I
nte
rna
t
io
na
l J
o
urna
l o
f
E
lect
rica
l a
nd
Co
m
pu
t
er
E
ng
ineering
(
I
J
E
CE
)
Vo
l.
14
,
No
.
6
,
Dec
em
b
er
20
24
,
p
p
.
6
8
4
6
~
6
8
6
0
I
SS
N:
2088
-
8
7
0
8
,
DOI
: 1
0
.
1
1
5
9
1
/ijece.
v
14
i
6
.
pp
6
8
4
6
-
6
8
6
0
6846
J
o
ur
na
l ho
m
ep
a
g
e
:
h
ttp
:
//ij
ec
e.
ia
esco
r
e.
co
m
A
study
o
f
To
ba
cco us
e and
mo
rtal
ity by
data
minin
g
L
a
beria
no
Andra
de
-
Are
na
s
1
,
I
no
c
Rubi
o
P
a
uca
r
2
,
Cesa
r
Ya
ct
a
y
o
-
Aria
s
3
1
F
a
c
u
l
t
a
d
d
e
C
i
e
n
c
i
a
s e
I
n
g
e
n
i
e
r
í
a
,
U
n
i
v
e
r
s
i
d
a
d
d
e
C
i
e
n
c
i
a
s
y
H
u
m
a
n
i
d
a
d
e
s,
Li
ma,
P
e
r
ú
2
F
a
c
u
l
t
a
d
d
e
I
n
g
e
n
i
e
r
í
a
y
N
e
g
o
c
i
o
s,
U
n
i
v
e
r
s
i
d
a
d
P
r
i
v
a
d
a
N
o
r
b
e
r
t
W
i
e
n
e
r
,
L
i
ma,
P
e
r
ú
3
D
e
p
a
r
t
a
m
e
n
t
o
d
e
Est
u
d
i
o
s Ge
n
e
r
a
l
e
s
,
U
n
i
v
e
r
si
d
a
d
C
o
n
t
i
n
e
n
t
a
l
,
Li
ma,
P
e
r
ú
Art
icle
I
nfo
AB
S
T
RAC
T
A
r
ticle
his
to
r
y:
R
ec
eiv
ed
Feb
2
7
,
2
0
2
4
R
ev
is
ed
J
u
l 1
1
,
2
0
2
4
Acc
ep
ted
J
u
l 1
7
,
2
0
2
4
Th
e
u
se
o
f
d
a
ta
m
in
i
n
g
t
o
a
d
d
re
ss
th
e
issu
e
o
f
p
e
o
p
le
wh
o
c
o
n
s
u
m
e
to
b
a
c
c
o
a
n
d
o
th
e
r
h
a
rm
fu
l
su
b
sta
n
c
e
s
f
o
r
t
h
e
ir
h
e
a
lt
h
h
a
s
le
d
t
o
a
s
ig
n
ifi
c
a
n
t
d
e
p
e
n
d
e
n
c
e
a
m
o
n
g
sm
o
k
e
rs,
w
h
ich
o
v
e
r
ti
m
e
c
a
u
se
s
il
l
n
e
ss
e
s
th
a
t
m
a
y
re
su
lt
in
t
h
e
a
d
d
ict'
s
d
e
a
th
.
As
a
re
su
lt
,
th
e
re
se
a
rc
h
'
s
g
o
a
l
is
to
a
p
p
ly
a
d
a
ta
m
in
in
g
stu
d
y
wh
o
se
fin
d
in
g
s
sh
o
we
d
th
a
t
t
h
e
c
o
n
fi
d
e
n
c
e
in
ter
v
a
ls
a
re
les
s
th
a
n
0
.
3
5
5
.
Ho
we
v
e
r,
t
h
e
li
ft
a
n
d
c
o
n
v
ictio
n
in
t
h
e
las
t
th
re
e
ru
les
a
re
a
lso
lo
we
r,
m
a
k
in
g
it
u
n
li
k
e
ly
t
h
a
t
t
h
e
se
ru
les
will
b
e
fo
l
lo
we
d
.
On
t
h
e
o
th
e
r
h
a
n
d
,
th
e
k
n
o
wle
d
g
e
d
isc
o
v
e
ry
in
d
a
ta
b
a
se
s
m
e
th
o
d
wa
s
u
se
d
.
It
c
o
n
sists
o
f
th
e
fo
ll
o
wi
n
g
sta
g
e
s:
d
a
ta
se
lec
ti
o
n
,
p
re
p
a
ra
ti
o
n
,
d
a
ta
m
in
in
g
,
a
n
d
e
v
a
lu
a
ti
o
n
a
n
d
i
n
terp
re
tati
o
n
o
f
t
h
e
re
su
lt
s
.
T
o
t
h
a
t
e
n
d
,
c
o
m
p
a
riso
n
s
o
f
a
g
il
e
d
a
ta
m
in
in
g
m
e
th
o
d
o
l
o
g
ies
li
k
e
c
ris
p
-
d
m
,
k
n
o
wle
d
g
e
d
isc
o
v
e
ry
i
n
d
a
ta
,
a
n
d
S
e
m
m
a
a
r
e
a
lso
d
o
n
e
.
As
a
re
su
lt
,
u
si
n
g
s
p
e
c
ifi
c
c
rit
e
ria,
d
ime
n
sio
n
s
a
re
se
g
m
e
n
ted
to
a
ll
o
w
fo
r
t
h
e
d
iff
e
re
n
ti
a
ti
o
n
o
f
t
h
e
se
m
e
th
o
d
o
lo
g
ies
.
As
a
re
su
lt
,
a
c
o
m
p
a
riso
n
g
ra
p
h
o
f
m
o
d
e
ls
s
u
c
h
a
s
n
a
iv
e
Ba
y
e
s,
d
e
c
isio
n
tree
s,
a
n
d
r
u
le
in
d
u
c
ti
o
n
is
u
se
d
.
T
o
su
m
u
p
,
it
c
a
n
b
e
sa
id
th
a
t
th
e
ru
les
o
f
a
ss
o
c
iatio
n
a
p
p
l
y
t
o
m
e
n
,
t
h
e
n
u
m
b
e
r
o
f
a
d
m
issio
n
s,
a
n
d
t
h
e
c
a
n
c
e
rs
th
a
t
c
a
n
b
e
b
r
o
u
g
h
t
o
n
b
y
sm
o
k
in
g
.
Als
o
,
th
e
p
e
rc
e
n
tag
e
o
f
m
a
le
p
a
ti
e
n
ts
a
d
m
it
ted
with
c
a
n
c
e
rs
t
h
a
t
c
a
n
b
e
b
ro
u
g
h
t
o
n
b
y
sm
o
k
in
g
Las
t
b
u
t
n
o
t
lea
st,
t
h
e
n
u
m
b
e
r
o
f
a
d
m
issio
n
s a
n
d
c
a
n
c
e
rs t
h
a
t
c
a
n
b
e
b
ro
u
g
h
t
o
n
b
y
sm
o
k
i
n
g
K
ey
w
o
r
d
s
:
A
p
r
io
r
i
Data
m
in
in
g
Kn
o
wled
g
e
d
is
co
v
e
r
y
in
d
ata
R
u
les o
f
ass
o
ciatio
n
T
o
b
ac
co
T
h
is i
s
a
n
o
p
e
n
a
c
c
e
ss
a
rticle
u
n
d
e
r th
e
CC B
Y
-
SA
li
c
e
n
se
.
C
o
r
r
e
s
p
o
nd
ing
A
uth
o
r
:
C
esar
Yac
tay
o
-
Ar
ias
Dep
ar
tam
en
to
d
e
E
s
tu
d
io
s
Ge
n
er
ales
,
Un
iv
er
s
id
ad
C
o
n
tin
e
n
tal
L
im
a
,
Per
ú
E
m
ail: c
y
ac
tay
o
@
co
n
tin
en
tal.
ed
u
.
p
e
1.
I
NT
RO
D
UCT
I
O
N
Peo
p
le
wh
o
ar
e
d
ep
e
n
d
en
t
o
n
th
is
s
u
b
s
tan
ce
h
av
e
a
p
r
o
g
r
ess
iv
e
p
r
o
b
lem
as
a
r
esu
lt
o
f
u
s
in
g
to
b
ac
c
o
.
B
ec
au
s
e
n
ico
tin
e
is
th
eir
ac
ti
v
e
co
m
p
o
n
en
t,
s
m
o
k
er
s
f
r
eq
u
e
n
tly
s
u
f
f
er
f
r
o
m
a
v
ar
iet
y
o
f
d
is
ea
s
es
d
u
e
to
th
eir
co
n
s
u
m
p
tio
n
o
r
a
b
s
tin
en
ce
f
r
o
m
n
ico
tin
e.
Acc
o
r
d
in
g
to
a
s
tu
d
y
c
o
n
d
u
cted
b
y
th
e
W
o
r
l
d
H
ea
lth
Or
g
an
izatio
n
(
W
HO
)
,
2
2
.
3
%
o
f
th
e
wo
r
ld
'
s
p
o
p
u
latio
n
is
ex
p
ec
ted
t
o
u
s
e
to
b
ac
co
in
2
0
2
0
.
T
o
b
e
p
r
ec
is
e,
am
o
n
g
m
en
,
3
6
.
7
% a
r
e
a
d
d
icted
to
to
b
ac
co
,
an
d
am
o
n
g
wo
m
en
,
7
.
8
% a
r
e
ad
d
icted
to
t
h
is
s
u
b
s
tan
ce
[
1
]
.
On
e
o
f
th
e
lea
d
in
g
ca
u
s
es
o
f
d
ea
th
in
r
ec
e
n
t
y
ea
r
s
is
to
b
ac
co
.
T
h
is
im
p
lies
th
at
ce
r
tai
n
d
is
ea
s
es
m
an
if
est
th
em
s
elv
es
in
ac
co
r
d
an
ce
with
ex
ce
s
s
iv
e
n
ico
tin
e
co
n
s
u
m
p
tio
n
[
2
]
.
T
o
b
ac
co
c
o
n
s
u
m
p
tio
n
h
as
th
e
ab
ilit
y
to
ca
u
s
e
d
is
ea
s
es
th
at
r
esu
lt
in
d
ea
th
,
t
h
o
u
g
h
.
B
u
t
t
h
e
tr
u
th
is
th
at
c
o
u
n
tr
ies
th
at
h
a
v
e
u
s
ed
m
et
h
o
d
s
to
cu
r
b
th
is
d
is
ea
s
e
h
av
e
h
elp
ed
to
ac
h
iev
e
p
o
s
itiv
e
r
esu
lts
[
3
]
.
T
h
e
y
o
u
n
g
er
g
en
er
atio
n
is
b
ein
g
in
cr
ea
s
in
g
ly
ex
p
o
s
ed
to
th
e
co
n
s
u
m
p
tio
n
o
f
h
ar
m
f
u
l
s
u
b
s
tan
ce
s
lik
e
t
o
b
ac
co
[
4
]
.
T
h
e
u
s
e
o
f
to
b
ac
co
d
u
r
in
g
ad
o
lescen
ce
ac
ts
as
a
p
r
ec
u
r
s
o
r
to
th
e
a
d
a
p
tatio
n
o
f
o
th
er
b
r
ain
-
h
ar
m
in
g
s
u
b
s
tan
ce
s
in
th
e
f
u
tu
r
e
[
5
]
.
L
o
n
g
-
ter
m
ef
f
ec
ts
r
esu
lt
in
a
d
ec
lin
e
in
th
e
p
er
s
o
n
'
s
co
g
n
itiv
e
ab
ilit
ies
an
d
ch
an
g
es
to
th
eir
lev
el
o
f
ag
itatio
n
[
6
]
.
Stu
d
ies
wer
e
co
n
d
u
cte
d
o
n
th
e
f
o
llo
win
g
s
y
m
p
to
m
s
,
s
u
ch
as
p
s
y
ch
o
lo
g
ica
l
is
s
u
es,
co
n
d
u
ct
is
s
u
es,
an
d
n
eu
r
o
lo
g
ical
is
s
u
es,
r
eg
ar
d
in
g
n
ico
tin
ic
in
ta
k
e
a
n
d
th
e
u
s
e
o
f
ex
o
g
en
o
u
s
p
r
o
g
est
er
o
n
e
f
o
r
tr
ea
tin
g
th
e
d
is
ea
s
e
[
7
]
.
C
an
ce
r
-
r
elate
d
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
A
s
tu
d
y
o
f To
b
a
c
co
u
s
e
a
n
d
m
o
r
ta
lity b
y
d
a
ta
min
in
g
(
La
b
eria
n
o
A
n
d
r
a
d
e
-
A
r
en
a
s
)
6847
d
is
ea
s
es
ar
e
ca
u
s
ed
b
y
m
o
r
e
th
an
ju
s
t
ex
ce
s
s
iv
e
to
b
ac
co
u
s
e.
Patien
ts
with
ca
n
ce
r
ar
e
a
ls
o
im
p
ac
ted
b
y
a
tem
p
o
r
ar
y
to
b
ac
co
d
e
p
en
d
e
n
c
e.
T
h
er
ef
o
r
e
,
th
e
lin
k
b
etwe
e
n
s
m
o
k
in
g
to
b
ac
c
o
an
d
ca
n
ce
r
af
f
ec
ts
th
e
p
atien
t
p
s
y
ch
o
lo
g
ically
,
lead
in
g
t
o
u
n
d
esira
b
le
r
ea
ctio
n
s
in
th
e
p
er
s
o
n
'
s
b
eh
av
io
r
[
8
]
.
