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Mar
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2
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4
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is
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Feb
23
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2
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2
5
Acc
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ted
Mar
15
,
2
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5
Artifi
c
ial
in
telli
g
e
n
c
e
(AI)
p
a
v
e
d
th
e
wa
y
a
n
d
h
e
lp
i
n
g
h
a
n
d
f
o
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t
h
e
m
e
d
ica
l
p
ra
c
ti
ti
o
n
e
rs
in
v
a
ri
o
u
s
a
sp
e
c
ts
a
n
d
e
a
rly
d
ise
a
se
p
re
d
ictio
n
is
o
n
e
a
m
o
n
g
m
a
n
y
.
I
n
terd
isc
ip
l
in
a
ry
re
se
a
rc
h
stu
d
ies
o
n
th
e
e
a
rly
p
re
d
ictio
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o
f
d
ise
a
se
s
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re
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ften
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n
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ly
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e
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se
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a
c
c
u
ra
c
y
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f
th
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p
re
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ictio
n
m
o
d
e
l.
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t
h
o
w
e
a
rly
th
e
se
d
ise
a
se
s
c
a
n
b
e
p
re
d
icte
d
will
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o
t
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e
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n
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d
in
m
a
n
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o
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h
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se
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rc
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ies
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n
les
s
th
e
y
h
a
v
e
a
ti
m
e
se
ries
d
a
ta.
Th
is
wo
rk
p
ro
p
o
se
s
a
m
a
c
h
in
e
lea
rn
in
g
m
o
d
e
l,
A
p
D
e
C
wh
ich
so
l
v
e
s
th
e
a
b
o
v
e
-
m
e
n
ti
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n
e
d
p
ro
b
lem
b
y
g
e
n
e
ra
ti
n
g
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ss
o
c
iati
o
n
r
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les
fo
r
t
h
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e
a
rly
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ise
a
se
p
re
d
ictio
n
o
f
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h
e
ime
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p
a
ti
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n
ts.
Th
e
A
p
D
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m
o
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e
l
c
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late
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t
h
e
p
ro
b
a
b
il
it
y
o
f
o
c
c
u
rre
n
c
e
o
f
e
lev
e
n
Alz
h
e
ime
r
d
ise
a
se
p
re
d
ictio
n
risk
fa
c
to
rs
a
n
d
id
e
n
ti
fies
th
e
c
o
m
b
in
a
ti
o
n
o
f
d
ise
a
se
s
th
a
t
c
a
n
lea
d
t
o
Alz
h
e
ime
r
d
ise
a
se
.
Th
e
a
ss
o
c
iatio
n
ru
les
will
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e
g
e
n
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ra
ted
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y
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o
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sid
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g
t
h
e
o
b
se
rv
e
d
c
o
m
b
in
a
ti
o
n
o
f
risk
fa
c
to
rs
.
Th
e
re
se
a
rc
h
i
n
tro
d
u
c
e
s
a
n
i
n
n
o
v
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ti
v
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a
p
p
ro
a
c
h
th
a
t
h
e
lp
s
in
th
e
e
a
rly
p
re
d
ictio
n
o
f
Alz
h
e
ime
r
d
ise
a
se
fro
m
th
e
risk
fa
c
to
rs
/
s
y
m
p
to
m
s
.
Th
e
re
su
lt
s
s
h
o
w
t
h
e
st
r
o
n
g
c
o
rr
e
latio
n
o
f
d
iab
e
tes
a
n
d
b
l
o
o
d
p
re
ss
u
re
wi
th
Alz
h
e
ime
r
d
ise
a
se
.
K
ey
w
o
r
d
s
:
Alzh
eim
er
’
s
d
is
ea
s
e
Ap
r
io
r
i a
lg
o
r
ith
m
Ar
tific
ial
in
tellig
en
ce
Ass
o
ciatio
n
r
u
les
Ma
ch
in
e
lear
n
in
g
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
:
So
n
am
Vay
alip
ar
am
b
il M
aju
Dep
ar
tm
en
t o
f
C
o
m
p
u
ter
Scie
n
ce
an
d
E
n
g
in
ee
r
in
g
,
C
h
r
is
t U
n
iv
er
s
ity
B
an
g
alo
r
e,
Kar
n
atak
a
,
I
n
d
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E
m
ail: so
n
am
.
m
aju
@
r
es.c
h
r
is
tu
n
iv
er
s
ity
.
in
1.
I
NT
RO
D
UCT
I
O
N
I
n
th
e
er
a
o
f
d
ata
an
al
y
tics
an
d
ar
tific
ial
in
tellig
en
ce
(
AI
)
,
th
e
p
r
ev
alen
ce
o
f
co
n
d
itio
n
s
s
u
ch
a
s
Alzh
eim
er
’
s
d
is
ea
s
e
h
as
em
er
g
ed
as
a
s
ig
n
if
ican
t
2
1
st
-
ce
n
tu
r
y
p
u
b
lic
h
ea
lth
ch
allen
g
e.
D
esp
ite
Alzh
eim
er
's
b
ein
g
in
cu
r
ab
le,
lev
er
a
g
in
g
d
ata
an
aly
tics
o
n
elec
tr
o
n
ic
h
e
alth
r
ec
o
r
d
s
(
E
HR
)
h
av
e
b
ec
o
m
e
in
s
tr
u
m
en
tal
in
p
r
ed
ictin
g
th
e
d
is
ea
s
e
at
an
ea
r
ly
s
tag
e
[
1
]
.
AI
alg
o
r
ith
m
s
h
elp
in
u
n
d
er
s
tan
d
in
g
th
e
co
r
r
elatio
n
o
f
d
is
ea
s
es,
lo
n
g
d
is
tan
ce
m
o
n
ito
r
in
g
o
f
p
atien
ts
,
an
aly
zin
g
th
e
m
ed
ic
al
im
ag
es
f
o
r
id
en
tify
in
g
th
e
f
o
r
eig
n
b
o
d
ies
an
d
wh
at
n
o
t
[
2
]
.
