c
ul
ar
Sc
ie
nc
e
s
, v
o
l
. 22, n
o
. 16, p. 8661, Aug. 2
021, d
o
i:
10.3390/i
jm
s
22168661.
[
9]
A
.
H
.
K
r
is
t
e
t
al
.
,
“
S
c
r
e
e
ni
ng
f
or
lu
ng
c
a
nc
e
r
:
U
S
pr
e
ve
nt
i
ve
s
e
r
v
i
c
e
s
ta
s
k
f
o
r
c
e
r
e
c
o
mm
e
nda
ti
o
n
s
ta
te
m
e
nt
,”
J
A
M
A
-
J
our
nal
of
th
e
A
m
e
r
ic
an M
e
di
c
al
A
s
s
oc
ia
ti
on
, v
o
l.
325, n
o
. 10, pp. 962
–
97
0,
M
a
r
. 2021, do
i:
10.1001/j
a
ma
.2021.1117.
[
10]
D
.
Y
a
ng,
Y
.
L
iu
,
C
.
B
a
i,
X
.
W
a
ng,
a
nd
C
. A
.
P
o
w
e
ll
,
“
E
pi
d
e
mi
o
l
o
g
y
of
lu
ng
c
a
nc
e
r
a
nd
lu
ng
c
a
nc
e
r
s
c
r
e
e
ni
ng
pr
o
gr
a
ms
in
C
hi
na
a
nd t
he
U
ni
te
d
S
ta
te
s
,”
C
anc
e
r
L
e
tt
e
r
s
, v
o
l.
468, pp. 82
–
87, J
a
n. 2020, do
i:
10.1016/
j.
c
a
nl
e
t.
2019.10.009.
[
11]
W
o
r
ld
H
e
a
lt
h
O
r
ga
ni
z
a
ti
o
n,
“
T
h
e
t
o
p
10
c
a
us
e
s
of
de
a
th
,”
W
or
ld
H
e
al
th
O
r
gani
z
at
io
n
,
vol
.
12,
2020,
[
O
nl
in
e
]
.
A
v
a
il
a
bl
e
:
ht
tp
s
:/
/ww
w
.w
ho
.i
nt
/n
e
w
s
-
r
oo
m/
f
a
c
t
-
s
h
e
e
ts
/d
e
ta
il
/t
h
e
-
t
o
p
-
10
-
c
a
us
e
s
-
of
-
de
a
th
(
a
c
c
e
s
s
e
d D
e
c
. 03, 2023)
.
[
12]
N
a
ti
o
na
l
C
a
nc
e
r
I
ns
ti
tu
t
e
,
“
C
a
nc
e
r
s
ta
t
f
a
c
ts
:
lu
ng
a
nd
br
o
nc
h
us
c
a
nc
e
r
.
s
e
c
o
nda
r
y
c
a
n
c
e
r
s
ta
t
f
a
c
ts
:
lu
ng
a
nd
br
o
n
c
hus
c
a
nc
e
r
,”
Sur
v
e
il
la
nc
e
,
E
pi
de
m
io
lo
gy
,
and
E
nd
R
e
s
ul
ts
P
r
ogr
am
,
2021,
[
O
nl
in
e
]
.
A
v
a
il
a
bl
e
:
ht
tp
s
:/
/s
e
e
r
.c
a
n
c
e
r
.g
ov
/s
ta
t
f
a
c
ts
/h
tm
l
/l
ungb.ht
ml
(
a
c
c
e
s
s
e
d D
e
c
.
03, 2023)
.
[
13]
“
A
me
r
i
c
a
n
C
a
nc
e
r
S
oc
i
e
t
y
,”
“
L
ung
c
a
nc
e
r
s
ta
ti
s
ti
c
s
|
ho
w
c
omm
o
n
is
lu
ng
c
a
n
c
e
r
,”
F
ac
ts
and
F
ig
ur
e
s
2020
,
2020,
[
O
nl
in
e
]
.
A
v
a
il
a
bl
e
:
ht
tp
s
:/
/ww
w
.c
a
n
c
e
r
.
o
r
g/
c
a
nc
e
r
/t
y
pe
s
/l
ung
-
c
a
n
c
e
r
/a
b
o
ut
/k
e
y
-
s
ta
ti
s
ti
c
s
.ht
ml
(
a
c
c
e
s
s
e
d D
e
c
. 0
3, 2023)
.
[
14]
“
C
á
nc
e
r
d
e
pul
món,
tr
a
qu
e
a
y
br
o
nqui
o
s
:
f
a
ll
e
c
im
i
e
nt
o
s
p
o
r
pa
ís
O
C
D
E
,”
St
at
is
ta
,
2023,
[
O
nl
in
e
]
.
A
v
a
il
a
bl
e
:
ht
tp
s
:/
/e
s
.s
ta
ti
s
ta
.c
o
m
/e
s
ta
di
s
ti
c
a
s
/5
88401/num
e
r
o
-
de
-
mue
r
t
e
s
-
po
r
-
ne
o
p
la
s
ia
-
en
-
d
e
t
e
r
mi
na
d
o
s
-
pa
is
e
s
-
de
-
la
-
oc
d
e
/
(
a
c
c
e
s
s
e
d
D
e
c
.
03, 202
3)
.
[
15]
W
o
r
ld
H
e
a
lt
h
O
r
ga
ni
z
a
ti
o
n,
“
W
o
r
ld
H
e
a
lt
h
O
r
ga
ni
z
a
ti
o
n.
w
o
r
ld
he
a
lt
h
r
a
nki
ngs
li
ve
l
o
ng
e
r
l
i
v
e
be
t
te
r
2016,”
W
or
ld
H
e
al
th
O
r
gani
z
at
io
n
, 2016, [
O
nl
in
e
]
. A
v
a
il
a
bl
e
:
w
w
w
.w
o
r
ld
li
f
e
e
x
pe
c
t
a
nc
y
.c
o
m/
w
or
ld
-
h
e
a
lt
h
-
r
a
nki
ngs
(
a
c
c
e
s
s
e
d D
e
c
. 03, 2023)
.
[
16]
B
.
Z
h
o
u
e
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.
,
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ld
w
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r
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a
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pi
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a
l,
br
o
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c
hus
,
a
nd
lu
ng
c
a
nc
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r
:
a
po
pul
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ti
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-
ba
s
e
d
s
tu
d
y
,”
e
B
io
M
e
di
c
in
e
, vo
l.
78, p. 103951, Apr
. 2022, d
oi
:
10.1016/j
.
e
bi
o
m.2022.103951.
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