Sm
o
k
in
g
-
r
elate
d
illn
ess
es
h
av
e
p
lay
ed
a
r
o
le
in
th
e
d
ev
elo
p
m
en
t
o
f
d
is
ea
s
es,
in
clu
d
in
g
lu
n
g
ca
n
ce
r
.
o
s
teo
ar
th
r
itis
(
OA)
,
o
s
teo
p
o
r
o
s
is
(
O
P),
an
d
r
h
eu
m
ato
i
d
ar
th
r
itis
(
A
R
)
h
av
e
all
b
ee
n
lin
k
ed
to
th
e
d
ev
elo
p
m
e
n
t
o
f
ca
d
m
iu
m
(
C
D)
,
wh
ich
is
f
o
u
n
d
i
n
to
b
ac
c
o
th
r
o
u
g
h
h
u
m
u
s
[
9
]
.
On
th
e
o
th
er
h
an
d
,
th
i
s
in
f
ec
tio
n
ca
u
s
es
a
s
er
io
u
s
in
cr
ea
s
e
in
C
D
lev
els
in
th
e
b
l
o
o
d
,
wh
ich
ca
u
s
es
o
x
id
ativ
e
s
tr
ess
an
d
r
esu
lts
in
a
lo
s
s
o
f
ch
o
lest
er
o
l
[
1
0
]
.
T
h
e
r
is
e
in
s
m
o
k
er
s
h
as
led
to
th
e
d
e
v
elo
p
m
e
n
t
o
f
s
er
i
o
u
s
h
ea
lth
is
s
u
es
lik
e
lu
n
g
ca
n
ce
r
.
E
a
r
ly
d
is
ea
s
e
d
etec
tio
n
o
f
f
er
s
th
e
p
o
ten
tial
to
co
n
tr
o
l
th
e
co
n
d
itio
n
a
n
d
s
av
e
m
an
y
p
eo
p
le'
s
liv
es.
C
er
tain
tr
ea
tm
en
ts
p
r
escr
ib
ed
b
y
o
n
co
lo
g
y
s
p
ec
i
alis
ts
h
av
e
b
ee
n
d
e
v
elo
p
ed
th
an
k
s
to
tech
n
o
lo
g
ies
lik
e
c
o
m
p
u
ter
ized
r
a
d
io
lo
g
y
[
1
1
]
.
T
h
e
r
em
o
v
al
o
f
l
u
n
g
n
o
d
u
les
an
d
o
th
er
s
u
r
g
ical
p
r
o
c
ed
u
r
es
h
av
e
p
r
e
v
en
ted
t
h
e
g
r
o
wth
o
f
ca
n
ce
r
o
u
s
ce
lls
b
r
o
u
g
h
t
o
n
b
y
to
b
ac
co
u
s
e
wh
ile
estab
lis
h
in
g
h
ea
lth
p
r
e
ca
u
tio
n
s
f
o
r
t
h
eir
tr
ea
tm
en
t
[
1
2
]
.
I
n
o
n
e
s
tu
d
y
,
d
ata
m
in
in
g
wit
h
class
if
icatio
n
alg
o
r
ith
m
s
was
u
s
ed
with
a
s
am
p
le
o
f
2
2
,
0
0
0
s
tu
d
en
ts
b
etwe
en
th
e
ag
es
o
f
1
4
an
d
1
9
.
T
h
er
e
wer
e
s
ig
n
s
th
er
e
th
at
m
en
tio
n
ed
teen
ag
er
s
s
m
o
k
in
g
to
b
ac
co
[
1
3
]
.
On
th
e
o
th
er
h
an
d
,
a
p
r
o
t
o
ty
p
e
t
h
at
p
r
ed
icts
th
e
h
ig
h
r
is
k
o
f
to
b
ac
co
s
m
o
k
er
s
co
m
p
ar
ed
t
o
a
g
r
o
u
p
o
f
n
o
n
-
s
m
o
k
er
s
was
u
s
ed
.
Usi
n
g
a
u
to
m
atic
lear
n
in
g
alg
o
r
ith
m
s
,
3
1
8
p
eo
p
le
wer
e
s
u
b
jecte
d
to
th
is
p
r
ed
ictio
n
ac
co
r
d
in
g
to
its
s
y
m
p
to
m
s
[
1
4
]
.
T
h
e
u
s
e
o
f
s
p
ec
ialized
s
y
s
tem
s
m
an
ag
ed
with
th
e
k
n
o
wled
g
e
o
f
a
d
ata
b
ase
k
n
o
wn
as
San
Facto
r
im
p
lem
en
ted
f
o
r
th
e
p
r
ev
en
tio
n
o
f
q
u
itti
n
g
s
m
o
k
in
g
h
as
h
elp
ed
d
ec
is
io
n
-
m
ak
er
s
ad
o
p
t
s
p
ec
if
ic
m
ed
ical
tr
ea
tm
en
ts
[
1
5
]
.
Ho
wev
e
r
,
g
i
v
in
g
u
p
s
m
o
k
i
n
g
r
e
q
u
ir
es
a
s
ig
n
if
ica
n
t
p
s
y
c
h
o
lo
g
ical
ef
f
o
r
t
f
o
r
s
o
m
eo
n
e
ad
d
icted
t
o
an
o
th
e
r
s
u
b
s
tan
ce
,
esp
ec
ially
if
th
ey
h
av
e
b
ee
n
d
ia
g
n
o
s
ed
with
ca
n
c
er
.
T
o
ac
h
iev
e
t
h
is
g
o
al,
th
e
u
s
e
o
f
d
ee
p
lear
n
i
n
g
m
o
d
els
en
ab
les
th
e
p
r
o
ce
s
s
in
g
o
f
lar
g
e
am
o
u
n
ts
o
f
d
ata,
f
in
d
in
g
cr
u
cial
ch
ar
ac
ter
is
tics
lik
e
th
e
ce
s
s
a
ti
o
n
o
f
s
m
o
k
in
g
h
a
b
its
[
1
6
]
.
A
n
o
th
er
s
tu
d
y
'
s
u
s
e
o
f
alg
o
r
ith
m
s
lik
e
n
aiv
e
B
ay
es
an
d
R
an
d
o
m
Fo
r
est
im
p
lem
e
n
ted
u
s
in
g
th
e
W
E
KA
to
o
l
to
an
aly
ze
th
e
ab
an
d
o
n
m
en
t
o
f
s
m
o
k
er
s
r
esu
lted
in
th
e
p
r
o
v
is
io
n
o
f
2
0
d
is
tin
ct
attr
ib
u
tes to
p
r
e
d
ict
th
e
n
u
m
b
er
o
f
p
eo
p
le
wh
o
will g
iv
e
u
p
t
h
is
h
ab
it
[
1
7
]
.
T
h
e
r
esear
ch
is
aim
ed
at
co
n
tr
ib
u
tin
g
n
ew
k
n
o
wled
g
e
o
r
p
e
r
s
p
ec
tiv
es
to
th
e
s
p
ec
if
ic
f
ield
o
f
s
tu
d
y
,
ad
d
r
ess
in
g
g
ap
s
in
th
e
ex
i
s
tin
g
liter
atu
r
e,
s
o
lv
in
g
p
r
a
ctica
l
o
r
co
m
p
lex
p
r
o
b
lem
s
,
im
p
r
o
v
in
g
t
h
e
u
n
d
er
s
tan
d
i
n
g
o
f
s
p
ec
if
ic
p
h
e
n
o
m
en
a.
T
h
e
d
ata
m
in
i
n
g
ai
m
s
to
m
ak
e
d
ec
is
io
n
s
in
ac
c
o
r
d
an
ce
with
s
o
m
e
p
r
ed
ictio
n
s
b
y
em
p
l
o
y
in
g
al
g
o
r
ith
m
s
th
at
allo
w
f
o
r
s
o
cieta
lly
ac
ce
p
tab
le
o
u
tco
m
es.
As
a
r
esu
lt,
th
e
in
v
esti
g
atio
n
'
s
g
o
al
is
to
co
n
d
u
ct
a
s
tu
d
y
o
n
p
e
o
p
le
wh
o
ar
e
ad
d
icted
to
to
b
ac
co
u
s
e
u
s
in
g
d
ata
m
in
in
g
.
2.
L
I
T
E
R
AT
U
RE
R
E
VI
E
W
T
h
is
s
ec
tio
n
p
r
o
p
o
s
es
two
v
er
y
in
ter
esti
n
g
s
eg
m
en
ts
.
T
h
e
f
ir
s
t
f
o
cu
s
es
o
n
r
esear
ch
p
r
o
jects
d
ea
lin
g
with
th
e
to
p
ic
o
f
to
b
ac
co
co
n
s
u
m
p
tio
n
wh
ile
u
tili
zin
g
s
p
ec
if
ic
tech
n
o
lo
g
ies
lik
e
d
ata
m
i
n
in
g
.
Similar
ly
,
th
e
s
ec
o
n
d
p
o
i
n
t is r
elate
d
to
th
e
r
esear
ch
th
at
is
d
o
n
e
o
n
th
e
r
aised
to
p
ic.
2
.
1
.
T
heo
re
t
ica
l
ba
s
is
Data
m
in
in
g
is
u
s
ed
to
f
in
d
p
atter
n
s
in
b
eh
av
io
r
am
o
n
g
a
lar
g
e
am
o
u
n
t
o
f
d
ata
b
y
ex
tr
ac
tin
g
th
em
as
v
ar
iab
les
an
d
ap
p
ly
in
g
s
p
ec
if
i
c
alg
o
r
ith
m
s
d
ev
elo
p
ed
f
o
r
ea
ch
ac
tiv
ity
[
1
8
]
.
On
th
e
o
n
e
h
a
n
d
,
a
clea
r
e
x
am
p
le
is
th
e
au
to
m
atic
lear
n
in
g
a
p
p
licatio
n
,
wh
ich
in
s
o
m
e
s
tu
d
ies
aim
s
to
id
en
tify
an
d
co
m
p
ile
c
r
u
cial
ch
ar
ac
ter
is
tics
o
f
a
p
ar
ticu
lar
s
u
b
ject
u
s
in
g
m
ath
em
atica
l
al
g
o
r
ith
m
s
[
1
9
]
.
T
o
th
at
p
u
r
p
o
s
e,
th
e
ap
p
licatio
n
o
f
r
u
le
-
b
ased
a
n
aly
s
is
tak
es
in
to
ac
co
u
n
t
h
o
w
th
e
d
ata
an
aly
s
is
alg
o
r
ith
m
s
w
o
r
k
.
Ho
wev
er
,
m
ak
in
g
d
ec
is
io
n
s
is
b
ased
o
n
th
e
o
u
tc
o
m
es
o
f
a
cc
u
r
ate
p
r
ed
ictio
n
s
m
a
d
e
wit
h
tech
n
o
lo
g
ies
th
at
p
r
o
v
id
e
co
n
f
id
en
ce
in
th
eir
o
u
tco
m
es
[
2
0
]
.
T
h
e
alg
o
r
ith
m
s
u
s
ed
in
d
ata
m
i
n
in
g
in
clu
d
e
class
if
icatio
n
,
g
r
o
u
p
i
n
g
,
a
n
d
ass
o
ciatio
n
r
u
les
-
b
ased
alg
o
r
ith
m
s
,
am
o
n
g
o
t
h
e
r
s
,
to
p
r
e
d
ict
s
p
ec
if
ic
p
r
o
b
le
m
s
in
v
ar
io
u
s
s
o
cial
s
p
h
er
es.
E
ac
h
alg
o
r
ith
m
h
as
u
n
iq
u
e
ch
ar
ac
ter
is
tics
d
ep
e
n
d
i
n
g
o
n
h
o
w
it
is
u
s
ed
in
s
p
ec
if
i
c
ar
tific
ial
in
tellig
en
ce
(
AI
)
-
r
e
lated
ar
ea
s
[
2
1
]
.
I
n
r
ef
er
en
ce
t
o
th
e
d
is
cu
s
s
io
n
o
f
d
ata
m
in
in
g
,
m
a
n
y
to
o
ls
allo
w
f
o
r
th
e
cr
ea
tio
n
o
f
m
o
d
els
t
h
at
ar
e
s
u
itab
le
f
o
r
ea
ch
u
n
iq
u
e
s
itu
atio
n
.
Or
g
an
i
za
tio
n
s
th
at
m
a
n
ag
e
lar
g
e
am
o
u
n
ts
o
f
d
ata
ar
e
ab
le
to
m
ak
e
d
ec
is
io
n
s
in
a
m
o
r
e
s
p
ec
if
ic
m
an
n
er
th
a
n
k
s
to
th
e
ap
p
licatio
n
o
f
ce
r
tain
alg
o
r
ith
m
s
[
2
2
]
.
2
.
1
.
1
.
Cla
s
s
if
ica
t
io
n a
lg
o
rit
h
m
s
T
h
e
class
if
icatio
n
alg
o
r
ith
m
s
i
n
d
ata
m
in
in
g
cr
ea
te
m
o
d
els th
at
attem
p
t to
p
r
e
-
class
if
y
d
ata
r
elate
d
to
d
ec
is
io
n
tr
ee
s
o
r
n
eu
r
al
n
etwo
r
k
s
.
Fo
r
th
is
r
ea
s
o
n
,
th
e
s
y
s
tem
m
u
s
t
ca
r
r
y
o
u
t
a
lear
n
in
g
p
r
o
ce
s
s
b
y
an
aly
zin
g
d
ata
th
r
o
u
g
h
tr
ain
i
n
g
.