E
ar
ly
d
is
ea
s
e
p
r
ed
ictio
n
is
o
n
e
s
eg
m
en
t
th
at
h
ails
th
e
co
n
tr
i
b
u
tio
n
s
o
f
AI
[
3
]
,
b
u
t
m
o
s
t
o
f
th
e
r
esear
ch
wo
r
k
h
alts
with
th
e
p
r
ed
ictio
n
ac
cu
r
ac
y
b
u
t
h
o
w
ea
r
ly
a
d
is
ea
s
e
ca
n
b
e
p
r
ed
icted
o
r
th
e
ten
tativ
e
tim
e
a
d
is
ea
s
e
will
tak
e
to
ev
o
lv
e
in
th
e
h
u
m
an
b
o
d
y
is
n
o
t
ex
p
lo
r
ed
m
u
ch
.
T
h
e
d
is
ea
s
e'
s
ev
o
lu
tio
n
tim
e
in
a
h
u
m
an
b
o
d
y
ch
an
g
e
f
r
o
m
p
e
r
s
o
n
to
p
er
s
o
n
d
e
p
en
d
i
n
g
o
n
h
is
/h
er
life
s
ty
le,
s
tr
ess
lev
els,
ea
tin
g
h
ab
its
,
a
n
d
ex
er
cise.
So
,
g
en
er
alizin
g
th
e
tim
e
d
u
r
atio
n
o
r
ag
e
is
p
r
ac
tically
im
p
o
s
s
ib
le.
Ho
wev
e
r
,
tech
n
ically
it
is
p
o
s
s
ib
le
to
p
r
o
v
e
th
at
a
s
et
o
f
s
y
m
p
to
m
s
ca
n
lead
to
a
p
a
r
ticu
lar
d
is
ea
s
e
in
f
u
tu
r
e
a
n
d
t
h
er
eb
y
u
n
d
e
r
g
o
in
g
co
n
s
is
ten
t m
o
n
ito
r
in
g
an
d
p
r
e
v
en
tiv
e
tr
ea
tm
en
ts
to
p
u
s
h
th
e
d
is
ea
s
e
f
o
r
a
ce
r
tain
tim
e.
Alzh
eim
er
b
ein
g
a
n
eu
r
o
d
eg
en
er
ativ
e
d
is
ea
s
e
is
ca
u
s
ed
b
y
th
e
f
o
r
m
atio
n
o
f
ab
n
o
r
m
al
d
ep
o
s
its
o
f
p
r
o
tein
i
n
th
e
b
r
ai
n
.
T
h
e
b
r
ain
o
f
a
p
e
r
s
o
n
with
Alzh
eim
er
'
s
also
h
as
lo
wer
le
v
els
o
f
n
eu
r
o
tr
an
s
m
itter
,
wh
ich
m
ay
ca
u
s
e
th
e
r
em
ain
i
n
g
ce
ll
s
to
co
m
m
u
n
icate
with
ea
ch
o
th
er
less
ef
f
ec
tiv
ely
an
d
c
r
e
ates
p
r
o
b
lem
s
with
m
em
o
r
y
an
d
th
in
k
in
g
[
4
]
.
Alz
h
eim
er
d
is
ea
s
e
ca
n
b
e
p
r
ed
ict
ed
ea
r
ly
b
y
o
b
s
er
v
in
g
th
e
s
y
m
p
to
m
s
an
d
d
is
ea
s
es
wh
ich
ca
n
lead
to
th
e
co
n
d
iti
o
n
o
f
Alzh
eim
er
.
Stu
d
ies
s
h
o
w
d
iv
er
s
e
d
is
ea
s
es
lik
e
h
ea
r
t
d
is
ea
s
e,
d
iab
etes
,
Evaluation Warning : The document was created with Spire.PDF for Python.
I
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t J Ar
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I
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tell
I
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N:
2252
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8
9
3
8
A
p
DeC:
A
r
u
le
g
en
era
to
r
fo
r
A
lz
h
eime
r
's d
is
ea
s
e
p
r
ed
ictio
n
(
S
o
n
a
m
V
a
ya
lip
a
r
a
mb
il Ma
ju
)
1773
b
lo
o
d
p
r
ess
u
r
e,
a
n
d
ch
o
lest
er
o
l
as
f
ac
to
r
s
th
at
ten
d
to
ca
u
s
e
Alzh
eim
er
d
is
ea
s
e
[
5
]
.
I
t
is
p
o
s
s
ib
le
to
d
ev
elo
p
alg
o
r
ith
m
s
to
s
elec
t
an
d
r
an
k
th
e
f
ac
to
r
s
wh
ich
ca
u
s
e
d
is
ea
s
es,
to
an
aly
ze
th
e
co
r
r
elatio
n
o
f
d
is
ea
s
es
an
d
to
id
en
tify
th
e
c
o
m
b
in
atio
n
o
f
d
i
s
ea
s
es wh
ich
tr
ig
g
er
th
e
co
n
d
i
tio
n
o
f
o
th
er
d
is
ea
s
es.
Nu
m
er
o
u
s
alg
o
r
ith
m
s
ar
e
a
v
a
ilab
le
to
u
n
c
o
v
er
t
h
e
ass
o
ciatio
n
s
b
etwe
en
th
e
s
y
m
p
to
m
s
o
r
th
e
r
is
k
f
ac
to
r
s
an
d
to
p
r
e
d
ict
th
e
ass
o
ciatio
n
b
etwe
en
d
is
ea
s
es
[
6
]
,
[
7
]
.
E
HR
d
ata
is
b
ein
g
u
tili
ze
d
f
o
r
v
ar
io
u
s
clin
ical
an
d
r
esear
ch
a
p
p
licatio
n
s
,
in
clu
d
in
g
d
is
ea
s
e
p
r
ed
ictio
n
an
d
ass
o
ciatio
n
r
u
le
m
in
in
g
.
Su
p
er
v
is
ed
m
ac
h
in
e
lear
n
in
g
alg
o
r
ith
m
s
ar
e
o
f
ten
u
s
ed
in
d
is
ea
s
e
p
r
ed
ictio
n
r
esear
ch
.
