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teristics
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a
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–
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)
a
n
d
a
n
a
ly
z
e
s
1
1
p
re
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r
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d
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d
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m
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(CRIS
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fra
m
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o
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r
s
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p
e
rv
ise
d
m
a
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h
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rn
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L)
m
o
d
e
ls
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d
e
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tree
,
ra
n
d
o
m
f
o
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st,
g
ra
d
ien
t
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d
tree
s,
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n
d
n
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Ba
y
e
s
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we
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e
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d
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v
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lu
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te
d
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se
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e
m
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tri
c
s,
with
re
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lt
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s
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g
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t
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lan
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e
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ictiv
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p
e
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rm
a
n
c
e
a
n
d
c
lea
r
in
terp
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tab
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,
m
a
k
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n
g
it
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tab
le
fo
r
h
e
a
rt
fa
il
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re
risk
p
re
d
ictio
n
.
T
h
e
fi
n
d
i
n
g
s
d
e
m
o
n
stra
te
th
e
p
o
ten
ti
a
l
o
f
ML
a
p
p
r
o
a
c
h
e
s
to
su
p
p
o
rt
c
li
n
ica
l
risk
a
ss
e
ss
m
e
n
t
a
n
d
e
n
h
a
n
c
e
d
e
c
isio
n
-
m
a
k
i
n
g
in
h
e
a
r
t
fa
il
u
re
m
a
n
a
g
e
m
e
n
t.
K
ey
w
o
r
d
s
:
C
lin
ical
r
is
k
p
r
ed
ictio
n
C
o
n
g
esti
v
e
h
ea
r
t f
ailu
r
e
C
R
I
S
P
-
DM
Dec
is
io
n
tr
ee
I
n
ter
p
r
etab
le
m
o
d
els
Ma
ch
in
e
lear
n
in
g
Mo
d
el
ev
alu
atio
n
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
:
Yu
en
y
o
n
g
Nils
iam
Dep
ar
tm
en
t o
f
E
lectr
ical
an
d
C
o
m
p
u
ter
E
n
g
in
ee
r
in
g
,
Facu
lt
y
o
f
E
n
g
in
ee
r
i
n
g
Kin
g
Mo
n
g
k
u
t
’
s
Un
iv
er
s
ity
o
f
T
ec
h
n
o
lo
g
y
No
r
th
B
an
g
k
o
k
1
5
1
8
B
an
g
s
u
e,
B
an
g
k
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k
,
T
h
ailan
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E
m
ail: y
u
en
y
o
n
g
.
n
@
en
g
.
k
m
u
t
n
b
.
ac
.
th
1.
I
NT
RO
D
UCT
I
O
N
C
o
n
g
esti
v
e
h
ea
r
t
f
ailu
r
e
(
C
HF)
is
a
lead
in
g
ca
u
s
e
o
f
m
o
r
b
id
ity
an
d
m
o
r
tality
wo
r
ld
wid
e
,
with
r
is
k
f
ac
to
r
s
s
u
ch
as
ag
e,
s
ex
,
h
y
p
er
ten
s
io
n
,
d
y
s
lip
id
em
ia,
a
n
d
d
iab
etes
th
at
ca
n
p
r
o
g
r
ess
to
s
er
io
u
s
ca
r
d
iac
co
m
p
licatio
n
s
,
u
n
d
e
r
s
co
r
in
g
t
h
e
n
ee
d
f
o
r
ea
r
ly
r
is
k
id
en
tifi
ca
tio
n
[
1
]
,
[
2
]
.
Ma
ch
in
e
lear
n
in
g
(
ML
)
,
a
b
r
a
n
ch
o
f
ar
tific
ial
in
tellig
en
ce
th
at
lear
n
s
f
r
o
m
d
ata
with
o
u
t
e
x
p
licit
p
r
o
g
r
am
m
in
g
[
3
]
−
[
5
]
,
h
as
s
h
o
wn
s
tr
o
n
g
p
o
ten
tial
f
o
r
h
ea
r
t
f
ailu
r
e
p
r
ed
ictio
n
[
6
]
−
[
8
]
.
Su
p
e
r
v
is
ed
alg
o
r
ith
m
s
—
d
ec
is
io
n
tr
ee
s
,
r
an
d
o
m
f
o
r
ests
,
g
r
ad
ien
t
b
o
o
s
tin
g
,
an
d
n
aïv
e
B
ay
es
—
p
r
ed
ict
h
e
ar
t
f
ailu
r
e
o
u
tco
m
e
s
with
p
r
o
m
is
in
g
ac
cu
r
ac
y
[
9
]
−
[
1
2
]
,
y
et
o
f
ten
em
p
h
asize
class
if
icatio
n
p
er
f
o
r
m
an
ce
o
v
e
r
in
ter
p
r
etab
ilit
y
an
d
ev
alu
atio
n
r
ig
o
r
[
1
3
]
,
[
1
4
]
.