T
h
ese
tr
ain
ed
alg
o
r
it
h
m
s
en
ab
le
t
h
e
d
eter
m
in
atio
n
o
f
s
p
ec
if
ic
s
ets
o
f
p
r
escr
ib
ed
p
ar
am
eter
s
th
r
o
u
g
h
class
if
icati
o
n
r
u
les
[
2
3
]
.
2
.
1
.
2
.
Clus
t
er
ing
T
h
e
clu
s
ter
in
g
to
p
ic
allo
ws
f
o
r
th
e
ac
q
u
is
itio
n
o
f
u
n
ta
g
g
e
d
g
r
o
u
p
i
n
g
d
ata
in
o
r
d
er
to
c
ar
r
y
o
u
t
t
h
e
co
n
s
tr
u
ctio
n
o
f
g
r
o
u
p
in
g
s
o
f
d
ata
k
n
o
wn
as
clu
s
ter
s
.
I
n
th
is
s
en
s
e,
ea
ch
clu
s
ter
is
a
co
n
s
o
lid
ated
g
r
a
p
h
m
ad
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.
14
,
No
.
6
,
Dec
em
b
e
r
20
24
:
6
8
4
6
-
6
8
6
0
6848
u
p
o
f
a
g
r
o
u
p
o
f
item
s
o
r
d
at
a
th
at,
u
p
o
n
an
aly
s
is
,
ar
e
s
h
o
wn
to
b
e
co
m
p
ar
ab
le
to
o
n
e
an
o
th
er
.
B
u
t
at
th
e
s
am
e
tim
e,
th
ey
o
b
tai
n
elem
en
ts
th
at
ar
e
d
if
f
er
en
t
f
r
o
m
o
th
er
item
s
an
d
d
ata
s
ets
[
2
4
]
.
2
.
1
.
3
.
Neura
l net
wo
rk
s
I
n
n
e
u
r
al
n
etwo
r
k
alg
o
r
it
h
m
s
,
it
is
u
n
d
er
s
to
o
d
as
a
c
o
llectio
n
o
f
in
ter
c
o
n
n
ec
ted
en
tr
y
an
d
ex
it
u
n
its
.
T
h
e
n
eu
r
al
n
etwo
r
k
lear
n
s
to
ad
ju
s
t
ce
r
tain
weig
h
ts
to
p
r
e
d
ict
th
e
class
tu
p
le
lab
els
d
u
r
in
g
m
o
d
el
lear
n
in
g
.
Fo
r
th
is
r
ea
s
o
n
,
n
eu
r
al
n
etwo
r
k
s
ca
n
ex
tr
ac
t
m
ea
n
in
g
f
r
o
m
co
m
p
lex
o
r
p
r
in
ted
d
ata
u
s
ed
to
ex
tr
ac
t
p
atter
n
s
an
d
id
en
tif
y
cr
u
cial
tr
e
n
d
s
f
o
r
d
ec
is
io
n
-
m
ak
in
g
[
2
5
]
.
2
.
1
.
4
.
Dec
is
io
n t
re
es
A
d
ec
is
io
n
tr
ee
alg
o
r
ith
m
i
s
a
n
o
n
-
p
a
r
am
etr
ic
lear
n
i
n
g
alg
o
r
ith
m
th
at
is
em
p
l
o
y
e
d
f
o
r
b
o
th
class
if
icatio
n
an
d
r
eg
r
ess
io
n
t
ask
s
.
I
t
h
as
a
jer
r
y
-
r
ig
g
ed
tr
e
e
s
tr
u
ctu
r
e
m
ad
e
u
p
o
f
a
ce
n
t
r
al
n
o
d
e,
b
r
an
c
h
es,
in
ter
n
al
n
o
d
es,
an
d
h
o
a
r
y
n
o
d
es.
A
d
ec
is
io
n
-
m
ak
in
g
tr
ee
h
a
s
an
o
p
en
n
o
d
e
with
o
u
t
en
twi
n
ed
b
r
a
n
ch
es.
T
h
e
in
ter
n
al
n
o
d
es,
o
r
d
ec
is
io
n
n
o
d
es,
ar
e
f
e
d
b
y
th
e
r
ad
ial
b
r
a
n
ch
es
o
f
t
h
e
p
r
im
ar
y
n
o
d
e.
B
o
t
h
ty
p
es
o
f
n
o
d
es
d
o
ev
alu
atio
n
s
to
cr
ea
te
h
o
m
o
g
en
eo
u
s
s
u
b
s
ets.
T
o
p
u
t
it
an
o
th
er
way
,
d
ep
en
d
i
n
g
o
n
th
e
av
aila
b
le
ch
ar
ac
ter
is
tics
,
th
ey
ar
e
in
d
icate
d
b
y
n
o
d
es
h
o
ja
o
r
n
o
d
es
ter
m
in
als.
All
p
o
ten
tial
r
esu
lts
f
r
o
m
th
e
co
l
lectio
n
o
f
d
ata
ar
e
s
h
o
wn
as h
o
ar
y
n
o
d
es
[
2
6
]
.
2
.
2
.
Rela
t
ed
wo
rk
V
a
r
i
o
u
s
i
s
s
u
es
h
a
v
e
b
e
e
n
b
r
o
u
g
h
t
o
n
b
y
t
h
e
a
d
d
i
c
t
i
v
e
s
m
o
k
i
n
g
o
f
c
i
g
a
r
s
a
n
d
t
h
e
i
r
c
o
n
s
t
i
t
u
e
n
t
p
a
r
t
s
.
I
n
t
h
i
s
s
e
n
s
e
,
t
h
o
s
e
w
h
o
o
v
e
r
i
n
d
u
l
g
e
b
e
c
o
m
e
d
e
p
e
n
d
e
n
t
o
n
t
h
e
s
e
s
u
b
s
t
a
n
ce
s
b
e
c
a
u
s
e
t
h
e
y
li
k
e
f
e
e
l
i
n
g
r
e
l
a
x
e
d
a
n
d
f
i
g
h
t
i
n
g
t
h
e
p
s
y
c
h
o
l
o
g
i
ca
l
s
t
r
e
s
s
a
s
s
o
c
i
at
e
d
wi
t
h
s
m
o
k
i
n
g
[
2
7
]
.
A
s
a
r
es
u
l
t
,
i
t
w
a
s
p
r
o
p
o
s
e
d
i
n
r
es
e
a
r
c
h
t
o
c
l
a
s
s
i
f
y
s
m
o
k
e
r
s
u
s
i
n
g
t
h
e
t
e
c
h
n
i
q
u
e
o
f
d
a
t
a
m
i
n
i
n
g
,
c
a
t
e
g
o
r
i
zi
n
g
t
h
e
m
as
li
g
h
t
o
r
h
e
a
v
y
s
m
o
k
e
r
s
,
a
n
d
a
p
p
l
y
i
n
g
t
h
e
k
n
o
w
l
e
d
g
e
d
i
s
c
o
v
e
r
y
i
n
d
a
t
a
m
et
h
o
d
o
l
o
g
y
(
K
D
D
)
t
o
c
er
t
a
i
n
p
r
o
c
es
s
es
.
O
n
t
h
e
o
t
h
e
r
h
a
n
d
,
i
n
a
d
i
f
f
e
r
e
n
t
s
t
u
d
y
,
d
a
t
a
m
i
n
i
n
g
w
i
ll
b
e
u
s
ed
t
o
p
r
e
d
i
ct
t
h
e
m
a
r
k
e
t
d
e
m
a
n
d
f
o
r
c
i
g
a
r
s
a
n
d
t
h
e
i
n
d
i
v
i
d
u
a
ls
w
h
o
p
u
r
c
h
a
s
e
t
h
is
p
r
o
d
u
c
t
[
2
8
]
.
O
n
e
o
f
t
h
e
k
e
y
c
o
n
t
r
i
b
u
t
o
r
s
t
o
t
h
e
e
a
r
l
y
d
i
a
g
n
o
s
is
o
f
c
a
r
d
i
o
v
a
s
c
u
l
a
r
d
is
e
as
es
is
e
x
c
e
s
s
i
v
e
al
c
o
h
o
l
a
n
d
t
o
b
a
c
c
o
u
s
e
.
T
h
i
s
is
w
h
y
th
e
u
s
e
o
f
d
at
a
m
i
n
i
n
g
i
n
v
o
l
v
e
s
t
h
e
d
e
t
ec
t
i
o
n
o
f
f
a
ct
o
r
s
l
i
n
k
e
d
t
o
s
o
m
e
s
y
m
p
t
o
m
s
,
l
i
k
e
h
i
g
h
b
l
o
o
d
s
u
g
a
r
l
e
v
e
ls
,
h
i
g
h
c
h
o
l
e
s
t
e
r
o
l
,
a
n
d
h
i
g
h
ar
t
e
r
y
p
r
e
s
s
u
r
e
.
I
n
t
h
is
c
o
n
t
e
x
t
,
m
e
d
i
c
a
l
d
a
ta
a
r
e
a
n
a
l
y
z
e
d
u
s
i
n
g
d
a
t
a
m
i
n
i
n
g
t
e
ch
n
i
q
u
e
s
b
a
s
e
d
o
n
a
u
t
o
m
a
t
i
c
l
ea
r
n
i
n
g
[
2
9
]
.
F
i
n
a
l
l
y
,
t
h
e
u
s
e
o
f
t
h
e
s
e
m
a
t
h
e
m
a
ti
c
a
l
a
l
g
o
r
i
t
h
m
s
e
n
a
b
le
s
t
h
e
a
c
q
u
i
s
i
ti
o
n
o
f
f
a
v
o
r
a
b
l
e
r
e
s
u
l
ts
i
n
t
h
e
p
e
r
s
o
n
'
s
b
e
h
a
v
i
o
r
t
o
c
o
u
n
t
e
r
a
ct
e
x
c
e
s
s
i
v
e
t
o
b
a
c
c
o
c
o
n
s
u
m
p
t
i
o
n
.
M
a
k
i
n
g
d
e
c
is
i
o
n
s
o
n
m
e
d
i
t
a
ti
o
n
a
n
d
t
h
e
li
k
e
l
i
h
o
o
d
o
f
q
u
i
t
t
i
n
g
s
m
o
k
i
n
g
u
s
i
n
g
d
a
t
a
m
i
n
i
n
g
t
o
o
ls
w
i
l
l
b
e
p
o
s
s
i
b
l
e
t
h
a
n
k
s
t
o
t
h
e
d
e
v
e
l
o
p
m
e
n
t
o
f
m
o
d
e
l
s
f
o
r
h
y
b
r
i
d
p
r
e
d
i
c
t
i
v
e
a
n
a
l
y
s
is
[
3
0
]
.
3.
M
E
T
H
O
D
3
.
1
.
Def
ini
t
io
n o
f
t
he
K
DD
m
et
h
o
do
lo
g
y
T
h
e
KDD
m
eth
o
d
o
lo
g
y
is
a
p
r
o
ce
s
s
th
at
ex
tr
ac
ts
k
n
o
wl
ed
g
e
b
ased
o
n
a
lar
g
e
v
o
lu
m
e
o
f
d
ata
s
am
p
les.
B
ec
au
s
e
o
f
th
is
,
it
er
ativ
e,
p
r
ac
tical
m
eth
o
d
s
a
r
e
u
s
ed
wh
e
n
ev
er
it
is
n
ec
ess
ar
y
to
o
b
tain
th
is
in
f
o
r
m
atio
n
.
I
ts
m
ain
q
u
ality
i
s
ad
ap
tab
ilit
y
to
t
h
e
co
m
p
lete
d
p
r
o
ject
[
3
1
]
.
I
n
o
th
er
w
o
r
d
s
,
it
allo
ws
r
etu
r
n
in
g
to
ea
r
lier
p
h
ases
th
at
wer
e
d
ev
elo
p
e
d
f
o
r
u
p
d
atin
g
la
ter
p
h
ases
.
Sev
er
al
s
tep
s
m
ak
e
u
p
th
e
KDD
m
eth
o
d
o
l
o
g
y
'
s
d
ata
an
aly
s
is
p
r
o
ce
s
s
.
T
o
t
h
at
en
d
,
it
is
n
ec
ess
ar
y
to
ex
tr
ac
t
p
atr
o
n
s
,
wh
ich
ar
e
r
u
les
o
r
f
u
n
ctio
n
s
th
at
en
ab
le
d
ec
is
io
n
-
m
ak
in
g
as
s
h
o
wn
in
Fig
u
r
e
1
.
T
h
e
KDD
is
ju
s
tifie
d
in
th
e
s
t
u
d
y
o
f
to
b
ac
co
u
s
e
an
d
m
o
r
tality
d
u
e
to
its
ab
ilit
y
to
ad
d
r
ess
th
e
co
m
p
lex
ity
o
f
d
ata,
id
en
tif
y
h
id
d
en
p
atte
r
n
s
,
an
d
s
ig
n
if
ican
t
r
elatio
n
s
h
ip
s
in
lar
g
e
d
atasets
.
T
h
is
r
ig
o
r
o
u
s
m
eth
o
d
o
lo
g
ical
ap
p
r
o
ac
h
e
n
s
u
r
es
v
alid
atio
n
an
d
r
e
p
r
o
d
u
cib
ilit
y
o
f
r
esu
lts
,
cr
itical
asp
ec
ts
in
p
u
b
lic
h
ea
lth
r
esear
c
h
an
d
r
i
s
k
f
ac
to
r
s
s
u
ch
as
to
b
ac
co
u
s
e.