Ho
wev
er
,
v
er
y
f
ew
ar
ticles
ex
p
lo
r
e
th
e
p
o
s
s
ib
ilit
ies
o
f
ass
o
ciatio
n
r
u
l
es
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d
en
s
em
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le
m
o
d
els
o
f
ap
r
io
r
i
an
d
o
th
e
r
alg
o
r
ith
m
s
.
Ass
o
ciatio
n
r
u
les
ar
e
u
s
ed
to
ex
tr
ac
t
m
atch
ed
f
ea
tu
r
es
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r
o
m
m
ed
ical
r
ec
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r
d
s
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th
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s
ed
clu
s
ter
in
g
an
d
m
u
ltip
le
-
cr
iter
ia
d
ec
is
io
n
an
aly
s
is
to
d
iag
n
o
s
e
th
e
ex
ac
t
d
is
ea
s
e
o
f
th
e
p
atien
t
[
8
]
,
[
9
]
.
Acc
o
r
d
in
g
to
I
n
am
d
ar
et
a
l.
[
1
0
]
,
h
ea
r
t
attac
k
r
is
k
f
ac
to
r
s
wer
e
an
aly
ze
d
u
s
in
g
ass
o
ciatio
n
r
u
les,
an
d
au
th
o
r
s
s
tates
th
e
ad
v
an
tag
es
o
f
u
n
v
eilin
g
th
e
ass
o
ciatio
n
b
etwe
en
r
is
k
f
ac
to
r
s
f
o
r
d
is
ea
s
e
p
r
ed
ictio
n
[
1
1
]
u
s
es
f
r
eq
u
en
t
p
atter
n
g
r
o
wt
h
(
FP
-
Gr
o
wth
)
to
m
in
e
p
atter
n
s
f
r
o
m
th
e
c
o
n
s
o
r
tiu
m
to
estab
lis
h
a
r
eg
is
tr
y
f
o
r
Alzh
eim
er
'
s
d
is
ea
s
e
-
n
eu
r
o
p
s
y
ch
o
lo
g
ical
b
atter
y
(
C
E
R
AD
-
NB
)
d
atab
ase.
T
h
es
e
alg
o
r
ith
m
s
o
f
f
er
s
u
p
p
lem
e
n
tar
y
d
ata
an
aly
s
is
,
p
r
ed
ictiv
e
item
r
esp
o
n
s
e
ca
p
a
b
ilit
ies,
an
d
aid
in
clin
ical
d
ec
is
io
n
-
m
ak
in
g
.
Fin
ally
,
L
iu
et
a
l.
[
1
2
]
ad
d
r
ess
es
a
f
ew
d
r
awb
ac
k
s
o
f
ap
r
io
r
i
al
g
o
r
ith
m
a
n
d
p
r
o
p
o
s
es
a
d
ata
m
in
in
g
al
g
o
r
ith
m
o
f
ass
o
ciatio
n
r
u
les
co
m
b
in
in
g
clu
s
ter
in
g
m
atr
ix
an
d
p
r
u
n
in
g
s
tr
ateg
y
.
I
n
th
is
r
esear
ch
wo
r
k
,
th
e
co
n
ce
p
ts
o
f
s
u
p
er
v
is
ed
an
d
u
n
s
u
p
er
v
is
ed
m
ac
h
in
e
lea
r
n
in
g
alg
o
r
ith
m
s
ar
e
u
s
ed
to
d
esig
n
a
n
en
s
em
b
le
Ap
DeC
alg
o
r
ith
m
.
W
h
ich
ev
alu
ates
th
e
co
m
b
in
atio
n
s
o
f
r
is
k
f
ac
to
r
s
a
n
d
g
en
er
atin
g
r
u
les
f
r
o
m
th
e
r
is
k
f
ac
to
r
s
.
T
h
is
h
elp
s
i
n
th
e
e
ar
ly
p
r
e
d
ictio
n
Alzh
eim
e
r
d
is
ea
s
e
an
d
ass
is
ts
th
e
h
ea
lth
p
r
ac
titi
o
n
er
s
f
o
r
f
u
r
th
er
m
o
n
ito
r
in
g
an
d
tr
ea
tm
en
ts
.
2.
M
E
T
H
O
D
Gen
er
ally
,
all
th
e
Alzh
eim
e
r
p
r
ed
ictio
n
al
g
o
r
ith
m
s
ar
e
s
u
c
ce
s
s
f
u
l
in
d
etec
tin
g
th
e
d
is
ea
s
e
f
r
o
m
th
e
g
iv
en
r
is
k
f
ac
to
r
s
,
b
u
t
n
o
t
d
ir
ec
tin
g
to
th
e
ea
r
ly
p
r
ed
ictio
n
o
f
th
e
d
is
ea
s
e.
Alzh
eim
er
b
ei
n
g
th
e
n
o
n
-
c
u
r
ab
le
d
is
ea
s
e
o
n
ly
th
e
ea
r
ly
p
r
ed
icti
o
n
ca
n
h
elp
th
e
m
e
d
ical
p
r
ac
t
itio
n
er
s
to
tr
ea
t
f
r
o
m
th
e
ea
r
ly
s
tag
e.
Hen
ce
,
th
is
r
esear
ch
an
aly
ze
s
th
e
co
r
r
el
atio
n
o
f
d
is
ea
s
es,
u
n
v
eilin
g
th
e
ass
o
ciatio
n
o
f
r
is
k
f
ac
to
r
s
an
d
co
n
s
is
ten
t
m
o
n
ito
r
in
g
o
f
d
is
ea
s
es.
T
o
attain
th
is
,
an
en
s
em
b
led
alg
o
r
it
h
m
n
am
ed
Ap
DeC
alg
o
r
ith
m
was
p
r
o
p
o
s
ed
wh
ich
en
tails
b
y
cr
ea
tin
g
p
e
r
m
u
tat
io
n
s
an
d
co
m
b
i
n
atio
n
s
o
f
r
is
k
f
ac
to
r
s
f
o
r
Alzh
eim
er
d
is
ea
s
e
p
r
ed
ictio
n
,
em
p
lo
y
in
g
d
ec
is
io
n
tr
ee
s
t
o
ex
tr
ac
t
ass
o
ciatio
n
r
u
les.