E
x
p
lain
a
b
ilit
y
r
em
ain
s
s
im
ilar
ly
u
n
d
er
d
e
v
elo
p
ed
,
with
f
ew
C
HF
s
tu
d
ies
lin
k
in
g
i
n
p
u
t
f
ea
tu
r
es
to
p
r
ed
i
ctio
n
s
[
1
5
]
,
[
1
6
]
,
o
r
co
m
b
in
in
g
p
r
e
d
ictiv
e
m
o
d
elin
g
with
s
tr
u
ctu
r
ed
d
ata
-
m
in
in
g
an
d
p
r
ac
tical
r
e
p
o
r
tin
g
.
T
h
is
s
tu
d
y
a
d
d
r
ess
es
th
at
g
a
p
with
a
c
r
o
s
s
-
in
d
u
s
tr
y
s
tan
d
ar
d
p
r
o
ce
s
s
f
o
r
d
ata
m
in
in
g
(
C
R
I
SP
-
DM
)
b
ased
ML
f
r
am
ewo
r
k
f
o
r
C
HF
r
is
k
p
r
ed
ictio
n
,
b
u
ild
in
g
an
d
co
m
p
ar
in
g
f
o
u
r
s
u
p
er
v
is
ed
m
o
d
els
—
d
ec
is
io
n
Evaluation Warning : The document was created with Spire.PDF for Python.
I
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2
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tell
,
Vo
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1
5
,
No
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5
,
Octo
b
er
2
0
2
6
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4
3
4
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4346
tr
ee
,
r
an
d
o
m
f
o
r
est,
g
r
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t
b
o
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s
ted
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r
ee
,
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d
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v
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B
ay
es
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o
n
clin
ical
r
is
k
-
f
ac
to
r
d
ata,
alo
n
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s
id
e
a
d
ash
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o
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m
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en
tiv
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p
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2.
L
I
T
E
R
AT
U
RE
R
E
VI
E
W
2
.
1
.
H
ea
rt
f
a
ilu
re
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HF
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a
g
r
o
u
p
o
f
s
y
m
p
to
m
s
r
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ltin
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f
r
o
m
ab
n
o
r
m
al
h
ea
r
t
m
u
s
cle
f
u
n
ctio
n
th
at
im
p
air
s
th
e
h
ea
r
t
’
s
ab
ilit
y
to
p
u
m
p
o
r
r
ec
eiv
e
b
l
o
o
d
n
o
r
m
ally
.
I
t
ca
n
b
e
class
if
ied
b
y
a
f
f
ec
ted
s
id
e,
wh
er
e
r
ig
h
t
-
s
id
ed
f
ailu
r
e
ca
u
s
es
f
lu
id
r
eten
tio
n
,
s
wo
ll
en
f
ee
t,
an
d
an
en
lar
g
ed
li
v
er
,
wh
ile
lef
t
-
s
id
ed
f
ailu
r
e
ca
u
s
es
p
u
lm
o
n
ar
y
co
n
g
esti
o
n
a
n
d
ed
em
a,
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d
b
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o
n
s
et,
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e
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u
te
f
ailu
r
e
d
ev
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p
s
r
a
p
id
ly
wh
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ch
r
o
n
ic
f
ailu
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f
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llo
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a
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d
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n
o
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C
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n
s
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m
p
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m
s
in
clu
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s
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o
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atig
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a
n
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f
lu
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s
alt
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elate
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a,
with
u
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ly
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ca
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to
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an
d
elev
ated
b
lo
o
d
lip
i
d
s
.
2
.
2
.
Da
t
a
s
et
Acc
o
r
d
in
g
t
o
th
e
W
o
r
ld
He
alth
Or
g
an
izatio
n
(
W
HO)
,
c
ar
d
io
v
ascu
lar
d
is
ea
s
es
(
C
VD
s
)
ar
e
th
e
lead
in
g
ca
u
s
e
o
f
d
ea
th
g
l
o
b
al
ly
,
ac
co
u
n
tin
g
f
o
r
an
esti
m
ate
d
1
9
.
8
m
illi
o
n
d
ea
t
h
s
in
2
0
2
2
,
wh
ich
r
ep
r
esen
ts
ap
p
r
o
x
im
ately
3
2
%
o
f
all
g
lo
b
al
d
ea
th
s
[
1
]
.