Fu
r
th
er
m
o
r
e,
its
p
r
ev
io
u
s
ap
p
licab
ilit
y
in
m
ed
ical
r
esear
ch
s
u
p
p
o
r
ts
its
u
til
ity
in
ex
tr
ac
tin
g
u
s
ef
u
l
in
s
ig
h
ts
th
at
ca
n
in
f
o
r
m
h
ea
lth
p
o
licies an
d
p
r
ev
e
n
tio
n
s
tr
ateg
ies.
3
.
2
.
Dev
elo
pm
ent
o
f
t
he
K
DD
m
e
t
ho
do
lo
g
y
I
n
th
is
s
ec
tio
n
,
we
d
elv
e
in
to
th
e
d
ev
elo
p
m
en
t
o
f
th
e
KDD
m
eth
o
d
o
lo
g
y
,
lev
er
ag
i
n
g
t
h
e
p
o
wer
f
u
l
ca
p
ab
ilit
ies
o
f
R
ap
id
Min
er
Stu
d
io
.
B
y
h
ar
n
ess
in
g
th
is
r
o
b
u
s
t
to
o
l
an
d
lev
e
r
ag
in
g
th
e
d
at
a
g
ath
er
e
d
f
r
o
m
th
e
s
p
ec
if
ied
to
p
ic,
we
ap
p
ly
a
s
et
o
f
p
r
ed
eter
m
in
ed
cr
iter
ia
t
o
g
u
id
e
o
u
r
an
al
y
tical
p
r
o
ce
s
s
.
W
ith
R
ap
id
Min
er
Stu
d
io
'
s
in
tu
itiv
e
in
ter
f
ac
e
an
d
co
m
p
r
eh
en
s
iv
e
s
u
ite
o
f
d
ata
m
in
in
g
f
u
n
ctio
n
alities
,
we
em
b
ar
k
o
n
a
s
y
s
tem
atic
jo
u
r
n
ey
th
r
o
u
g
h
t
h
e
v
ar
io
u
s
s
tag
es
o
f
th
e
KDD
p
r
o
ce
s
s
.
Fro
m
d
ata
s
elec
tio
n
an
d
p
r
ep
r
o
ce
s
s
in
g
to
m
o
d
elin
g
an
d
ev
al
u
atio
n
,
ea
c
h
s
tep
is
m
eticu
lo
u
s
ly
e
x
ec
u
te
d
to
u
n
c
o
v
er
h
id
d
en
p
atter
n
s
,
tr
en
d
s
,
an
d
i
n
s
ig
h
ts
with
in
th
e
d
ataset.
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
A
s
tu
d
y
o
f To
b
a
c
co
u
s
e
a
n
d
m
o
r
ta
lity b
y
d
a
ta
min
in
g
(
La
b
eria
n
o
A
n
d
r
a
d
e
-
A
r
en
a
s
)
6849
3
.
2
.
1
.
Da
t
a
s
elec
t
io
n
A
d
atab
ase
o
f
d
ata
a
b
o
u
t
p
e
o
p
le
wh
o
ar
e
to
b
ac
co
ad
d
icts
was
o
b
tain
ed
d
u
r
in
g
th
e
d
ata
s
elec
tio
n
p
r
o
ce
s
s
.
W
h
ich
p
r
o
v
i
d
es
th
e
d
iag
n
o
s
es
as
a
co
n
s
eq
u
e
n
ce
o
f
m
e
d
ical
ex
am
i
n
atio
n
s
p
e
r
f
o
r
m
ed
b
y
s
u
b
ject
-
m
atter
ex
p
er
ts
.
T
h
e
d
ata
clea
n
in
g
p
r
o
ce
s
s
is
th
en
ca
r
r
ied
o
u
t
af
ter
m
ak
in
g
s
u
r
e
th
at
th
e
r
e
a
r
e
n
o
b
la
n
k
s
p
ac
es
o
r
n
o
is
e
in
th
e
r
ec
o
r
d
s
.
A
q
u
esti
o
n
m
ar
k
is
d
is
p
lay
e
d
n
ex
t
to
ex
tr
an
eo
u
s
d
ata
i
n
th
e
r
ec
o
r
d
s
,
as
s
how
n
i
n
T
ab
le
1
.
T
h
is
co
u
ld
ca
u
s
e
is
s
u
es
wh
en
it
co
m
es
tim
e
to
m
ak
e
th
e
p
r
ed
ictio
n
.
Fo
r
t
h
is
r
ea
s
o
n
,
d
ata
clea
n
in
g
is
d
o
n
e
to
r
ed
u
ce
n
o
is
e.
Fig
u
r
e
1
.
KDD
m
eth
o
d
o
lo
g
y
T
ab
le
1
.
Data
b
ase
with
n
o
is
e
Y
e
a
r
I
C
D
1
0
C
o
d
e
I
C
D
1
0
d
i
a
g
n
o
s
i
s
D
i
a
g
n
o
si
s
M
e
t
r
i
c
S
e
x
2
0
/
1
4
/
1
5
I
7
0
A
t
h
e
r
o
sc
l
e
r
o
si
s
C
i
r
c
u
l
a
t
o
r
y
d
i
se
a
ses
w
h
i
c
h
c
a
n
b
e
c
a
u
sed
b
y
sm
o
k
i
n
g
A
t
t
r
i
b
u
t
a
b
l
e
n
u
m
b
e
r
M
a
l
e
2
0
/
1
4
/
1
5
K
2
5
-
K
2
7
S
t
o
m
a
c
h
/
D
u
o
d
e
n
a
l
U
l
c
e
r
D
i
g
e
s
t
i
v
e
d
i
se
a
ses
w
h
i
c
h
c
a
n
b
e
c
a
u
s
e
d
b
y
sm
o
k
i
n
g
A
t
t
r
i
b
u
t
a
b
l
e
n
u
m
b
e
r
M
a
l
e
2
0
/
1
4
/
1
5
K
5
0
C
r
o
h
n
’
s
d
i
se
a
se
D
i
g
e
s
t
i
v
e
d
i
se
a
ses
w
h
i
c
h
c
a
n
b
e
c
a
u
s
e
d
b
y
sm
o
k
i
n
g
A
t
t
r
i
b
u
t
a
b
l
e
n
u
m
b
e
r
M
a
l
e
2
0
/
1
4
/
1
5
K
0
5
P
e
r
i
o
d
o
n
t
a
l
d
i
s
e
a
se
/
P
e
r
i
o
d
o
n
t
i
t
i
s
D
i
g
e
s
t
i
v
e
d
i
se
a
ses
w
h
i
c
h
c
a
n
b
e
c
a
u
s
e
d
b
y
sm
o
k
i
n
g
A
t
t
r
i
b
u
t
a
b
l
e
n
u
m
b
e
r
M
a
l
e
2
0
/
1
4
/
1
5
H
2
5
A
g
e
r
e
l
a
t
e
d
C
a
t
a
r
a
c
t
4
5
+
O
t
h
e
r
d
i
se
a
ses
w
h
i
c
h
c
a
n
b
e
c
a
u
se
d
b
y
smo
k
i
n
g
A
t
t
r
i
b
u
t
a
b
l
e
n
u
m
b
e
r
M
a
l
e
2
0
/
1
4
/
1
5
S
7
2
.
0
-
S
7
2
.
2
H
i
p
F
r
a
c
t
u
r
e
5
5
+
O
t
h
e
r
d
i
se
a
ses
w
h
i
c
h
c
a
n
b
e
c
a
u
se
d
b
y
smo
k
i
n
g
A
t
t
r
i
b
u
t
a
b
l
e
n
u
m
b
e
r
M
a
l
e
2
0
/
1
4
/
1
5
O
.
O
3
S
p
o
n
t
a
n
e
o
u
s
A
b
o
r
t
i
o
n
O
t
h
e
r
d
i
se
a
ses
w
h
i
c
h
c
a
n
b
e
c
a
u
se
d
b
y
smo
k
i
n
g
A
t
t
r
i
b
u
t
a
b
l
e
n
u
m
b
e
r
M
a
l
e
On
e
o
f
th
e
m
ain
ad
v
an
tag
es
o
f
u
s
in
g
R
ap
id
Min
er
Stu
d
io
i
s
its
in
tu
itiv
e
an
d
ea
s
y
-
to
-
u
s
e
in
ter
f
ac
e,
wh
ich
allo
ws
u
s
er
s
to
p
er
f
o
r
m
co
m
p
lex
d
ata
an
al
y
s
is
with
o
u
t
p
r
io
r
p
r
o
g
r
am
m
in
g
ex
p
er
ien
ce
.
Fig
u
r
e
2
s
h
o
ws
an
ex
am
p
le
o
f
t
h
is
d
ata
s
elec
ti
o
n
p
r
o
ce
s
s
,
wh
er
e
d
if
f
er
en
t
co
m
p
o
n
e
n
ts
ar
e
u
s
ed
to
p
r
o
ce
s
s
th
e
d
ata
an
d
s
elec
t
th
e
r
elev
an
t
f
ield
s
f
o
r
an
aly
s
i
s
.
T
h
e
f
ir
s
t
s
tag
e
in
t
h
e
d
ata
s
elec
tio
n
p
r
o
ce
s
s
u
s
u
ally
i
n
v
o
l
v
es
im
p
o
r
tin
g
d
ata
s
ets
f
r
o
m
v
ar
io
u
s
s
o
u
r
ce
s
,
s
u
c
h
as
lo
ca
l
f
iles
,
d
atab
ases
o
r
ev
en
clo
u
d
s
er
v
ices.
On
ce
th
e
d
ata
is
lo
ad
ed
in
t
o
R
ap
id
Min
er
Stu
d
io
,
u
s
er
s
ca
n
u
s
e
a
v
ar
iety
o
f
co
m
p
o
n
e
n
ts
to
p
r
ep
r
o
ce
s
s
th
e
d
ata,
s
u
ch
as
d
ata
clea
n
s
in
g
,
n
o
r
m
aliza
tio
n
a
n
d
v
a
r
iab
le
tr
a
n
s
f
o
r
m
atio
n
.
a.
R
etr
iev
e
ad
m
is
s
io
n
s
T
h
is
o
p
er
ato
r
is
u
s
ed
to
lo
ad
d
ata
in
to
R
ap
id
Min
er
Stu
d
io
.
I
t
is
cr
u
cial
th
at
th
er
e
is
n
o
i
m
p
o
r
t
lim
it
o
n
th
e
n
u
m
b
e
r
o
f
r
ec
o
r
d
s
.
T
h
i
s
o
p
er
ato
r
en
s
u
r
es th
at
all
d
ata
is
p
r
o
ce
s
s
ed
wi
th
o
u
t r
estrictio
n
s
.
I
n
th
is
way
,
th
e
ad
eq
u
ate
n
u
m
b
er
o
f
r
ec
o
r
d
s
is
g
u
ar
an
tee
d
.
b.
Fil
ter
ex
am
p
les
E
n
ab
les
th
e
s
elec
tio
n
o
f
th
e
m
o
s
t
p
er
tin
en
t
d
ata
f
o
r
th
e
a
n
aly
s
is
.
I
n
o
th
er
wo
r
d
s
,
ch
o
o
s
e
th
e
m
o
s
t
cr
u
cial
f
ield
s
to
p
r
ep
ar
e
it
f
o
r
an
aly
s
is
.
T
h
is
en
s
u
r
es
th
at
th
e
d
ata
is
r
ea
d
y
f
o
r
ac
c
u
r
ate
an
d
ef
f
icien
t
p
r
o
ce
s
s
in
g
.
I
n
th
is
way
th
e
f
ilt
er
is
m
ad
e
to
wo
r
k
with
th
e
r
e
q
u
ir
ed
f
ield
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.
14
,
No
.
6
,
Dec
em
b
e
r
20
24
:
6
8
4
6
-
6
8
6
0
6850
c.
Select
attr
ib
u
tes
T
h
is
o
p
er
ato
r
e
x
tr
ac
ts
u
s
eless
d
ata
b
y
g
e
n
er
atin
g
n
ew
c
h
ar
a
cter
is
tics
f
r
o
m
th
e
ex
is
tin
g
d
ata.
Fo
r
t
h
is
r
ea
s
o
n
,
ce
r
tain
s
u
b
s
ets
o
f
attr
ib
u
tes
o
r
ch
a
r
ac
ter
is
tics
f
r
o
m
a
co
llectio
n
o
f
d
ata
ar
e
c
h
o
s
en
f
o
r
f
u
r
th
er
a
n
aly
s
is
.
T
h
is
p
r
o
ce
s
s
en
h
an
ce
s
th
e
q
u
a
lity
o
f
d
ata
u
s
ed
in
th
e
a
n
aly
s
is
.
d.
Dete
ct
o
u
tlier
Dep
e
n
d
in
g
o
n
t
h
e
d
is
t
an
ce
b
etwe
e
n
h
is
cl
o
s
es
t
n
e
ig
h
b
o
r
s
.
T
h
is
o
p
e
r
a
to
r
d
is
c
o
v
e
r
s
e
x
ce
p
ti
o
n
al
v
al
u
es i
n
t
h
e
c
o
ll
ec
ti
o
n
o
f
e
x
a
m
p
les
p
r
o
v
i
d
e
d
.
T
h
e
p
a
r
a
m
e
te
r
s
ca
n
b
e
u
s
e
d
t
o
d
e
te
r
m
in
e
th
e
v
ar
ia
b
l
es
a
n
d
.
Fig
u
r
e
2
.
Data
s
elec
tio
n
p
r
o
ce
s
s
3
.
2
.
2
.