Su
b
s
eq
u
en
tly
,
t
h
e
ass
o
ciatio
n
r
u
les
ar
e
test
ed
b
y
co
n
s
id
er
in
g
d
i
f
f
er
en
t
p
er
f
o
r
m
an
ce
m
etr
ics
o
f
ap
r
io
r
i
alg
o
r
i
th
m
,
s
u
ch
as
s
u
p
p
o
r
t,
lift
an
d
co
n
f
id
en
ce
wh
ic
h
h
elp
s
to
id
en
tify
th
e
an
tece
d
e
n
t
an
d
co
n
s
eq
u
e
n
t
r
is
k
f
ac
to
r
s
o
f
th
e
ea
r
ly
Alzh
eim
er
’
s
d
is
ea
s
e
p
r
ed
ictio
n
.
T
h
e
f
lo
w
o
f
th
e
p
r
o
p
o
s
ed
m
o
d
el
is
s
h
o
wn
in
Fig
u
r
e
1
.
Fig
u
r
e
1
.
W
o
r
k
f
lo
w
o
f
Ap
Dec
alg
o
r
ith
m
Evaluation Warning : The document was created with Spire.PDF for Python.
I
SS
N
:
2252
-
8
9
3
8
I
n
t J Ar
tif
I
n
tell
,
Vo
l.
14
,
No
.
3
,
J
u
n
e
20
25
:
1
7
7
2
-
1
7
8
0
1774
T
h
e
p
r
o
p
o
s
ed
m
o
d
el
s
tar
ts
b
y
id
en
tify
in
g
t
h
e
Alzh
eim
er
tr
ig
g
er
in
g
r
is
k
f
ac
to
r
s
an
d
is
o
b
tain
ed
f
r
o
m
ex
is
tin
g
liter
atu
r
e
r
e
v
iews
.
Ma
ju
an
d
Pu
s
h
p
am
[
1
3
]
s
h
o
r
tlis
ted
elev
en
Alzh
eim
e
r
tr
ig
g
er
in
g
r
is
k
f
ac
to
r
s
an
d
is
b
ein
g
co
n
s
id
er
ed
i
n
th
is
r
ese
ar
ch
wo
r
k
.
Fro
m
th
e
o
b
tain
e
d
r
is
k
f
ac
to
r
s
,
th
e
co
m
b
in
atio
n
o
f
r
is
k
f
ac
to
r
s
is
g
en
er
ated
,
an
d
th
e
p
r
o
b
a
b
ilit
y
o
f
ea
ch
co
m
b
in
atio
n
is
ca
l
cu
lated
.
L
ater
,
th
e
Ap
DeC
al
g
o
r
ith
m
cr
ea
tes
th
e
an
tece
d
en
ts
an
d
co
n
s
eq
u
e
n
ts
f
r
o
m
th
e
co
m
b
in
atio
n
s
o
f
r
i
s
k
f
ac
to
r
s
th
r
o
u
g
h
ass
o
ciatio
n
r
u
les.
I
n
itially
,
a
d
ec
is
io
n
tr
ee
is
tr
ain
ed
o
n
a
d
ataset
u
s
in
g
an
iter
ativ
e
d
ic
h
o
to
m
is
er
3
(
I
D3
)
alg
o
r
ith
m
s
.
T
h
e
d
ec
is
io
n
tr
ee
lear
n
s
to
m
ak
e
d
ec
is
io
n
s
b
ased
o
n
th
e
f
ea
tu
r
es
o
f
th
e
d
a
ta.
On
ce
th
e
d
ec
is
io
n
tr
ee
is
tr
ain
ed
,
r
u
les
ar
e
ex
tr
ac
ted
f
r
o
m
it.
T
h
e
r
u
les
ex
tr
ac
ted
f
r
o
m
th
e
d
ec
is
io
n
tr
ee
ca
n
b
e
c
o
n
v
er
te
d
in
to
ass
o
ciatio
n
r
u
les.
Ass
o
ciatio
n
r
u
les
ty
p
ically
h
a
v
e
th
e
f
o
r
m
"X=
>Y
,
"
wh
er
e
X
is
th
e
an
tece
d
en
t
(
th
e
r
is
k
f
ac
to
r
s
)
an
d
Y
is
th
e
co
n
s
eq
u
en
t
(
th
e
Alzh
eim
er
d
is
ea
s
e)
.
Af
ter
f
ilter
in
g
,
th
e
r
ef
in
ed
r
u
les
ca
n
b
e
co
n
s
id
er
e
d
as
ass
o
ciat
io
n
r
u
les
th
at
p
r
o
v
id
e
in
s
ig
h
ts
in
to
r
elat
io
n
s
h
ip
s
b
etwe
en
r
is
k
f
ac
to
r
s
in
th
e
d
ataset
an
d
th
e
p
er
f
o
r
m
a
n
ce
is
ev
alu
ated
b
y
ca
lcu
latin
g
th
e
lift,
s
u
p
p
o
r
t,
a
n
d
co
n
f
i
d
en
ce
.
2
.
1
.
Co
m
bin
a
t
o
rics a
nd
pro
ba
bil
it
y
T
h
e
f
ir
s
t
s
tep
is
t
o
id
e
n
tify
t
h
e
ass
o
ciatio
n
b
etwe
en
p
o
s
s
ib
le
r
is
k
f
ac
to
r
s
o
f
Alzh
eim
er
d
i
s
ea
s
e.
T
h
e
p
r
in
cip
les
o
f
co
m
b
i
n
ato
r
y
ar
e
u
s
ed
to
ex
tr
ac
t
all
p
o
s
s
ib
le
c
o
m
b
in
atio
n
s
o
f
th
ese
r
is
k
f
ac
t
o
r
s
.