Hea
r
t
f
ailu
r
e
i
s
a
m
ajo
r
ca
teg
o
r
y
with
in
C
VDs
,
an
d
in
d
i
v
id
u
als
with
r
is
k
f
ac
to
r
s
s
u
c
h
as
h
y
p
er
ten
s
io
n
,
d
iab
etes,
elev
ated
b
lo
o
d
lip
id
lev
els,
o
r
ex
is
tin
g
ca
r
d
io
v
ascu
la
r
co
n
d
itio
n
s
r
e
q
u
ir
e
ea
r
ly
s
cr
ee
n
in
g
an
d
ap
p
r
o
p
r
iate
tr
ea
tm
en
t.
T
h
e
d
ataset
u
s
ed
f
o
r
ML
m
o
d
el
d
ev
elo
p
m
en
t
in
th
is
s
tu
d
y
was o
b
tain
ed
f
r
o
m
p
u
b
licly
av
ailab
le
h
ea
r
t d
is
ea
s
e
d
atasets
co
m
p
iled
f
r
o
m
th
e
C
lev
elan
d
,
Hu
n
g
ar
y
,
Switzer
lan
d
,
an
d
L
o
n
g
B
ea
ch
r
ep
o
s
ito
r
ies
[
1
7
]
.
T
h
ese
d
atasets
co
n
tain
1
1
clin
ical
an
d
d
em
o
g
r
ap
h
ic
attr
ib
u
tes
r
elate
d
to
h
ea
r
t
d
is
ea
s
e
r
is
k
an
d
ar
e
wid
ely
u
s
ed
in
ML
–
b
ased
ca
r
d
io
v
ascu
lar
r
ese
ar
ch
;
th
e
attr
ib
u
tes
em
p
lo
y
ed
in
th
is
s
tu
d
y
ar
e
s
u
m
m
ar
ized
in
T
a
b
le
1
.
T
ab
le
1
.
Data
s
et
ch
ar
ac
ter
is
tics
N
o
.
A
t
t
r
i
b
u
t
e
D
e
scri
p
t
i
o
n
1
A
g
e
P
a
t
i
e
n
t
’
s a
g
e
(
y
e
a
r
s)
2
S
e
x
P
a
t
i
e
n
t
’
s
g
e
n
d
e
r
(
M
:
m
a
l
e
,
F
:
f
e
m
a
l
e
)
3
C
h
e
st
P
a
i
n
T
y
p
e
Ty
p
e
o
f
c
h
e
s
t
p
a
i
n
(
TA
:
t
y
p
i
c
a
l
a
n
g
i
n
a
,
A
TA
:
a
t
y
p
i
c
a
l
a
n
g
i
n
a
,
N
A
P
:
n
o
n
-
a
n
g
i
n
a
l
p
a
i
n
,
A
S
Y
:
a
s
y
mp
t
o
ma
t
i
c
)
4
R
e
st
i
n
g
B
P
R
e
st
i
n
g
b
l
o
o
d
p
r
e
ss
u
r
e
(
mm
H
g
)
5
C
h
o
l
e
st
e
r
o
l
B
l
o
o
d
c
h
o
l
e
st
e
r
o
l
l
e
v
e
l
(
m
g
/
d
L)
6
F
a
st
i
n
g
B
S
F
a
st
i
n
g
b
l
o
o
d
su
g
a
r
(
1
:
f
a
st
i
n
g
B
S
>
1
2
0
mg
/
d
L,
0
:
o
t
h
e
r
)
7
R
e
st
i
n
g
EC
G
R
e
st
i
n
g
e
l
e
c
t
r
o
c
a
r
d
i
o
g
r
a
m
(
N
o
r
ma
l
:
n
o
r
m
a
l
,
S
T:
S
T
a
b
n
o
r
ma
l
i
t
y
,
LV
H
:
l
e
f
t
v
e
n
t
r
i
c
u
l
a
r
h
y
p
e
r
t
r
o
p
h
y
)
8
M
a
x
H
R
M
a
x
i
m
u
m
h
e
a
r
t
r
a
t
e
9
Ex
e
r
c
i
seA
n
g
i
n
a
Ex
e
r
c
i
se
-
i
n
d
u
c
e
d
c
h
e
s
t
p
a
i
n
(
Y
:
y
e
s,
N
:
n
o)
10
O
l
d
p
e
a
k
S
T
d
e
p
r
e
ss
i
o
n
v
a
l
u
e
(
0
mV
:
n
o
r
ma
l
,
0
.
1
-
1
.
0
mV
:
m
i
l
d
a
b
n
o
r
m
a
l
i
t
y
,
1
.
0
-
2
.
0
mV
:
m
o
d
e
r
a
t
e
a
b
n
o
r
m
a
l
i
t
y
,
2
.
0
-
4
.
0
mV
:
s
e
v
e
r
e
a
b
n
o
r
m
a
l
i
t
y
,
4
.
0
-
6
.