Da
t
a
prepro
ce
s
s
ing
Mo
s
t
f
r
eq
u
en
tly
,
d
u
r
in
g
th
is
p
r
o
ce
s
s
,
th
e
d
ata
im
p
o
r
ted
f
o
r
a
n
aly
s
is
co
n
tain
s
n
eg
ativ
e
ch
ar
ac
ter
is
tics
,
in
clu
d
in
g
m
is
s
in
g
d
ata,
ex
ce
s
s
ch
ar
ac
ter
s
,
an
d
em
p
ty
f
ield
s
.
As
a
r
esu
lt,
d
ata
clea
n
u
p
is
d
o
n
e
in
th
is
p
h
ase
,
f
ix
in
g
an
y
er
r
o
r
s
th
at
wer
e
d
is
co
v
er
ed
.
Ad
d
itio
n
ally
,
p
er
tin
en
t
ch
ar
ac
ter
is
tics
an
d
v
ar
iab
l
es
ar
e
o
b
tain
ed
t
o
m
ak
e
it e
asier
to
m
in
e
th
e
d
ata
f
o
r
th
e
an
aly
s
is
.
a.
Dete
ctio
n
o
f
m
is
s
in
g
v
alu
es
R
ep
r
esen
tin
g
th
e
v
alu
es
t
h
at
a
r
e
m
is
s
in
g
f
r
o
m
a
co
lu
m
n
s
o
r
p
r
o
p
o
s
ed
attr
ib
u
tes.
T
h
e
v
alu
e
s
th
at
ar
e
lack
in
g
ar
e
f
illed
in
as
a
r
esu
lt
.
On
e
tech
n
i
q
u
e
f
o
r
c
ar
r
y
i
n
g
o
u
t
th
is
task
is
to
r
ep
lace
v
alu
e
s
with
th
e
m
ed
ian
,
av
er
ag
e,
o
r
p
r
ed
eter
m
in
e
d
v
al
u
e,
as
s
h
o
wn
in
(
1
)
.
%
.
=
.
.
.
.
100
%
(
1
)
is
r
ep
r
esen
ts
m
is
s
in
g
v
alu
es (
d
ata)
,
.
is
r
ep
r
esen
ts
th
e
n
u
m
b
e
r
o
f
m
is
s
in
g
n
u
m
b
er
s
in
t
h
e
m
o
d
el
,
.
is
r
ep
r
esen
ts
th
e
n
u
m
b
er
o
f
r
ec
o
r
d
s
in
th
e
d
atab
ase.
b.
D
etec
tio
n
o
f
m
is
s
in
g
v
alu
es
I
n
ter
q
u
a
r
tile
r
an
g
e
(
I
QR
)
as
s
h
o
wn
in
(
2
)
.
T
h
e
th
i
r
d
q
u
ar
til
e
o
f
th
is
f
o
r
m
u
la
is
Q3
,
wh
il
e
th
e
f
ir
s
t
q
u
ar
tile
is
Q1
.
Valu
es
b
elo
w
Q1
-
1
.
5
x
I
QR
o
r
ab
o
v
e
Q
3
+1
.
5
x
I
QR
ar
e
co
n
s
id
er
ed
e
x
ce
p
tio
n
s
an
d
m
ay
b
e
elim
in
ated
o
r
tr
ea
ted
in
a
s
p
ec
if
ic
m
an
n
er
.
=
3
−
1
(
2
)
c.
Fo
r
th
e
s
tan
d
ar
d
izatio
n
o
f
d
ata
Min
-
m
ax
s
ca
lin
g
:
Dete
r
m
in
e
t
h
e
n
u
m
er
ical
attr
ib
u
te
s
ca
lin
g
in
a
ce
r
tain
r
an
g
e;
a
clea
r
e
x
a
m
p
le
co
u
ld
b
e
0
an
d
1
,
as
s
ee
n
in
(
3
)
.
T
h
is
h
elp
s
in
n
o
r
m
alizin
g
th
e
d
at
a
f
o
r
b
etter
an
aly
s
is
.
I
n
t
h
is
s
en
s
e,
th
e
d
ata
ca
n
b
e
n
o
r
m
alize
d
c
o
r
r
ec
tly
.
=
−
−
(
3
)
d.
Z
-
Sco
r
e
s
tan
d
ar
d
izatio
n
T
o
s
tan
d
ar
d
ize
n
u
m
e
r
ical
attr
ib
u
tes
with
a
m
ea
n
o
f
0
an
d
a
s
tan
d
ar
d
d
ev
iatio
n
o
f
1
,
as
s
how
n
in
(
4
)
.
W
h
er
e
is
th
e
m
ea
n
an
d
is
th
e
s
tan
d
ar
d
d
ev
iatio
n
.
I
n
th
at
s
en
s
e,
th
e
o
p
er
atio
n
is
p
er
f
o
r
m
e
d
as v
is
u
alize
d
in
(
4
)
.
C
o
n
s
eq
u
en
tly
,
Fig
u
r
e
3
s
h
o
ws
a
g
r
ap
h
as
f
ar
as
d
ata
p
r
o
ce
s
s
in
g
is
co
n
ce
r
n
ed
.
T
h
at
is
,
in
th
is
p
r
o
ce
s
s
,
we
p
er
f
o
r
m
d
ata
clea
n
in
g
,
n
o
r
m
al
izatio
n
,
d
ata
d
u
p
licatio
n
,
a
n
d
o
th
er
f
u
n
ctio
n
s
.
=
−
(
4
)
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
A
s
tu
d
y
o
f To
b
a
c
co
u
s
e
a
n
d
m
o
r
ta
lity b
y
d
a
ta
min
in
g
(
La
b
eria
n
o
A
n
d
r
a
d
e
-
A
r
en
a
s
)
6851
Fig
u
r
e
3
.
Data
p
r
o
ce
s
s
in
g
3
.
2
.
3
.
Da
t
a
t
ra
ns
f
o
r
m
a
t
io
n
At
th
is
p
o
in
t,
th
e
d
ata
p
r
e
v
io
u
s
ly
p
r
o
ce
s
s
ed
in
th
e
p
r
e
v
io
u
s
s
tep
s
is
tr
an
s
f
o
r
m
ed
in
to
a
m
o
r
e
ac
cu
r
ate
r
ep
r
esen
tatio
n
o
f
th
e
d
esire
d
in
v
esti
g
atio
n
ac
co
r
d
in
g
to
th
e
o
b
jectiv
es
estab
lis
h
ed
in
th
e
r
esear
ch
.
T
h
i
s
in
clu
d
es
ad
d
in
g
n
ew
f
ea
tu
r
es
to
th
e
estab
lis
h
ed
m
o
d
el
t
o
d
e
cr
ea
s
e
th
e
d
im
en
s
io
n
o
f
th
e
d
ata.
I
n
th
is
co
n
te
x
t,
th
e
co
n
ce
p
ts
o
f
th
e
d
ata
tr
an
s
f
o
r
m
atio
n
s
tep
will
b
e
ap
p
lied
an
d
e
x
p
lain
ed
in
d
etail
b
y
m
at
h
em
atica
l
f
o
r
m
u
las
in
ea
ch
p
r
o
ce
s
s
.
a.
No
r
m
aliza
tio
n
T
h
e
m
in
im
u
m
an
d
m
ax
im
u
m
n
o
r
m
aliza
tio
n
f
o
r
m
u
la
to
a
d
ju
s
t
th
e
v
alu
es
o
f
a
v
ar
iab
le
X
to
a
s
p
ec
if
i
c
r
an
g
e,
f
o
r
e
x
am
p
le,
f
r
o
m
0
t
o
1
as
s
p
ec
if
ied
in
(
5
)
.
T
h
is
tech
n
iq
u
e
e
n
s
u
r
es
th
at
all
v
al
u
es
f
all
with
in
th
e
d
esire
d
r
an
g
e
.
I
n
th
is
way
n
o
r
m
aliza
tio
n
ca
n
b
e
p
er
f
o
r
m
ed
o
p
tim
ally
.
=
−
(
)
m
ax
(
)
−
m
i
n
(
)
(
5
)
T
h
e
Z
-
s
co
r
e,
also
k
n
o
wn
as
a
s
tan
d
ar
d
s
co
r
e,
is
a
s
tatis
t
ica
l
m
ea
s
u
r
e
th
at
m
ea
s
u
r
es
th
e
n
u
m
b
er
o
f
s
tan
d
ar
d
d
ev
iatio
n
s
at
a
s
p
ec
if
ic
d
ata
p
o
in
t th
at
ar
e
ab
o
v
e
o
r
b
elo
w
th
e
m
ea
n
o
f
a
d
ata
s
et.
I
t is a
u
s
ef
u
l to
o
l in
s
tatis
t
ics
an
d
d
ata
an
aly
s
i
s
t
o
ass
es
s
th
e
r
elativ
e
p
o
s
it
io
n
o
f
a
d
ata
p
o
in
t
in
a
d
is
tr
ib
u
tio
n
.
T
h
e
Z
-
s
co
r
e
n
o
r
m
aliza
tio
n
f
o
r
m
u
la
f
o
r
s
tan
d
ar
d
izin
g
o
n
th
e
v
ar
iab
le
X
as
v
is
u
alize
d
in
(
6
)
.
=
−
(
)
s
t
d
(
)
(
6
)
b.
I
m
p
u
tatio
n
o
f
m
is
s
in
g
d
ata
−
I
m
p
u
tatio
n
b
ased
o
n
m
ea
n
Mean
-
b
ased
im
p
u
tatio
n
is
a
m
is
s
in
g
d
ata
h
an
d
lin
g
tech
n
iq
u
e
th
at
in
clu
d
es
r
ep
lacin
g
m
is
s
in
g
v
alu
es
with
th
e
ass
o
ciate
d
v
ar
iab
le'
s
ar
ith
m
etic
m
ea
n
.
W
h
en
d
ea
lin
g
with
m
is
s
in
g
d
ata
in
a
d
ata
co
llectio
n
.
T
h
i
s
s
tr
ateg
y
is
wid
ely
em
p
lo
y
e
d
to
m
ain
tain
s
tatis
tical
co
n
s
is
ten
c
y
,
as d
escr
ib
ed
in
(
7
)
.
=
(
)
(
7
)
−
Me
d
ian
-
b
ased
im
p
u
tatio
n
Me
d
ian
-
b
ased
im
p
u
tatio
n
is
a
m
is
s
in
g
d
ata
m
a
n
ag
em
e
n
t
s
tr
ateg
y
th
at
r
ep
lace
s
m
is
s
in
g
v
alu
es
with
th
e
v
ar
iab
le'
s
m
ed
ian
.
T
h
is
s
tr
ateg
y
,
lik
e
m
e
d
ian
-
b
ased
im
p
u
tatio
n
,
is
em
p
lo
y
ed
wh
en
th
er
e
is
m
is
s
in
g
d
ata
an
d
aim
s
to
m
ain
tain
s
tatis
tic
al
co
n
s
is
ten
cy
in
th
e
d
ata
s
et,
as
s
h
o
wn
in
(
8
)
.
T
h
is
a
p
p
r
o
ac
h
h
elp
s
t
o
p
r
eser
v
e
th
e
ce
n
tr
al
ten
d
e
n
cy
o
f
th
e
d
ata.
=
(
)
(
8)
c.
Dim
en
s
io
n
ality
r
ed
u
ctio
n
−
Fo
r
m
u
la
f
o
r
p
r
in
cip
al
co
m
p
o
n
en
ts
an
aly
s
is
(
PC
A)
As
in
d
icate
d
in
f
o
r
m
(
9
)
,
it
is
a
s
tatis
tical
tech
n
iq
u
e
u
s
ed
to
tu
r
n
a
s
et
o
f
co
r
r
elate
d
v
ar
ia
b
les
in
to
a
s
et
o
f
u
n
co
r
r
elate
d
v
ar
iab
les
k
n
o
wn
as
p
r
in
cip
al
co
m
p
o
n
en
ts
.
T
h
is
m
eth
o
d
s
im
p
lifie
s
th
e
d
ata
b
y
r
ed
u
ci
n
g
its
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.
14
,
No
.
6
,
Dec
em
b
e
r
20
24
:
6
8
4
6
-
6
8
6
0
6852
d
im
en
s
io
n
ality
wh
ile
r
etain
in
g
m
o
s
t
o
f
its
o
r
ig
in
al
in
f
o
r
m
a
tio
n
.
Prin
cip
al
co
m
p
o
n
en
t
an
a
ly
s
is
i
s
wid
ely
u
s
ed
in
v
ar
io
u
s
f
ield
s
s
u
ch
as d
ata
c
o
m
p
r
ess
io
n
,
f
ea
t
u
r
e
ex
tr
ac
tio
n
,
an
d
d
ata
v
is
u
aliza
tio
n
.
=
×
(
9
)
T
h
e
o
p
er
ato
r
s
u
tili
ze
d
in
th
e
d
ata
tr
an
s
f
o
r
m
atio
n
p
r
o
ce
s
s
ar
e
d
ep
icted
in
Fig
u
r
e
4
.
T
h
is
p
r
ep
ar
es
th
e
d
ata
f
o
r
th
e
f
o
llo
win
g
p
r
o
ce
d
u
r
e,
wh
ic
h
co
n
s
o
lid
ates
th
e
m
o
d
el
p
r
es
en
ted
in
th
e
o
b
jectiv
es.
T
h
ese
s
tep
s
ar
e
cr
u
cial
f
o
r
en
s
u
r
in
g
ac
c
u
r
ate
an
d
m
ea
n
in
g
f
u
l r
esu
lts
in
th
e
a
n
aly
s
is
.