Su
p
p
o
s
e
if
r
is
k
f
ac
to
r
1
an
d
r
is
k
f
ac
to
r
2
is
p
r
esen
t,
th
en
th
e
p
r
o
b
ab
ilit
y
o
f
h
av
in
g
Alzh
eim
er
in
th
e
f
u
tu
r
e
will b
e
h
ig
h
.
Usi
n
g
th
e
f
o
r
m
u
la
g
iv
e
n
in
(
1
)
,
all
p
o
s
s
ib
le
co
m
b
in
atio
n
s
h
av
e
b
ee
n
o
b
tain
ed
.
T
h
u
s
,
a
f
u
n
ctio
n
s
y
s
tem
atica
lly
g
en
er
ates p
er
m
u
tatio
n
s
o
f
v
ar
i
ab
les to
ex
p
lo
r
e
d
if
f
er
e
n
t c
o
m
b
in
atio
n
s
an
d
is
s
h
o
wn
in
T
ab
l
e
1
.
=
!
!
(
−
)
!
(
1
)
T
o
ca
lcu
late
th
e
p
r
o
b
ab
ilit
y
o
f
ea
ch
co
m
b
i
n
atio
n
o
f
r
is
k
f
ac
to
r
s
b
y
th
eir
o
cc
u
r
r
en
ce
in
th
e
E
HR
.
Fo
r
th
is
,
th
e
ca
lcu
latio
n
is
d
o
n
e
with
ea
ch
n
u
m
b
er
o
f
c
o
m
b
in
atio
n
s
s
elec
ted
ag
ain
s
t
th
e
to
tal
n
u
m
b
er
o
f
co
m
b
in
atio
n
s
c
r
ea
ted
.
T
o
ca
lc
u
late
th
e
p
r
o
b
a
b
ilit
y
o
f
ea
ch
c
o
m
b
in
atio
n
cr
ea
ted
.
Fin
ally
,
a
m
ap
p
in
g
b
etwe
en
co
m
b
in
atio
n
s
o
f
r
is
k
f
ac
to
r
s
an
d
th
eir
co
r
r
esp
o
n
d
i
n
g
p
r
o
b
ab
ilit
ies
wa
s
ca
lcu
lated
f
o
r
f
u
r
th
er
ev
alu
atio
n
o
f
Ap
DeC
alg
o
r
ith
m
.
I
n
a
d
ec
is
io
n
tr
ee
f
o
r
class
if
icatio
n
,
ea
ch
leaf
n
o
d
e
r
ep
r
esen
ts
a
cla
s
s
o
r
o
u
tco
m
e.
T
o
ca
lcu
late
th
e
p
r
o
b
a
b
ilit
y
ass
o
ciate
d
with
a
s
p
ec
if
ic
co
m
b
in
atio
n
o
f
f
ea
tu
r
es th
at
lead
s
to
a
p
ar
ticu
lar
leaf
n
o
d
e,
d
is
tr
ib
u
tio
n
o
f
i
n
s
tan
ce
s
in
t
h
e
tr
ain
in
g
d
ata
th
at
r
ea
ch
ed
th
at
leaf
n
o
d
e
d
u
r
in
g
th
e
co
n
s
tr
u
ct
io
n
o
f
th
e
tr
ee
will
b
e
o
b
s
er
v
e
d
.
Fo
r
ea
ch
leaf
n
o
d
e
in
th
e
d
ec
is
io
n
tr
ee
,
ex
am
i
n
e
th
e
tr
ain
i
n
g
d
ata
in
s
tan
ce
s
th
at
r
ea
ch
e
d
th
at
leaf
.
C
o
u
n
t
h
o
w
m
an
y
in
s
tan
ce
s
b
elo
n
g
to
ea
c
h
class
(
e.
g
.
,
Alzh
eim
er
'
s
d
is
ea
s
e
o
r
n
o
A
lzh
eim
er
'
s
d
is
ea
s
e
).
C
alcu
late
th
e
p
r
o
b
a
b
ilit
y
o
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ad
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T
ab
le
1
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8
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1775
2
.
2
.
ApDec
:
a
s
s
o
cia
t
io
n r
ule
g
ener
a
t
io
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T
o
ex
tr
ac
t
ass
o
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o
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ith
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s
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h
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s
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o
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ly
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co
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r
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e
n
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et
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e
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ated
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u
r
t
h
er
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s
ed
to
f
in
d
t
h
e
ass
o
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b
etwe
en
th
e
d
is
ea
s
es.
Ap
r
io
r
i
alg
o
r
ith
m
is
u
s
e
d
id
en
tif
y
th
e
b
est
co
m
b
in
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n
o
f
r
is
k
f
ac
to
r
s
th
a
t c
an
tr
ig
g
er
th
e
co
n
d
itio
n
o
f
Alzh
eim
er
d
is
ea
s
e.
2
.
2
.
1
.
Ass
o
cia
t
io
n r
ule g
ener
a
t
o
r
t
hro
ug
h t
re
es
I
n
th
e
d
ataset,
wh
ich
is
u
s
ed
i
n
th
e
r
esear
ch
wo
r
k
,
h
as
r
is
k
f
ac
to
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s
o
f
Alzh
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is
ea
s
e,
alo
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g
with
th
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g
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al
in
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o
r
m
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o
f
th
e
p
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ts
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n
th
is
s
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ic
im
p
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tatio
n
,
th
e
ass
o
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r
u
les
o
f
r
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k
f
ac
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s
ar
e
g
e
n
er
ated
f
o
r
p
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ed
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wh
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th
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e
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t,
in
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e
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Fig
u
r
e
2
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o
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l
(
C
h
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d
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is
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ased
o
n
t
h
e
p
r
o
b
ab
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o
f
o
cc
u
r
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en
ce
.
Similar
ly
,
p
o
s
s
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f
ch
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l
,
with
p
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(
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d
k
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)
,
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e
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w
n
f
u
r
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.
Fig
u
r
e
2
.