0
mV
o
r
g
r
e
a
t
e
r
:
v
e
r
y
s
e
v
e
r
e
a
b
n
o
r
m
a
l
i
t
y
)
11
S
TSlo
p
e
S
T
se
g
me
n
t
sl
o
p
e
c
h
a
r
a
c
t
e
r
i
st
i
c
s (U
p
:
u
p
w
a
r
d
,
F
l
a
t
:
f
l
a
t
,
D
o
w
n
:
d
o
w
n
w
a
r
d
)
12
H
e
a
r
t
D
i
s
e
a
s
e
C
h
a
n
c
e
o
f
o
c
c
u
r
r
e
n
c
e
(
1
:
h
e
a
r
t
d
i
se
a
s
e
,
0
:
n
o
r
ma
l
)
2
.
3
.
M
a
chine
lea
rning
ML
,
a
f
o
r
m
o
f
a
r
t
if
i
c
i
a
l
i
n
t
el
l
i
g
e
n
ce
,
e
n
ab
l
e
s
co
m
p
u
t
er
s
t
o
l
e
ar
n
p
a
t
t
e
r
n
s
f
r
o
m
d
a
t
a
a
n
d
m
ak
e
p
r
ed
i
c
t
i
o
n
s
o
r
c
l
a
s
s
i
f
i
ca
t
i
o
n
s
w
i
t
h
o
u
t
e
x
p
l
i
c
i
t
p
r
o
g
r
a
m
m
i
n
g
[
1
8
]
,
[
1
9
]
,
w
i
th
ap
p
li
c
a
t
i
o
n
s
s
p
a
n
n
i
n
g
f
o
r
ec
a
s
t
i
n
g
[
2
0
]
an
d
im
a
g
e
r
ec
o
g
n
i
t
i
o
n
[
2
1
]
.
M
L
i
s
b
r
o
ad
ly
g
r
o
u
p
ed
i
n
t
o
s
u
p
er
v
i
s
ed
l
e
ar
n
i
n
g
,
wh
i
c
h
tr
a
i
n
s
o
n
l
a
b
e
l
e
d
d
a
t
a
u
s
i
n
g
m
e
th
o
d
s
s
u
c
h
a
s
n
a
ïv
e
B
a
y
e
s
,
d
e
c
i
s
io
n
t
r
e
e
s
,
an
d
n
e
u
r
a
l
n
e
t
wo
r
k
s
f
o
r
r
eg
r
e
s
s
i
o
n
o
r
c
l
a
s
s
i
f
i
c
a
t
i
o
n
[
2
2
]
,
[
2
3
]
;
u
n
s
u
p
e
r
v
i
s
e
d
l
ea
r
n
in
g
,
wh
i
c
h
f
i
n
d
s
s
t
r
u
c
t
u
r
e
in
u
n
l
a
b
e
l
e
d
d
a
t
a
t
h
r
o
u
g
h
c
lu
s
t
e
r
in
g
me
t
h
o
d
s
;
r
e
i
n
f
o
r
c
e
m
e
n
t
l
e
ar
n
i
n
g
,
w
h
ic
h
o
p
t
i
m
i
z
e
s
a
n
ag
en
t
’
s
ac
t
i
o
n
s
to
w
a
r
d
a
g
o
a
l
v
ia
r
e
wa
r
d
f
e
e
d
b
a
ck
;
a
n
d
s
em
i
-
s
u
p
e
r
v
i
s
e
d
l
e
ar
n
in
g
,
a
h
y
b
r
i
d
o
f
t
h
e
t
w
o
.
T
h
i
s
s
t
u
d
y
i
s
g
r
o
u
n
d
ed
in
s
u
p
e
r
v
i
s
e
d
l
e
a
r
n
in
g
.
2
.
4
.
Dec
is
io
n
t
re
e
A
d
ec
is
io
n
tr
ee
is
a
tr
ee
-
s
tr
u
ctu
r
ed
m
o
d
el
in
wh
ich
a
r
o
o
t
n
o
d
e
b
r
an
c
h
es
in
to
ch
ild
n
o
d
es
,
en
d
in
g
in
leaf
n
o
d
es
th
at
r
e
p
r
esen
t
th
e
p
r
ed
icted
class
[
2
4
]
,
[
2
5
]
.
C
lass
if
icatio
n
p
r
o
ce
e
d
s
b
y
co
m
p
ar
in
g
in
p
u
t
attr
ib
u
tes
ag
ain
s
t
n
o
d
e
co
n
d
itio
n
s
f
r
o
m
th
e
r
o
o
t
d
o
wn
to
a
leaf
[
2
6
]
,
[
2
7
]
.