Fig
u
r
e
4
.
Data
tr
a
n
s
f
o
r
m
atio
n
p
r
o
ce
s
s
3
.
2
.
4
.
Da
t
a
m
ini
ng
T
h
e
r
ep
r
esen
tatio
n
o
f
th
e
d
ata
m
in
er
is
th
e
p
h
ase
o
f
th
e
KD
D
m
eth
o
d
o
l
o
g
y
th
at
is
m
o
s
t
i
m
p
o
r
tan
t
i
n
th
e
o
v
er
all
p
r
o
ce
s
s
.
T
h
is
p
r
o
ce
s
s
estab
li
s
h
es
s
o
m
e
cr
iter
ia,
s
u
ch
as
th
e
id
en
tific
atio
n
o
f
s
ig
n
if
ican
t
p
atr
o
n
s
th
at
will
p
r
o
v
id
e
r
ep
r
esen
tati
o
n
s
d
ep
en
d
in
g
o
n
th
e
ty
p
es
o
f
m
o
d
els.
As
a
r
esu
lt,
ch
o
o
s
in
g
a
d
ata
m
in
in
g
alg
o
r
ith
m
also
in
v
o
lv
es
c
h
o
o
s
in
g
th
e
m
eth
o
d
s
to
f
i
n
d
p
atter
n
s
in
th
e
d
ata
as
well
as
th
e
b
est
m
o
d
els
an
d
p
ar
am
eter
s
d
ep
e
n
d
in
g
o
n
t
h
e
k
in
d
o
f
d
ata
(
ca
teg
o
r
ical
o
r
n
u
m
er
ical)
.
a.
Ap
r
io
r
i a
lg
o
r
ith
m
Fo
r
th
e
p
r
o
ce
s
s
in
g
o
f
co
m
b
in
atio
n
s
o
f
f
r
eq
u
e
n
tly
o
cc
u
r
r
in
g
elem
en
ts
,
p
r
io
r
k
n
o
wled
g
e
a
lg
o
r
ith
m
s
ar
e
u
s
ed
.
As
a
r
esu
lt,
c
r
ea
te
a
co
llectio
n
o
f
d
ata
k
n
o
wn
as
an
item
,
f
o
r
wh
ich
a
d
atab
ase
is
u
s
ed
to
p
r
o
v
id
e
s
u
p
p
o
r
t.
E
n
ab
les
th
e
ef
f
ec
tiv
e
d
is
co
v
er
y
o
f
a
s
et
o
f
f
r
e
q
u
en
tly
o
cc
u
r
r
in
g
s
ets
o
f
ter
m
s
th
at
s
er
v
e
as
th
e
f
o
u
n
d
atio
n
f
o
r
ass
o
ciativ
e
r
u
le
s
[
3
2
]
.
−
I
tem
s
et
I
tem
s
ets
p
lay
a
f
u
n
d
am
en
tal
r
o
le
in
d
ata
m
in
in
g
an
d
ass
o
ciatio
n
r
u
le
lear
n
in
g
,
p
ar
ticu
l
ar
ly
in
th
e
co
n
tex
t
o
f
tr
an
s
ac
tio
n
al
d
ata
b
ases
.
I
n
s
u
ch
d
atab
ases
,
ea
ch
tr
an
s
ac
tio
n
ty
p
ically
r
ep
r
es
en
ts
a
s
et
o
f
item
s
p
u
r
ch
ased
o
r
o
b
s
er
v
e
d
to
g
eth
er
.
An
item
s
et
is
e
s
s
en
tially
a
co
llectio
n
o
f
o
n
e
o
r
m
o
r
e
ite
m
s
th
at
f
r
eq
u
en
tly
o
cc
u
r
to
g
eth
er
with
in
t
h
ese
tr
an
s
ac
tio
n
s
.
−
Su
p
p
o
r
t
T
h
e
co
n
ce
p
t
o
f
s
u
p
p
o
r
t
is
ce
n
tr
al
to
f
r
eq
u
en
t
item
s
et
m
in
in
g
an
d
ass
o
ciatio
n
r
u
le
lear
n
in
g
.
I
t
q
u
an
tifie
s
th
e
f
r
eq
u
en
cy
with
wh
ich
a
p
a
r
ticu
lar
item
s
et
ap
p
ea
r
s
in
a
d
atab
ase
r
elativ
e
to
t
h
e
to
tal
n
u
m
b
er
o
f
tr
an
s
ac
tio
n
s
in
th
e
d
atab
ase.
I
n
o
th
er
wo
r
d
s
,
s
u
p
p
o
r
t
m
ea
s
u
r
es
th
e
p
r
o
p
o
r
tio
n
o
f
tr
an
s
ac
tio
n
s
th
at
co
n
tain
a
s
p
ec
if
ic
co
m
b
in
atio
n
o
f
item
s
.
−
Fre
q
u
en
t
item
s
et
I
n
ass
o
ciatio
n
r
u
le
m
in
in
g
,
a
f
r
eq
u
en
t
item
s
et
is
a
s
et
o
f
item
s
(
o
r
item
s
)
th
at
o
cc
u
r
to
g
eth
er
f
r
eq
u
e
n
tly
in
a
d
ataset,
s
u
r
p
ass
in
g
a
p
r
ed
ef
in
e
d
th
r
esh
o
l
d
k
n
o
w
n
as
th
e
s
u
p
p
o
r
t
th
r
e
s
h
o
ld
o
r
m
i
n
im
u
m
s
u
p
p
o
r
t.
T
h
e
s
u
p
p
o
r
t
o
f
an
ite
m
s
et
is
th
e
p
r
o
p
o
r
tio
n
o
f
tr
a
n
s
ac
tio
n
s
in
th
e
d
ataset
th
at
co
n
tain
th
at
p
ar
ticu
lar
item
s
et.
T
h
e
s
u
p
p
o
r
t
th
r
esh
o
l
d
,
o
r
m
in
im
u
m
s
u
p
p
o
r
t,
ac
ts
as
a
cr
iter
io
n
f
o
r
d
eter
m
in
i
n
g
wh
ich
item
s
et
s
ar
e
co
n
s
id
er
ed
f
r
eq
u
e
n
t.
I
tem
s
et
s
with
a
s
u
p
p
o
r
t
v
alu
e
eq
u
al
t
o
o
r
h
ig
h
er
t
h
an
th
e
s
u
p
p
o
r
t
th
r
esh
o
ld
is
d
ee
m
e
d
f
r
eq
u
e
n
t,
wh
ile
th
o
s
e
with
a
s
u
p
p
o
r
t
v
alu
e
b
elo
w
t
h
e
th
r
esh
o
ld
ar
e
co
n
s
id
er
e
d
in
f
r
e
q
u
en
t
a
n
d
ty
p
ically
d
is
ca
r
d
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:
2088
-
8
7
0
8
A
s
tu
d
y
o
f To
b
a
c
co
u
s
e
a
n
d
m
o
r
ta
lity b
y
d
a
ta
min
in
g
(
La
b
eria
n
o
A
n
d
r
a
d
e
-
A
r
en
a
s
)
6853
−
R
u
les o
f
ass
o
ciatio
n
T
h
e
u
s
e
o
f
ass
o
ciatio
n
r
u
les
in
d
ata
m
in
in
g
m
ak
es
it
p
o
s
s
ib
le
to
an
aly
ze
d
atab
ases
in
s
ea
r
ch
o
f
p
atter
n
s
o
r
c
o
-
o
cc
u
r
r
en
ce
s
.
A
clea
r
ex
am
p
le
is
th
e
d
is
cu
s
s
io
n
o
f
o
n
e
ar
ticle
in
r
elatio
n
to
an
o
th
er
.
Giv
en
th
e
p
o
s
s
ib
ilit
y
th
at
o
n
e
item
is
ch
o
s
en
,
g
iv
en
th
at
a
n
o
th
er
item
h
as
alr
ea
d
y
b
ee
n
c
h
o
s
en
.
I
t
is
p
o
s
s
ib
le
to
s
tate
th
at
a
cu
s
to
m
er
is
lik
ely
to
b
u
y
b
o
t
h
item
s
if
h
e/sh
e
b
u
y
s
item
A,
as v
is
u
alize
d
in
(
1
0
)
.
{
í
,
í
}
→
{
í
}
(
1
0
)
b.
Su
p
p
o
r
t a
n
d
tr
u
s
t
I
n
o
th
er
wo
r
d
s
,
s
u
p
p
o
r
t
an
d
c
o
n
f
id
en
ce
a
r
e
two
k
ey
p
ar
am
e
ter
s
in
r
u
le
-
o
r
ien
ted
al
g
o
r
ith
m
s
.
L
et
b
e
a
lis
t.
As
a
r
esu
lt,
th
e
s
u
p
p
o
r
t
is
to
p
er
f
o
r
m
t
h
e
f
o
llo
win
g
t
r
an
s
co
u
n
tab
le
f
r
ac
tu
r
e
as
m
en
tio
n
ed
in
(
1
1
)
.
T
h
e
n
u
m
b
er
o
f
tr
a
n
s
ac
tio
n
s
in
a
d
atab
ase
is
r
ep
r
esen
ted
b
y
th
e
l
etter
.
I
n
th
e
e
q
u
atio
n
,
(
)
r
ef
er
s
to
th
e
to
tal
n
u
m
b
er
o
f
tr
an
s
ac
tio
n
s
in
a
d
atab
ase,
with
s
er
v
in
g
as
th
e
an
tece
d
en
t
an
d
Y
s
er
v
in
g
as
th
e
r
esu
lt.
As
s
how
n
in
(
1
2
)
,
a
f
r
ac
tio
n
o
f
t
r
an
s
ac
tio
n
s
m
u
s
t
o
cc
u
r
in
wh
ic
h
t
h
e
item
s
ets
ap
p
ea
r
in
o
r
d
er
f
o
r
a
r
u
le
to
b
e
co
n
s
id
er
ed
r
eliab
le.
I
t
co
u
ld
b
e
s
ee
n
as
th
e
f
r
eq
u
en
c
y
with
wh
ich
a
tr
an
s
ac
tio
n
in
v
o
lv
in
g
item
also
in
clu
d
es
item
.
(
⟹
)
=
(
∪
)
=
(
∪
)
(
1
1
)
(
⇒
)
=
(
∪
)
(
)
(
1
2
)
c.
L
if
t p
r
o
p
o
s
itio
n
T
h
e
lift
is
ca
r
r
ied
o
u
t
to
ac
q
u
ir
e
th
e
o
b
s
er
v
ed
f
r
e
q
u
en
cy
th
r
o
u
g
h
th
e
ap
p
licatio
n
o
f
a
r
u
le
th
at
is
ca
r
r
ied
o
u
t
with
th
e
an
ticip
ated
f
r
eq
u
e
n
cy
b
y
ch
an
ce
(
if
th
e
r
u
le
is
n
o
t
ac
tu
ally
th
er
e)
,
as
s
ee
n
in
(
1
3
)
.
On
th
e
o
th
er
h
an
d
,
th
e
lift
h
el
p
s
u
s
id
en
tify
in
s
tan
ce
s
o
f
in
cr
ea
s
ed
s
ales
p
r
o
b
ab
ilit
y
,
f
o
r
in
s
tan
ce
,
in
ac
co
r
d
a
n
ce
with
th
e
r
u
les
f
o
llo
wed
.
A
g
r
ap
h
illu
s
tr
atin
g
th
e
u
s
e
o
f
ass
o
ciatio
n
r
u
les
wh
ile
im
p
lem
en
tin
g
th
e
p
r
ev
i
o
u
s
alg
o
r
ith
m
is
s
h
o
wn
in
Fig
u
r
e
5
.
I
n
th
is
s
en
s
e,
all
th
e
c
o
n
ce
p
ts
m
en
tio
n
ed
i
n
th
e
ea
r
lier
-
estab
lis
h
ed
d
ef
in
itio
n
,
s
u
ch
as th
e
ca
lcu
latio
n
o
f
s
u
p
p
o
r
t,
co
n
f
id
en
ce
,
an
d
lift,
a
r
e
ap
p
licab
le.
=
(
⟶
)
(
)
(
1
3
)
Fig
u
r
e
5
.
Ap
p
licatio
n
o
f
th
e
a
p
r
io
r
i a
lg
o
r
ith
m
d.
Nu
m
er
ical
to
b
in
o
m
ial
T
h
is
o
p
er
ato
r
en
a
b
les
u
s
to
s
to
r
e
B
o
o
lean
v
ar
iab
les
(
tr
u
e
o
r
f
alse)
.
R
ea
lize
th
e
tr
an
s
f
o
r
m
atio
n
o
f
ch
ar
ac
ter
is
tics
o
r
attr
ib
u
tes
th
at
o
r
ig
in
ally
i
n
clu
d
ed
n
u
m
er
ic
v
alu
es
in
to
a
b
in
ar
y
o
r
b
in
o
m
in
al
v
ar
iab
le.
T
h
is
tr
an
s
f
o
r
m
atio
n
s
im
p
lifie
s
th
e
d
ata
r
ep
r
esen
tatio
n
f
o
r
ce
r
tain
an
aly
s
es.
−
FP
-
g
r
o
wth
W
e
ar
e
ab
le
to
f
ilter
u
s
in
g
t
h
e
m
in
im
al
am
o
u
n
t
o
f
s
u
p
p
o
r
t
r
eq
u
ir
e
d
an
d
lo
ca
te
f
r
e
q
u
en
tly
u
s
ed
item
s
ets.