T
r
ee
r
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r
esen
tatio
n
o
f
r
u
le
g
en
er
atio
n
2
.
2
.
2
.
Ref
ini
ng
a
nd
f
ilte
ring
E
v
al
u
at
e
t
h
e
s
e
lec
te
d
r
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les
b
y
r
e
f
i
n
i
n
g
a
n
d
f
il
te
r
i
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g
,
a
p
r
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o
r
i
a
lg
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it
h
m
is
u
s
e
d
f
o
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m
in
in
g
m
o
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t s
p
e
ci
f
i
c
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d
f
r
eq
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en
tl
y
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ep
o
r
te
d
s
y
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p
t
o
m
s
an
d
d
e
v
is
in
g
ass
o
ciat
io
n
r
u
les
f
r
o
m
a
t
r
a
n
s
a
cti
o
n
a
l
d
ata
b
as
e.
I
f
a
p
ati
e
n
t
is
h
a
v
i
n
g
r
is
k
f
ac
to
r
1
t
h
en
t
h
e
c
h
a
n
c
es
o
f
t
h
e
s
am
e
p
at
ie
n
t
h
a
v
i
n
g
r
is
k
f
ac
t
o
r
2
o
r
r
is
k
f
ac
t
o
r
3
.
So
,
t
h
e
m
ed
ic
al
p
r
ac
titi
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n
e
r
s
c
a
n
o
b
s
er
v
e
t
h
e
co
-
o
c
cu
r
r
e
n
c
e
p
at
te
r
n
/ass
o
ci
ati
o
n
f
o
ll
o
we
d
b
y
s
e
v
e
r
al
s
y
m
p
t
o
m
s
a
n
d
ad
v
is
e/
n
o
ti
f
y
th
e
p
at
ie
n
ts
a
b
o
u
t
t
h
e
d
is
ea
s
es
t
h
a
t
t
h
e
y
c
an
h
a
v
e
in
f
u
t
u
r
e
.
Me
d
i
ca
l
p
r
a
ctiti
o
n
e
r
s
ca
n
s
ta
r
t
m
e
d
ic
ati
o
n
a
n
d
o
t
h
e
r
n
e
ce
s
s
a
r
y
p
r
ec
au
ti
o
n
s
t
o
d
o
d
g
e
t
h
e
o
cc
u
r
r
e
n
ce
o
f
o
t
h
er
p
r
ed
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ed
d
is
e
ases
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r
e
,
w
e
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n
s
i
d
e
r
t
h
e
s
y
m
p
t
o
m
s
/r
is
k
f
a
cto
r
s
as
t
h
e
a
n
t
ec
ed
en
ts
a
n
d
t
h
e
d
is
e
ase
o
c
cu
r
s
d
u
e
t
o
a
n
t
ec
e
d
e
n
ts
as
c
o
n
s
e
q
u
e
n
t
.
T
h
e
t
h
r
e
e
m
at
r
i
ce
s
t
h
at
h
el
p
s
u
s
t
o
r
e
f
i
n
e
a
n
d
f
il
te
r
th
e
r
u
les
ar
e
s
u
p
p
o
r
t
,
co
n
f
i
d
e
n
c
e
,
a
n
d
li
f
t
.
So
,
s
u
p
p
o
r
t
ca
l
cu
lat
es t
h
e
f
r
e
q
u
e
n
c
y
o
f
o
cc
u
r
r
en
ce
o
f
tw
o
s
y
m
p
t
o
m
s
a
n
d
r
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le
o
u
t
t
h
e
c
o
m
b
i
n
at
io
n
s
wit
h
l
o
w
f
r
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q
u
e
n
c
y
.
Supp
or
t
=
(
,
)
(
2
)
C
o
n
f
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ce
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lcu
lated
h
o
w
o
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ten
th
e
co
m
b
in
atio
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y
m
p
to
m
s
o
cc
u
r
s
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d
ca
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b
e
r
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r
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on
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n
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e
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)
(
)
(
3
)
L
if
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is
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asically
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e
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en
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u
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if
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lates
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e
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en
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o
cc
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r
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r
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ab
ilit
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o
f
s
y
m
p
to
m
A
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d
s
y
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p
to
m
B.
L
ift
=
(
)
(
)
(
4
)
T
ab
le
2
s
h
o
ws
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e
m
etr
ics
c
alcu
latio
n
.
Ass
u
m
ed
m
i_
s
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p
p
o
r
t
is
4
0
%
an
d
m
in
_
s
u
p
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t_
co
u
n
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in
_
s
u
p
p
o
r
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x
item
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et
wh
ich
will
b
e
1
.
6
.
So
,
in
th
e
a
b
o
v
e
ex
am
p
le
we
will
d
is
ca
r
d
ev
e
r
y
th
in
g
less
th
an
1
.
6
.
T
h
e
alg
o
r
ith
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f
o
r
th
e
ab
o
v
e
-
m
en
tio
n
ed
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eth
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g
y
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iv
en
in
Alg
o
r
ith
m
1
.
Alg
o
r
ith
m
1
.
Ap
DeC Alg
o
r
ith
m
1.
if
p
r
ed
ictio
n
Par
am
ete
r
s
is
em
p
ty
:
2.
r
etu
r
n
[
[
]
]
//
b
ase
ca
s
e,
an
em
p
ty
s
et
h
as o
n
e
co
m
b
in
atio
n
(
e
m
p
ty
s
et
its
elf
)
3.
else:
Evaluation Warning : The document was created with Spire.PDF for Python.
I
SS
N
:
2252
-
8
9
3
8
I
n
t J Ar
tif
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.