No
d
e
s
p
lits
ar
e
co
m
m
o
n
ly
ch
o
s
en
u
s
in
g
in
f
o
r
m
atio
n
g
ain
,
w
h
ich
s
elec
ts
th
e
attr
ib
u
te
y
ield
in
g
th
e
h
ig
h
est
r
ed
u
ctio
n
in
e
n
tr
o
p
y
at
ea
ch
s
tep
[
2
8
]
,
[
2
9
]
,
co
n
tin
u
in
g
u
n
til en
tr
o
p
y
r
ea
ch
es z
er
o
o
r
a
s
to
p
p
in
g
cr
iter
io
n
is
m
et
[
3
0
]
.
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
I
n
terp
r
eta
b
le
ma
ch
in
e
lea
r
n
i
n
g
fr
a
mewo
r
k
fo
r
co
n
g
esti
ve
h
ea
r
t fa
ilu
r
e
r
is
k
p
r
ed
ictio
n
(
S
a
k
ch
a
i Ta
n
g
p
r
a
s
ert
)
4347
2
.
5
.
B
us
ines
s
i
nte
llig
ence
B
u
s
in
ess
in
tellig
en
ce
(
B
I
)
ap
p
lies
in
f
o
r
m
atio
n
tech
n
o
l
o
g
y
t
o
s
u
p
p
o
r
t
ac
c
u
r
ate,
d
ata
-
d
r
iv
en
d
ec
is
io
n
-
m
ak
in
g
[
3
1
]
,
[
3
2
]
.
I
t
p
r
o
v
i
d
es
o
r
g
an
izatio
n
s
with
lo
ts
o
f
b
en
ef
its
,
e.
g
.
,
b
ett
er
id
en
tific
atio
n
o
f
o
p
p
o
r
tu
n
ities
an
d
r
is
k
s
,
im
p
r
o
v
ed
o
p
er
atio
n
al
ef
f
icien
cy
,
ef
f
ec
tiv
e
p
er
f
o
r
m
a
n
ce
m
o
n
it
o
r
in
g
an
d
s
tr
ateg
ic
p
lan
n
in
g
.
A
ty
p
ical
B
I
wo
r
k
f
lo
w
b
eg
in
s
b
y
id
en
tify
i
n
g
in
ter
n
al
o
r
ex
ter
n
al
d
ata
s
o
u
r
ce
s
[
3
3
]
,
[
34
]
.
Fo
llo
wed
b
y
d
ata
s
o
u
r
ce
m
a
n
ag
em
en
t
an
d
v
er
if
icatio
n
[
35
]
,
tr
an
s
f
o
r
m
atio
n
i
n
to
a
d
ata
war
eh
o
u
s
e
v
ia
an
ex
tr
ac
t,
tr
an
s
f
o
r
m
,
a
n
d
l
o
ad
(
E
T
L
)
p
r
o
ce
s
s
,
an
d
f
i
n
ally
an
aly
s
is
u
s
in
g
to
o
ls
s
u
ch
as
o
n
lin
e
a
n
aly
tical
p
r
o
ce
s
s
in
g
(
OL
AP
)
an
d
d
ata
m
in
in
g
to
g
en
er
ate
r
e
p
o
r
ts
an
d
m
u
lti
-
d
im
en
s
io
n
al
in
s
ig
h
ts
th
at
s
u
p
p
o
r
t
b
etter
b
u
s
in
ess
d
ec
is
io
n
s
[
36
]
–
[
3
8
]
.
2
.
6
.
Da
t
a
v
is
ua
liza
t
io
n
Data
v
is
u
aliza
tio
n
is
a
k
ey
co
m
p
o
n
e
n
t
o
f
B
I
th
at
e
n
ab
les
o
r
g
an
izatio
n
s
to
ef
f
ec
tiv
ely
co
m
m
u
n
icate
d
ata
-
d
r
iv
e
n
in
s
ig
h
ts
,
id
en
tify
p
atter
n
s
an
d
tr
en
d
s
,
f
ac
ilit
ate
a
b
etter
u
n
d
er
s
tan
d
in
g
o
f
co
m
p
lex
in
f
o
r
m
atio
n
,
an
d
s
u
p
p
o
r
t
ev
id
e
n
ce
-
b
ased
an
d
tim
ely
d
ec
is
io
n
-
m
ak
in
g
.
Data
v
is
u
aliza
tio
n
p
r
esen
ts
in
f
o
r
m
atio
n
th
r
o
u
g
h
ch
ar
ts
,
d
iag
r
am
s
,
an
d
g
r
a
p
h
i
cs
to
m
ak
e
co
m
p
lex
d
ata
ea
s
ier
to
in
ter
p
r
et
an
d
u
n
d
er
s
tan
d
[
3
9
]
.