T
h
e
f
r
eq
u
en
tly
u
s
ed
item
s
ets
ar
e
f
ilter
ed
with
a
m
in
im
u
m
lev
el
o
f
s
u
p
p
o
r
t.
T
h
is
f
ilter
in
g
p
r
o
ce
s
s
h
elp
s
in
id
en
tify
i
n
g
m
ea
n
in
g
f
u
l p
atter
n
s
in
th
e
d
ata.
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.
14
,
No
.
6
,
Dec
em
b
e
r
20
24
:
6
8
4
6
-
6
8
6
0
6854
−
Mu
ltip
ly
T
o
d
u
p
licate
an
o
p
er
at
o
r
o
u
tp
u
t.
I
m
ag
in
e
th
e
o
f
ten
-
o
c
cu
r
r
in
g
item
s
ets
p
er
f
o
r
m
in
g
o
p
er
atio
n
s
in
v
o
lv
in
g
th
e
m
u
ltip
licatio
n
o
f
n
u
m
er
ical
attr
ib
u
tes
with
th
e
h
elp
o
f
th
is
o
p
e
r
ato
r
.
T
h
is
d
u
p
licatio
n
p
r
o
ce
s
s
aid
s
in
cr
ea
tin
g
m
u
ltip
le
in
s
tan
ce
s
o
f
th
e
s
am
e
o
u
tp
u
t
f
o
r
f
u
r
th
er
an
aly
s
is
.
−
C
r
ea
te
ass
o
ciatio
n
r
u
les
Per
m
its
s
ee
in
g
th
e
f
r
eq
u
en
t
item
s
ets
'
a
s
s
o
ciatio
n
r
u
les.
s
ep
ar
atin
g
r
elatio
n
s
h
ip
s
u
s
in
g
a
m
in
im
al
lev
el
o
f
tr
u
s
t.
T
h
is
h
elp
s
in
id
en
tify
in
g
m
ea
n
i
n
g
f
u
l
ass
o
ciati
o
n
s
with
h
ig
h
er
co
n
f
i
d
en
ce
le
v
els.
I
n
th
is
way
,
it
ca
n
b
e
d
o
n
e
m
o
r
e
ef
f
icien
tly
.
4.
RE
SU
L
T
S
4
.
1
.
E
v
a
lua
t
io
n o
f
re
s
ults
Af
ter
co
m
p
letin
g
th
e
p
r
o
ce
s
s
o
f
ev
alu
atin
g
th
e
alg
o
r
ith
m
'
s
a
p
p
licatio
n
b
ef
o
r
eh
a
n
d
an
d
im
p
lem
en
tin
g
th
e
p
r
o
ce
d
u
r
es
r
elate
d
t
o
th
is
alg
o
r
ith
m
u
s
in
g
th
e
to
o
l
R
ap
id
Min
er
Stu
d
io
.
T
h
e
p
r
o
ce
s
s
o
f
p
r
o
d
u
cin
g
th
e
p
r
o
ject'
s
r
esu
lt
s
will
b
e
in
itia
ted
.
T
h
e
estab
lis
h
ed
item
s
in
ea
ch
f
ield
ar
e
g
r
o
u
p
ed
to
g
e
th
er
in
to
item
s
ets,
allo
win
g
th
e
f
u
s
io
n
o
f
th
e
an
tece
d
en
t
v
ar
ia
b
les
an
d
th
e
co
n
s
eq
u
en
t
v
ar
iab
les.
Fo
r
th
is
r
ea
s
o
n
,
th
e
s
u
p
p
o
r
t
cr
iter
ia
lis
ted
in
T
ab
le
2
f
o
r
ea
ch
item
ar
e
ap
p
lied
.
T
ab
le
2
.
Data
o
n
item
s
an
d
th
e
ir
s
u
p
p
o
r
t
S
i
z
e
S
u
p
p
o
r
t
I
t
e
m 1
I
t
e
m 2
I
t
e
m 3
1
0
.
6
6
7
M
a
l
e
1
0
.
5
0
8
N
u
mb
e
r
o
f
a
d
mi
ss
i
o
n
s
C
a
n
c
e
r
w
h
i
c
h
c
a
n
b
e
c
a
u
se
d
b
y
smo
k
i
n
g
1
0
.
4
9
2
A
t
t
r
i
b
u
t
e
n
u
m
b
e
r
F
e
mal
e
1
0
.
3
4
9
C
a
n
c
e
r
w
h
i
c
h
c
a
n
b
e
c
a
u
se
d
b
y
sm
o
k
i
n
g
1
0
.
3
3
3
C
a
n
c
e
r
w
h
i
c
h
c
a
n
b
e
c
a
u
se
d
b
y
sm
o
k
i
n
g
F
e
mal
e
1
0
.
1
9
0
C
i
r
c
u
l
a
t
o
r
y
d
i
se
a
ses
w
h
i
c
h
c
a
n
b
e
c
a
u
sed
b
y
sm
o
k
i
n
g
2
0
.
3
3
9
F
e
mal
e
2
0
.
3
2
8
M
a
l
e
2
0
2
3
3
M
a
l
e
N
u
mb
e
r
o
f
a
d
mi
ss
i
o
n
s
2
0
.
1
2
7
M
a
l
e
A
t
t
r
i
b
u
t
e
n
u
m
b
e
r
2
0
.
1
7
5
M
a
l
e
C
a
n
c
e
r
w
h
i
c
h
c
a
n
b
e
c
a
u
se
d
b
y
smo
k
i
n
g
2
0
.
1
6
9
M
a
l
e
C
i
r
c
u
l
a
t
o
r
y
d
i
se
a
ses
w
h
i
c
h
c
a
n
b
e
c
a
u
se
d
b
y
sm
o
k
i
n
g
2
0
.
1
7
5
M
a
l
e
N
u
mb
e
r
o
f
a
d
mi
ss
i
o
n
s
C
a
n
c
e
r
s
t
h
a
t
c
a
n
b
e
c
a
u
se
d
b
y
smo
k
i
n
g
2
0
.
1
6
4
M
a
l
e
A
t
t
r
i
b
u
t
e
n
u
m
b
e
r
C
a
n
c
e
r
s
t
h
a
t
c
a
n
b
e
c
a
u
se
d
b
y
smo
k
i
n
g
2
0
.
1
1
6
N
u
mb
e
r
o
f
a
d
mi
ss
i
o
n
s
3
0
.
1
1
6
N
u
mb
e
r
o
f
a
d
mi
ss
i
o
n
s
C
a
n
c
e
r
s
t
h
a
t
c
a
n
b
e
c
a
u
se
d
b
y
sm
o
k
i
n
g
3
0
.
1
1
6
N
u
mb
e
r
o
f
a
d
mi
ss
i
o
n
s
F
e
mal
e
T
h
e
r
u
les
o
f
ass
o
ciatio
n
,
cr
u
cial
f
o
r
u
n
d
e
r
s
tan
d
in
g
th
e
in
ter
p
lay
b
etwe
en
v
ar
i
o
u
s
f
a
cto
r
s
,
ar
e
g
en
er
ated
with
s
tr
in
g
e
n
t
p
ar
a
m
eter
s
.
E
m
p
lo
y
in
g
a
m
in
im
u
m
s
u
p
p
o
r
t
o
f
0
.
5
an
d
a
lift
ev
alu
atio
n
cr
iter
io
n
o
f
0
.
1
en
s
u
r
es
th
at
o
n
ly
r
o
b
u
s
t
ass
o
ciatio
n
s
ar
e
co
n
s
id
er
ed
,
f
ilter
in
g
o
u
t
n
o
is
e
an
d
s
p
u
r
io
u
s
co
r
r
elatio
n
s
.
T
h
is
r
ig
o
r
o
u
s
ap
p
r
o
ac
h
,
e
x
em
p
lifie
d
in
T
ab
le
3
,
lay
s
th
e
f
o
u
n
d
ati
o
n
f
o
r
m
ea
n
in
g
f
u
l
an
aly
s
is
an
d
in
ter
p
r
etatio
n
.
I
n
T
ab
le
4
,
th
e
ass
o
ciatio
n
r
u
le
s
d
er
iv
ed
f
r
o
m
th
e
m
o
d
el
s
h
ed
lig
h
t
o
n
th
e
in
tr
icate
r
elat
io
n
s
h
ip
s
with
in
t
h
e
an
aly
ze
d
d
atab
ase.
Sp
ec
if
ical
ly
,
th
ese
r
u
les
illu
m
in
ate
th
e
co
n
n
ec
tio
n
b
etwe
en
s
ex
an
d
s
m
o
k
in
g
-
r
elate
d
d
is
ea
s
es,
p
r
o
v
id
in
g
in
s
ig
h
ts
in
to
p
o
ten
tial
r
is
k
f
ac
to
r
s
an
d
u
n
d
er
ly
in
g
p
atter
n
s
.
E
ac
h
r
u
le
is
ac
co
m
p
an
ied
b
y
a
co
n
f
id
en
ce
m
ea
s
u
r
e,
o
f
f
er
i
n
g
a
q
u
an
titativ
e
ass
ess
m
en
t o
f
th
e
r
eliab
ilit
y
o
f
th
e
ass
o
ciatio
n
.
T
ab
le
3
.
R
u
les o
f
ass
o
ciatio
n
N
o
.
P
r
e
mi
ses
C
o
n
c
l
u
s
i
o
n
5
C
o
u
n
t
a
b
l
e
n
u
mb
e
r
C
a
n
c
e
r
s
i
n
m
e
n
t
h
a
t
c
a
n
b
e
i
n
d
u
c
e
d
b
y
sm
o
k
i
n
g
15
C
o
u
n
t
a
b
l
e
n
u
mb
e
r
C
a
n
c
e
r
s
t
h
a
t
a
r
e
c
a
u
s
e
d
b
y
smo
k
i
n
g
14
F
e
mal
e
C
a
n
c
e
r
s
t
h
a
t
a
r
e
c
a
u
s
e
d
b
y
smo
k
i
n
g
13
M
a
l
e
C
a
n
c
e
r
s
t
h
a
t
a
r
e
c
a
u
s
e
d
b
y
smo
k
i
n
g
16
A
t
t
r
i
b
u
t
a
b
l
e
n
u
mb
e
r
,
m
a
l
e
C
a
n
c
e
r
s
t
h
a
t
a
r
e
c
a
u
s
e
d
b
y
smo
k
i
n
g
12
N
u
mb
e
r
o
f
a
d
mi
ss
i
o
n
s
,
m
a
l
e
C
a
n
c
e
r
s
t
h
a
t
a
r
e
c
a
u
s
e
d
b
y
smo
k
i
n
g
4
A
d
mi
ss
i
o
n
s f
i
g
u
r
e
s
C
a
n
c
e
r
s
i
n
m
e
n
t
h
a
t
c
a
n
b
e
i
n
d
u
c
e
d
b
y
sm
o
k
i
n
g
11
A
d
mi
ss
i
o
n
s f
i
g
u
r
e
s
C
a
n
c
e
r
s
t
h
a
t
a
r
e
c
a
u
s
e
d
b
y
smo
k
i
n
g
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
A
s
tu
d
y
o
f To
b
a
c
co
u
s
e
a
n
d
m
o
r
ta
lity b
y
d
a
ta
min
in
g
(
La
b
eria
n
o
A
n
d
r
a
d
e
-
A
r
en
a
s
)
6855
T
ab
le
4
.
Ass
o
ciatio
n
r
u
les o
b
tain
in
g
co
n
f
id
en
ce
I
d
e
n
t
i
f
i
e
d
a
sso
c
i
a
t
i
o
n
r
u
l
e
s
[
M
a
l
e
]
--
>
[
N
u
mb
e
r
o
f
a
d
mi
s
si
o
n
s
,
C
a
n
c
e
r
s w
h
i
c
h
c
a
n
b
e
c
a
u
s
e
d
b
y
sm
o
k
i
n
g
]
(
c
o
n
f
i
d
e
n
c
e
:
0
.
1
7
5
)
[
M
a
l
e
]
--
>
[
A
t
t
r
i
b
u
t
a
b
l
e
n
u
mb
e
r
,
C
a
n
c
e
r
s w
h
i
c
h
c
a
n
b
e
c
a
u
s
e
d
b
y
sm
o
k
i
n
g
]
(
c
o
n
f
i
d
e
n
c
e
:
0
.
1
7
5
)
[
M
a
l
e
]
--
>
[
C
i
r
c
u
l
a
t
o
r
y
d
i
se
a
ses
w
h
i
c
h
c
a
n
b
e
c
a
u
se
d
b
y
sm
o
k
i
n
g
]
(
c
o
n
f
i
d
e
n
c
e
:
0
.
1
9
0
)
[
N
u
mb
e
r
o
f
a
d
mi
ss
i
o
n
s]
--
>
[
M
a
l
e
,
C
a
n
c
e
r
s w
h
i
c
h
c
a
n
b
e
c
a
u
s
e
d
b
y
sm
o
k
i
n
g
]
(
c
o
n
f
i
d
e
n
c
e
:
0
.
2
2
9
[
A
t
t
r
i
b
u
t
a
b
l
e
n
u
m
b
e
r
]
--
>
[
M
a
l
e
,
C
a
n
c
e
r
s w
h
i
c
h
c
a
n
b
e
c
a
u
s
e
d
b
y
sm
o
k
i
n
g
]
(
c
o
n
f
i
d
e
n
c
e
:
0
.
2
3
7
)
[
N
u
mb
e
r
o
f
a
d
mi
ss
i
o
n
s]
--
>
[
F
e
ma
l
e
]
(
c
o
n
f
i
d
e
n
c
e
:
0
.