3
,
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u
n
e
20
25
:
1
7
7
2
-
1
7
8
0
1776
4.
cu
r
r
en
tPar
am
eter
=
p
r
ed
ictio
n
P
ar
am
eter
s
[
0
]
5.
r
estP
ar
am
eter
s
=p
r
ed
ictio
n
Par
am
eter
s
[
1
]
:
6.
with
o
u
tC
u
r
r
en
t=g
en
e
r
atePer
m
u
tatio
n
s
An
d
C
o
m
b
in
atio
n
s
Hel
p
er
(
r
estP
ar
am
eter
s
)
7.
with
C
u
r
r
en
t=[
]
8.
f
o
r
co
m
b
in
atio
n
in
with
o
u
tC
u
r
r
en
t:
9.
with
C
u
r
r
en
t.a
p
p
en
d
(
co
m
b
in
at
io
n
+[
cu
r
r
en
tPar
am
eter
]
)
10.
with
C
u
r
r
en
t.a
p
p
en
d
(
co
m
b
in
at
io
n
)
// in
clu
d
e
th
e
cu
r
r
en
t
p
ar
a
m
eter
as a
s
ep
ar
ate
co
m
b
i
n
atio
n
11.
r
etu
r
n
with
C
u
r
r
e
n
t
12.
#
C
alcu
late
Pro
b
ab
ilit
ies
13.
p
r
o
b
a
b
ilit
ies=ca
lcu
latePr
o
b
ab
ilit
ies(c
o
m
b
in
atio
n
s
)
14.
#
E
x
tr
ac
t A
s
s
o
ciatio
n
R
u
les u
s
in
g
Dec
is
io
n
T
r
ee
s
15.
d
ec
is
io
n
T
r
ee
Mo
d
el=
tr
ain
Dec
i
s
io
n
T
r
ee
Mo
d
el(
d
ataset)
16.
ass
o
ciatio
n
R
u
les=ex
tr
ac
tAs
s
o
ciatio
n
R
u
lesF
r
o
m
Dec
is
io
n
T
r
ee
(
d
ec
is
io
n
T
r
ee
Mo
d
el
)
17.
#
Ap
p
ly
Ap
r
io
r
i A
lg
o
r
ith
m
f
o
r
B
est C
o
m
b
in
atio
n
s
18.
b
estC
o
m
b
in
atio
n
s
=a
p
p
ly
Ap
r
i
o
r
iAlg
o
r
ith
m
(
d
ataset)
19.
#
E
v
alu
ate
R
u
les u
s
in
g
Su
p
p
o
r
t,
C
o
n
f
id
en
ce
,
an
d
L
if
t
20.
s
u
p
p
o
r
tMa
tr
ix
=c
alcu
lateSu
p
p
o
r
tMa
tr
ix
(
d
ataset,
ass
o
ciatio
n
R
u
les)
21.
co
n
f
id
en
ce
Ma
tr
i
x
=c
alcu
lateCo
n
f
id
en
ce
Ma
tr
i
x
(
d
ataset,
ass
o
ciatio
n
R
u
les)
22.
liftM
atr
ix
=c
alcu
lateL
if
tMa
tr
ix
(
s
u
p
p
o
r
tMa
tr
ix
,
co
n
f
id
en
ce
Ma
tr
ix
)
23.
#
Sh
o
r
tlis
t Ru
les with
Sp
ec
if
ic
R
is
k
Facto
r
s
24.
r
u
lesW
ith
Diab
etes=f
ilter
R
u
les
W
ith
R
i
s
k
Facto
r
(
ass
o
ciatio
n
R
u
les,
'
Diab
ete
s
'
)
25.
#
Def
in
e
Fu
n
ctio
n
to
Get
R
u
les f
o
r
Diab
etes
26.
d
iab
etesR
u
les=g
etR
u
le
s
Fo
r
Di
ab
etes(
d
ec
is
io
n
T
r
ee
Mo
d
el)
27.
#
C
alcu
late
Su
p
p
o
r
t,
C
o
n
f
id
en
ce
,
an
d
L
if
t
f
o
r
Diab
etes Ru
les
28.
s
u
p
p
o
r
tDiab
etesR
u
les=ca
lcu
lateSu
p
p
o
r
tMa
tr
ix
(
d
ataset,
d
iab
etesR
u
les)
29.
co
n
f
id
en
ce
Diab
etesR
u
les=ca
lcu
lateCo
n
f
id
en
ce
Ma
tr
ix
(
d
ataset,
d
iab
etesR
u
les)
30.
liftDia
b
etesR
u
le
s
=c
alcu
lateL
i
f
tMa
tr
ix
(
s
u
p
p
o
r
tDiab
etesR
u
les,
co
n
f
id
en
ce
Diab
etesR
u
les)
31.
#
Fit
Mo
d
els with
Ma
x
im
u
m
I
t
em
s
in
th
e
I
f
C
o
n
d
itio
n
32.
f
itMo
d
elW
ith
Ma
x
I
tem
s
(
d
ataset,
m
ax
I
tem
s
=4
)
T
ab
le
2
.
Me
tr
ics ca
lcu
latio
n
R
i
s
k
f
a
c
t
o
r
c
o
m
b
i
n
a
t
i
o
n
S
u
p
p
o
r
t
c
o
u
n
t
G
l
y
h
b
,
M
M
S
E
4
G
l
y
h
b
,
H
D
L
1
G
l
y
h
b
,
C
h
o
l
2
G
l
y
h
b
,
B
M
I
2
M
M
S
E,
h
d
l
1
M
M
S
E,
C
h
o
l
2
M
M
S
E,
B
M
I
2
H
D
L,
C
h
o
l
1
H
D
L,
B
M
I
1
C
h
o
l
,
B
M
I
1
3.
RE
SU
L
T
S AN
D
D
I
SCU
SS
I
O
N
T
h
e
r
esear
ch
wo
r
k
in
t
r
o
d
u
ce
d
a
n
o
v
el
s
tr
ateg
y
o
f
Ap
Dec
th
at
g
en
er
ates
ass
o
ciatio
n
r
u
le,
to
id
en
tify
th
e
co
r
r
elatio
n
b
etwe
en
th
e
r
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s
k
f
ac
to
r
s
wh
ich
ca
n
tr
ig
g
er
Alzh
eim
er
d
is
ea
s
e
an
d
th
er
eb
y
h
elp
in
g
in
ea
r
ly
p
r
ed
ictio
n
o
f
th
e
s
am
e.