A
r
elate
d
tech
n
iq
u
e,
f
o
r
ec
asti
n
g
,
u
s
es
h
is
to
r
ical
d
ata
to
p
r
e
d
ict
f
u
tu
r
e
tr
en
d
s
an
d
o
u
tc
o
m
es,
en
a
b
lin
g
o
r
g
an
izatio
n
s
to
an
ticip
ate
p
o
ten
tial c
h
an
g
es a
n
d
m
ak
e
m
o
r
e
i
n
f
o
r
m
ed
d
ec
is
i
o
n
s
[
4
0
]
,
[
4
1
]
.
3.
RE
S
E
ARCH
M
E
T
H
O
DO
L
O
G
Y
T
h
e
C
R
I
SP
-
DM
was
m
ain
ly
u
s
ed
f
o
r
s
y
s
tem
d
esig
n
an
d
web
s
ite
d
ev
elo
p
m
en
t.
I
t
is
a
s
tan
d
ar
d
p
r
o
ce
s
s
u
s
ed
in
d
ata
an
aly
s
is
an
d
d
ataset
m
an
a
g
em
en
t
f
o
r
d
ata
m
in
in
g
[
4
2
]
wh
ich
h
elp
s
d
is
co
v
er
k
n
o
wled
g
e
o
r
tr
e
n
d
s
f
r
o
m
d
ata
[
4
3
]
.
I
ts
p
r
o
ce
s
s
is
o
f
te
n
u
s
ed
t
o
s
u
p
p
o
r
t
o
r
g
a
n
izatio
n
al
d
e
cisi
o
n
-
m
ak
in
g
an
d
p
lan
n
in
g
[
4
4
]
.
C
R
I
SP
-
DM
h
as
s
ix
m
ain
s
tr
u
ctu
r
al
s
tep
s
,
i.e
.
,
b
u
s
in
ess
u
n
d
er
s
tan
d
in
g
,
d
ata
u
n
d
er
s
tan
d
in
g
,
d
ata
p
r
ep
ar
atio
n
,
m
o
d
elin
g
,
m
o
d
el
ev
alu
atio
n
,
a
n
d
d
e
p
lo
y
m
e
n
t.
3
.
1
.
B
us
ines
s
un
der
s
t
a
nd
ing
T
h
is
s
tu
d
y
d
r
aws
o
n
f
o
u
r
d
atasets
—
h
ea
r
t
f
ailu
r
e
p
r
ed
icti
o
n
d
ata
,
clin
ical
-
v
a
r
iab
le
h
ea
r
t
d
is
ea
s
e
p
r
ed
ictio
n
d
ata,
h
o
s
p
ital
m
o
r
t
ality
d
ata
with
h
ea
r
t
d
is
ea
s
e
as
th
e
p
r
im
a
r
y
ca
u
s
e,
a
n
d
Fr
am
in
g
h
am
p
r
o
ject
ca
s
e
-
s
tu
d
y
d
ata
—
to
id
e
n
tify
th
e
m
ain
r
is
k
f
ac
t
o
r
s
f
o
r
h
ea
r
t
d
is
ea
s
e.
T
h
ese
d
atasets
wer
e
a
ls
o
u
s
ed
to
ad
d
r
ess
two
r
esear
ch
q
u
esti
o
n
s
:
i)
wh
ich
s
ex
s
h
o
ws
a
h
ig
h
er
o
b
s
er
v
ed
p
r
ev
alen
ce
o
f
h
ea
r
t
f
ailu
r
e
in
t
h
e
a
n
aly
ze
d
d
ataset,
an
d
ii)
wh
at
a
r
e
th
e
k
ey
co
n
tr
i
b
u
tin
g
f
ac
to
r
s
ass
o
ciate
d
with
h
ea
r
t
f
ailu
r
e.
T
h
e
d
at
asets
,
s
o
u
r
ce
d
f
r
o
m
Kag
g
le
[
1
7
]
a
n
d
co
v
er
in
g
p
o
p
u
latio
n
s
in
C
lev
elan
d
,
Hu
n
g
ar
y
,
Switzer
lan
d
,
a
n
d
L
o
n
g
B
ea
ch
,
to
g
eth
er
co
m
p
r
is
e
4
,
9
2
7
ca
s
es
co
llected
f
r
o
m
J
an
u
ar
y
2
0
1
5
to
Feb
r
u
ar
y
2
0
2
0
ac
r
o
s
s
th
e
1
2
attr
ib
u
tes
s
u
m
m
ar
ized
in
T
ab
le
1
.
3
.
2
.