3
3
3
)
[
A
t
t
r
i
b
u
t
a
b
l
e
n
u
m
b
e
r
]
--
>
[
F
e
ma
l
e
]
(
c
o
n
f
i
d
e
n
c
e
:
0
.
3
3
3
[
C
a
n
c
e
r
s w
h
i
c
h
c
a
n
b
e
c
a
u
se
d
b
y
smo
k
i
n
g
]
--
>
[
F
e
m
a
l
e
]
(
c
o
n
f
i
d
e
n
c
e
:
0
.
3
3
3
)
[
C
a
n
c
e
r
s w
h
i
c
h
c
a
n
b
e
c
a
u
se
d
b
y
smo
k
i
n
g
]
--
>
[
M
a
l
e
,
N
u
m
b
e
r
o
f
a
d
m
i
ssi
o
n
s]
(
c
o
n
f
i
d
e
n
c
e
:
0
.
3
3
3
)
[
C
a
n
c
e
r
s w
h
i
c
h
c
a
n
b
e
c
a
u
se
d
b
y
smo
k
i
n
g
]
--
>
[
M
a
l
e
,
A
t
t
r
i
b
u
t
a
b
l
e
n
u
m
b
e
r
]
(
c
o
n
f
i
d
e
n
c
e
:
0
.
3
3
3
)
[
N
u
mb
e
r
o
f
a
d
mi
ss
i
o
n
s]
--
>
[
C
a
n
c
e
r
s
w
h
i
c
h
c
a
n
b
e
c
a
u
s
e
d
b
y
sm
o
k
i
n
g
]
(
c
o
n
f
i
d
e
n
c
e
:
0
.
3
4
4
)
[
M
a
l
e
,
N
u
m
b
e
r
o
f
a
d
m
i
ssi
o
n
s]
--
>
[
C
a
n
c
e
r
s w
h
i
c
h
c
a
n
b
e
c
a
u
s
e
d
b
y
sm
o
k
i
n
g
]
(
c
o
n
f
i
d
e
n
c
e
:
0
.
3
4
4
)
[
M
a
l
e
]
--
>
[
C
a
n
c
e
r
s
w
h
i
c
h
c
a
n
b
e
c
a
u
sed
b
y
sm
o
k
i
n
g
]
(
c
o
n
f
i
d
e
n
c
e
:
0
.
3
4
9
)
[
F
e
mal
e
]
--
>
[
C
a
n
c
e
r
s w
h
i
c
h
c
a
n
b
e
c
a
u
s
e
d
b
y
sm
o
k
i
n
g
]
(
c
o
n
f
i
d
e
n
c
e
:
0
.
3
4
9
)
[
A
t
t
r
i
b
u
t
a
b
l
e
n
u
m
b
e
r
]
--
>
[
C
a
n
c
e
r
s w
h
i
c
h
c
a
n
b
e
c
a
u
s
e
d
b
y
s
mo
k
i
n
g
]
(
c
o
n
f
i
d
e
n
c
e
:
0
.
3
5
5
)
[
M
a
l
e
,
A
t
t
r
i
b
u
t
a
b
l
e
n
u
m
b
e
r
]
--
>
[
C
a
n
c
e
r
s w
h
i
c
h
c
a
n
b
e
c
a
u
s
e
d
b
y
sm
o
k
i
n
g
]
(
c
o
n
f
i
d
e
n
c
e
:
0
.
3
5
5
)
[
M
a
l
e
]
--
>
[
A
t
t
r
i
b
u
t
a
b
l
e
n
u
mb
e
r
]
(
c
o
n
f
i
d
e
n
c
e
:
0
.
4
9
2
)
[
F
e
mal
e
]
--
>
[
A
t
t
r
i
b
u
t
a
b
l
e
n
u
m
b
e
r
]
(
c
o
n
f
i
d
e
n
c
e
:
0
.
4
9
2
)
[
C
a
n
c
e
r
s w
h
i
c
h
c
a
n
b
e
c
a
u
se
d
b
y
smo
k
i
n
g
]
--
>
[
N
u
m
b
e
r
o
f
a
d
mi
ss
i
o
n
s]
(
c
o
n
f
i
d
e
n
c
e
:
0
.
5
0
0
)
[
C
a
n
c
e
r
s w
h
i
c
h
c
a
n
b
e
c
a
u
se
d
b
y
smo
k
i
n
g
]
--
>
[
A
t
t
r
i
b
u
t
a
b
l
e
n
u
m
b
e
r
]
(
c
o
n
f
i
d
e
n
c
e
:
0
.
5
0
0
)
[
M
a
l
e
,
C
a
n
c
e
r
s w
h
i
c
h
c
a
n
b
e
c
a
u
s
e
d
b
y
sm
o
k
i
n
g
]
--
>
[
N
u
m
b
e
r
o
f
a
d
m
i
ssi
o
n
s]
(
c
o
n
f
i
d
e
n
c
e
:
0
.
5
0
0
)
[
M
a
l
e
,
C
a
n
c
e
r
s w
h
i
c
h
c
a
n
b
e
c
a
u
s
e
d
b
y
sm
o
k
i
n
g
]
--
>
[
A
t
t
r
i
b
u
t
a
b
l
e
n
u
m
b
e
r
]
(
c
o
n
f
i
d
e
n
c
e
:
0
.
5
0
0
)
[
M
a
l
e
]
--
>
[
N
u
mb
e
r
o
f
a
d
mi
s
si
o
n
s]
(
c
o
n
f
i
d
e
n
c
e
:
0
.
5
0
8
)
[
F
e
mal
e
]
--
>
[
N
u
m
b
e
r
o
f
a
d
mi
ss
i
o
n
s]
(
c
o
n
f
i
d
e
n
c
e
:
0
.
5
0
8
)
[
N
u
mb
e
r
o
f
a
d
mi
ss
i
o
n
s]
--
>
[
M
a
l
e
]
(
c
o
n
f
i
d
e
n
c
e
:
0
.
6
6
7
)
[
A
t
t
r
i
b
u
t
a
b
l
e
n
u
m
b
e
r
]
--
>
[
M
a
l
e
]
(
c
o
n
f
i
d
e
n
c
e
:
0
.
6
6
7
)
[
C
a
n
c
e
r
s w
h
i
c
h
c
a
n
b
e
c
a
u
se
d
b
y
smo
k
i
n
g
]
--
>
[
M
a
l
e
]
(
c
o
n
f
i
d
e
n
c
e
:
0
.
6
6
7
)
[
C
i
r
c
u
l
a
t
o
r
y
d
i
se
a
ses
w
h
i
c
h
c
a
n
b
e
c
a
u
se
d
b
y
sm
o
k
i
n
g
]
--
>
[
M
a
l
e
]
(
c
o
n
f
i
d
e
n
c
e
:
0
.
6
6
7
)
[
N
u
mb
e
r
o
f
a
d
mi
ss
i
o
n
s,
C
a
n
c
e
r
s w
h
i
c
h
c
a
n
b
e
c
a
u
se
d
b
y
sm
o
k
i
n
g
]
--
>
[
M
a
l
e
]
(
c
o
n
f
i
d
e
n
c
e
:
0
.
6
6
7
)
[
A
t
t
r
i
b
u
t
a
b
l
e
n
u
m
b
e
r
,
C
a
n
c
e
r
s
w
h
i
c
h
c
a
n
b
e
c
a
u
se
d
b
y
sm
o
k
i
n
g
]
--
>
[
M
a
l
e
]
(
c
o
n
f
i
d
e
n
c
e
:
0
.
6
6
7
)
I
m
p
o
r
ta
n
t
o
u
tco
m
es
s
u
ch
as
co
n
f
id
en
ce
s
u
p
p
o
r
t
o
r
lift,
as
lis
ted
in
T
ab
le
5
,
ar
e
h
ig
h
lig
h
ted
to
f
u
lf
ill
th
e
s
p
ec
if
ied
cr
iter
ia.
T
h
ese
c
r
iter
ia
s
er
v
e
as
b
en
c
h
m
ar
k
s
f
o
r
ev
alu
atin
g
th
e
s
tr
en
g
t
h
o
f
as
s
o
ciatio
n
s
b
etwe
en
d
if
f
er
en
t
elem
en
ts
.
T
h
e
c
o
n
f
i
d
en
ce
c
r
iter
ia,
d
e
p
icted
i
n
Fi
g
u
r
e
6
,
ar
e
s
tr
u
ct
u
r
ed
in
a
s
c
h
em
e
alig
n
i
n
g
with
th
eir
ass
o
ciatio
n
r
u
les
an
d
o
r
g
an
ized
elem
en
ts
.
Fig
u
r
e
7
p
r
esen
ts
th
e
ass
o
ciatio
n
r
u
les
alo
n
g
s
id
e
th
e
o
u
tc
o
m
es
o
f
ap
p
l
y
in
g
th
e
lift
cr
iter
io
n
,
e
n
ab
lin
g
th
e
s
elec
tio
n
o
f
r
u
les ex
ce
ed
in
g
a
v
alu
e
o
f
1
.
T
ab
le
5
.
R
esu
lt o
n
th
e
r
u
les o
f
ass
o
ciatio
n
S
u
p
p
o
r
t
C
o
n
f
i
d
e
n
c
e
La
P
l
a
c
e
G
a
i
n
p
-
s
Li
f
t
C
o
n
v
i
c
t
i
o
n
0
.
1
1
6
0
.
2
3
7
0
.
7
4
8
-
0
.
8
6
8
0
.
0
0
2
1
.
0
1
6
1
.
0
0
5
0
.
1
7
5
0
.
3
5
5
0
.
7
4
7
-
0
.
8
1
0
0
.
0
0
3
1
.
0
1
6
1
.
0
0
9
0
.
1
1
6
0
.
3
4
9
0
.
8
3
7
-
0
.
5
5
0
0
1
1
0
.
2
3
3
0
.
3
4
9
0
.
7
4
0
-
1
.
1
0
1
0
1
1
0
.
1
1
6
0
.
3
5
5
0
.
8
4
1
-
0
.
5
4
0
0
.
0
0
2
1
.
0
1
6
1
.
0
0
9
0
.
1
1
6
0
.
3
4
4
0
.
8
3
4
-
0
.
5
6
1
-
0
.
0
0
2
0
.
9
8
4
0
.
9
9
2
0
.
1
1
6
0
.
2
2
9
0
.
7
4
0
-
0
.
8
9
9
-
0
.
0
0
2
0
.
9
8
4
0
.
9
9
5
0
.
1
7
5
0
.
3
4
4
0
.
7
7
9
-
0
.
8
4
1
-
0
.
0
0
3
0
.
9
8
4
0
.
9
9
2
T
h
is
s
y
s
tem
atic
ap
p
r
o
ac
h
aid
s
in
id
en
tify
in
g
s
ig
n
if
ican
t
p
att
er
n
s
an
d
r
elatio
n
s
h
ip
s
with
in
t
h
e
d
ataset,
f
ac
ilit
atin
g
in
f
o
r
m
e
d
d
ec
is
io
n
-
m
ak
in
g
a
n
d
s
tr
ateg
y
d
ev
elo
p
m
en
t.
Mo
r
e
o
v
er
,
b
y
e
x
am
in
in
g
th
ese
m
etr
ics
s
u
ch
as
co
n
f
id
en
ce
s
u
p
p
o
r
t
an
d
lift,
r
esear
ch
er
s
ca
n
g
ain
i
n
s
ig
h
ts
in
to
th
e
r
eliab
ilit
y
an
d
s
ig
n
if
ican
ce
o
f
t
h
e
id
en
tifie
d
ass
o
ciatio
n
s
.
T
h
ese
m
etr
ics
p
lay
a
cr
u
cial
r
o
le
in
d
eter
m
in
in
g
th
e
s
tr
en
g
th
an
d
im
p
o
r
ta
n
ce
o
f
r
elatio
n
s
h
ip
s
b
etwe
en
v
ar
iab
l
es,
g
u
id
in
g
s
u
b
s
eq
u
en
t
a
n
aly
s
is
an
d
in
te
r
p
r
etatio
n
.
T
h
e
s
tr
u
ctu
r
ed
o
r
g
a
n
izatio
n
o
f
cr
iter
ia
a
n
d
ass
o
ciatio
n
r
u
l
es
s
tr
ea
m
lin
es
th
e
p
r
o
ce
s
s
o
f
id
en
tify
in
g
m
ea
n
in
g
f
u
l
p
atter
n
s
an
d
c
o
r
r
elatio
n
s
,
u
ltima
tely
co
n
tr
ib
u
tin
g
to
a
d
ee
p
er
u
n
d
er
s
tan
d
in
g
o
f
th
e
u
n
d
er
ly
i
n
g
d
ata
d
y
n
am
ics.
T
h
r
o
u
g
h
m
eticu
lo
u
s
an
aly
s
is
an
d
a
d
h
er
e
n
ce
to
estab
lis
h
ed
cr
iter
ia,
r
esear
ch
er
s
ca
n
m
ak
e
in
f
o
r
m
e
d
d
ec
is
io
n
s
,
o
p
tim
ize
p
r
o
ce
s
s
es,
an
d
u
n
co
v
er
v
alu
ab
le
i
n
s
ig
h
ts
th
at
d
r
iv
e
p
r
o
g
r
ess
an
d
in
n
o
v
a
tio
n
in
th
eir
r
esp
ec
tiv
e
f
ield
s
.
Evaluation Warning : The document was created with Spire.PDF for Python.