Star
tin
g
f
r
o
m
p
er
m
u
tatio
n
g
e
n
er
atio
n
f
o
llo
wed
b
y
p
r
o
b
ab
ilit
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lcu
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an
d
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tili
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g
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n
tr
ee
s
to
ex
tr
ac
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th
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ass
o
ciatio
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r
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f
in
ally
,
r
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m
etr
ics
an
aly
s
is
is
d
o
n
e
b
y
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alu
atin
g
s
u
p
p
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r
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co
n
f
id
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ce
a
n
d
lift.
T
h
e
d
er
i
v
ed
ass
o
ciatio
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r
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les
s
h
ed
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h
t
o
n
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icate
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elatio
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s
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ip
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b
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n
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m
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n
d
itio
n
s
.
T
h
e
a
p
p
li
ca
tio
n
o
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th
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p
r
o
p
o
s
ed
m
eth
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o
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g
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y
ield
e
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n
o
tewo
r
th
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o
u
tco
m
es
as
s
h
o
wn
in
T
ab
le
s
3
an
d
4
.
Fro
m
th
e
r
esu
lt
a
n
aly
s
is
,
p
ati
en
ts
d
iag
n
o
s
ed
with
b
lo
o
d
p
r
ess
u
r
e,
d
iab
etes,
c
h
o
lest
er
o
l
a
n
d
k
id
n
ey
d
is
ea
s
es
h
av
e
a
p
er
f
ec
t
as
s
o
ciatio
n
with
Alzh
eim
er
’
s
,
in
d
icatin
g
a
r
o
b
u
s
t
r
elatio
n
s
h
ip
b
etwe
en
th
ese
co
n
d
itio
n
s
as
s
h
o
wn
in
T
ab
le
3
.
E
v
en
th
o
u
g
h
th
is
r
esear
ch
co
n
ce
n
tr
ated
o
n
d
is
ea
s
es
ass
o
ciate
d
with
Alzh
eim
er
d
is
ea
s
es,
th
e
r
esu
lt
s
o
b
tain
ed
f
ew
v
e
r
y
in
ter
esti
n
g
ass
o
ciatio
n
s
b
etwe
en
t
h
e
r
is
k
f
ac
to
r
s
,
as
s
h
o
wn
in
T
ab
le
4
.
Un
d
er
s
tan
d
in
g
th
e
s
e
co
m
p
lex
r
elatio
n
s
h
ip
s
ca
n
ass
is
t
h
ea
lth
ca
r
e
p
r
o
f
ess
io
n
als
in
d
iag
n
o
s
in
g
an
d
m
an
ag
in
g
m
u
ltip
le
c
o
-
o
cc
u
r
r
i
n
g
co
n
d
itio
n
s
m
o
r
e
ef
f
ec
tiv
el
y
.
T
h
e
g
r
a
p
h
ical
r
e
p
r
esen
tatio
n
o
f
th
e
ass
o
ciatio
n
b
etwe
en
th
e
ab
o
v
e
-
m
e
n
tio
n
ed
d
is
ea
s
es with
Alzh
eim
er
is
s
h
o
wn
in
Fig
u
r
es 3
an
d
4
.
Evaluation Warning : The document was created with Spire.PDF for Python.
I
n
t J Ar
tif
I
n
tell
I
SS
N:
2252
-
8
9
3
8
A
p
DeC:
A
r
u
le
g
en
era
to
r
fo
r
A
lz
h
eime
r
's d
is
ea
s
e
p
r
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ictio
n
(
S
o
n
a
m
V
a
ya
lip
a
r
a
mb
il Ma
ju
)
1777
T
ab
le
3
.
R
esu
lt a
n
aly
s
is
R
u
l
e
s
O
b
serv
a
t
i
o
n
s
R
u
l
e
1
6
:
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l
o
o
d
P
r
e
ss
u
r
e
(
A
l
z
h
e
i
mers
,
D
i
a
b
e
t
e
s)
S
u
p
p
o
r
t
:
5
.
2
6
%
C
o
n
f
i
d
e
n
c
e
:
1
0
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Li
f
t
:
1
9
.
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Th
i
s
r
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l
e
s
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h
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l
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P
r
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r
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t
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o
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l
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s
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.
R
u
l
e
9
:
B
l
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d
P
r
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ss
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r
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e
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c
a
l
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R
u
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C
h
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C
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m
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r
s.
R
u
l
e
3
:
D
i
a
b
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t
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n
d
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l
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m
e
r
s
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[
1
4
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–
[
1
6
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a
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m
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Naïv
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d
Evaluation Warning : The document was created with Spire.PDF for Python.
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8
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20
25
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7
7
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1778
d
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tr
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[
1
7
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–
[
1
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h
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to
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els.
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ch
as
Gau
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t
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Featu
r
e
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s
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v
e
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ay
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s
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B
ay
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s
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n
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o
r
ith
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[
2
3
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co
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in
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B
ay
es'
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em
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as
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m
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m
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le
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eth
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d
s
[
1
6
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,
[
2
4
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,
[
2
5
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,
p
a
r
ticu
lar
l
y
s
tack
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f
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m
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e
in
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p
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o
v
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Fig
u
r
e
3
.
Ass
o
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an
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ly
s
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o
r
Alzh
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Fig
u
r
e
4
.
Ass
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ly
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On
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RE
F
E
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NC
E
S
[
1
]
M
.
L
.
K
o
l
l
i
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g
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.
,
“
D
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p
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R
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P
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H
e
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v
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.
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9
0
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0
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9
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.
[
2
]
N
.
G
.
N
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a
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K
a
p
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l
u
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sco
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[
3
]
Y
.
K
u
mar,
A
.
K
o
u
l
,
R
.
S
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n
g
l
a
,
a
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M
.
F
.
I
j
a
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,
“
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[
4
]
M
.
W
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B
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E
.
C
.
Ed
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o
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s
,
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n
d
D
.
P
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S
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o
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,
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5
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Z.
B
r
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j
y
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h
a
n
d
R
.
K
a
r
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ma
n
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.
[
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