Da
t
a
un
dersta
nd
ing
Fo
llo
win
g
th
e
C
R
I
SP
-
DM
f
r
am
ewo
r
k
,
r
ec
o
r
d
s
with
m
is
s
in
g
v
alu
es
in
k
e
y
clin
ical
v
ar
i
ab
les
(
ag
e,
s
ex
,
ch
est
-
p
ain
ty
p
e
,
r
esti
n
g
b
lo
o
d
p
r
ess
u
r
e,
ch
o
lest
er
o
l,
m
ax
im
u
m
h
ea
r
t
r
ate,
a
n
d
e
lectr
o
ca
r
d
io
g
r
ap
h
ic
in
d
icato
r
s
)
wer
e
ex
clu
d
ed
to
av
o
id
p
o
ten
tially
b
iased
im
p
u
tatio
n
.
All
1
1
p
r
e
d
icto
r
attr
ib
u
tes
wer
e
r
etain
ed
,
an
d
Hea
r
tDis
ea
s
e
was
u
s
ed
as
th
e
b
in
ar
y
tar
g
et.
C
o
n
t
in
u
o
u
s
v
ar
iab
les
(
a
g
e,
r
esti
n
g
b
lo
o
d
p
r
ess
u
r
e,
ch
o
lest
er
o
l,
an
d
m
ax
im
u
m
h
e
ar
t
r
ate)
wer
e
n
o
r
m
alize
d
,
wh
er
ea
s
ca
teg
o
r
ical
v
ar
iab
les
(
s
e
x
,
ch
est
-
p
ain
ty
p
e,
r
esti
n
g
E
C
G,
ex
er
cise
-
in
d
u
ce
d
an
g
in
a
,
an
d
ST
-
s
eg
m
en
t
s
lo
p
e)
wer
e
n
u
m
er
ically
en
co
d
ed
.
No
o
v
er
-
s
am
p
lin
g
o
r
u
n
d
er
-
s
am
p
lin
g
was
ap
p
li
ed
.
T
h
e
clea
n
ed
d
ataset
was
th
en
d
iv
i
d
ed
in
t
o
7
0
%
tr
ain
in
g
d
ata
an
d
3
0
%
test
in
g
d
ata.
3.
3
.
M
o
delin
g
Mo
d
el
d
ev
elo
p
m
en
t
was
p
er
f
o
r
m
ed
in
R
ap
id
Min
e
r
Stu
d
io
u
s
in
g
th
e
Au
to
Mo
d
el
wo
r
k
f
lo
w
o
n
th
e
p
r
ep
ar
e
d
4
,
9
2
7
-
ca
s
e
d
ataset.
T
h
e
clea
n
ed
d
ata
wer
e
d
iv
i
d
ed
u
s
in
g
a
s
tr
atif
ied
7
0
:3
0
tr
ain
-
t
est
s
p
lit,
p
r
o
d
u
cin
g
3
,
4
4
9
tr
ain
in
g
ca
s
es
an
d
1
,
4
7
8
test
ca
s
es.
T
h
e
s
am
e
p
ar
titi
o
n
was
u
s
ed
f
o
r
th
e
n
aïv
e
B
ay
es,
r
an
d
o
m
f
o
r
est,
d
ec
is
io
n
tr
ee
,
a
n
d
g
r
ad
ien
t
b
o
o
s
ted
tr
ee
m
o
d
els
to
en
s
u
r
e
a
f
air
co
m
p
ar
is
o
n
.
No
o
v
er
-
s
am
p
lin
g
o
r
u
n
d
er
-
s
am
p
lin
g
was
ap
p
lied
.
T
h
e
ex
ac
t
R
ap
id
Min
er
Stu
d
io
v
er
s
io
n
,
h
ar
d
war
e
co
n
f
ig
u
r
atio
n
,
an
d
r
an
d
o
m
s
ee
d
wer
e
n
o
t
r
etain
ed
i
n
th
e
o
r
ig
in
al
ex
p
er
im
en
tal
r
ec
o
r
d
;
co
n
s
eq
u
en
tly
,
th
e
r
ep
o
r
te
d
r
es
u
lts
co
r
r
esp
o
n
d
to
th
e
ar
ch
iv
ed
d
ata
p
a
r
titi
o
n
u
s
e
d
in
th
is
s
tu
d
y
.
3.
4
.
M
o
del
ev
a
lua
t
i
o
n
T
h
e
f
o
u
r
m
o
d
els
—
n
aïv
e
B
ay
es,
r
an
d
o
m
f
o
r
est,
d
ec
is
io
n
tr
ee
,
an
d
g
r
ad
ien
t
b
o
o
s
ted
tr
ee
—
wer
e
ev
alu
ated
o
n
th
e
s
am
e
h
eld
-
o
u
t
test
s
et
u
s
in
g
ac
cu
r
ac
y
,
p
r
ec
is
io
n
,
r
ec
all,
an
d
F
-
m
ea
s
u
r
e
d
er
iv
ed
f
r
o
m
th
e
co
n
f
u
s
io
n
m
atr
ix
.
T
h
e
s
tr
atif
ied
7
0
:3
0
s
p
lit
p
r
eser
v
e
d
th
e
cl
ass
d
is
tr
ib
u
tio
n
.
R
ap
id
Min
er
Stu
d
io
was
u
s
ed
f
o
r
Evaluation Warning : The document was created with Spire.PDF for Python.
I
SS
N
:
2
2
5
2
-
8
9
3
8
I
n
t J Ar
tif
I
n
tell
,
Vo
l.
1
5
,
No
.
5
,
Octo
b
er
2
0
2
6
:
4
3
4
5
-
4
3
5
5
4348
m
o
d
el
tr
ain
in
g
an
d
p
r
ed
ictio
n
,
Mic
r
o
s
o
f
t
E
x
ce
l
f
iles
wer
e
u
s
ed
to
tr
an
s
f
er
th
e
o
u
tp
u
ts
,
an
d
Mic
r
o
s
o
f
t
Po
wer
B
I
Desk
to
p
was
u
s
ed
f
o
r
E
T
L
p
r
o
ce
s
s
in
g
,
m
u
ltid
im
en
s
io
n
al
an
aly
s
is
,
an
d
d
ash
b
o
ar
d
v
is
u
aliza
tio
n
.
Fu
tu
r
e
wo
r
k
will
in
co
r
p
o
r
ate
a
f
i
x
e
d
r
an
d
o
m
s
ee
d
,
s
o
f
twar
e
-
v
er
s
io
n
r
ep
o
r
tin
g
,
k
-
f
o
ld
cr
o
s
s
-
v
alid
atio
n
,
r
ec
eiv
e
r
o
p
er
atin
g
ch
ar
ac
ter
is
tic
-
ar
ea
u
n
d
er
t
h
e
cu
r
v
e
(
R
OC
-
AUC
)
,
p
r
ec
is
io
n
-
r
ec
all
-
ar
ea
u
n
d
er
t
h
e
cu
r
v
e
(
PR
-
AUC
)
,
an
d
ex
ter
n
al
clin
ical
d
atasets
to
s
tr
en
g
th
en
r
ep
r
o
d
u
ci
b
ilit
y
an
d
g
en
er
aliza
b
ilit
y
.
3.
5
.
Deplo
y
m
ent
Dep
lo
y
m
en
t
u
s
ed
Mic
r
o
s
o
f
t
Po
wer
B
I
Desk
to
p
to
b
u
ild
p
r
ed
ictio
n
d
ash
b
o
ar
d
s
f
r
o
m
E
x
ce
l
-
s
to
r
ed
d
ata
v
ia
an
E
T
L
p
r
o
ce
s
s
th
r
o
u
g
h
th
r
ee
m
ain
s
tep
s
.
T
h
e
f
ir
s
t
s
tep
f
o
cu
s
es
o
n
g
ath
er
in
g
s
y
s
tem
r
eq
u
ir
em
e
n
ts
an
d
s
tak
eh
o
l
d
er
n
ee
d
s
,
id
e
n
tify
in
g
k
ey
f
ac
to
r
s
s
u
ch
as
ag
e,
h
ea
r
t
r
ate,
an
d
ch
o
lest
er
o
l
f
r
o
m
R
ap
id
Min
er
o
u
tp
u
ts
.
T
h
e
s
ec
o
n
d
s
tep
in
v
o
lv
es
s
y
s
tem
an
aly
s
is
an
d
d
esig
n
.
T
h
e
last
s
tep
is
to
b
u
ild
d
as
h
b
o
ar
d
r
ep
o
r
ts
th
at
let
u
s
er
s
f
ilter
b
y
co
n
d
itio
n
s
s
u
ch
as
h
ea
r
t
-
r
ate
r
an
g
e,
s
ex
,
a
n
d
ag
e
to
r
ev
ea
l
r
is
k
tr
en
d
s
.
T
h
e
s
y
s
tem
was
th
en
test
ed
to
v
alid
ate
r
ea
l
-
u
s
er
in
te
r
ac
tio
n
s
u
n
d
e
r
ac
tu
al
u
s
ag
e
s
c
en
ar
io
s
.
4.
RE
SU
L
T
S
AND
D
I
SCU
SS
I
O
N
4
.
1
.
M
o
del
s
elec
t
io
n
Fo
u
r
p
r
ed
ictio
n
m
o
d
els
wer
e
d
ev
elo
p
e
d
in
R
ap
id
Min
e
r
u
s
i
n
g
4
,
9
2
7
r
ec
o
r
d
s
co
llected
f
r
o
m
2
0
1
5
to
2
0
2
0
,
co
m
p
r
is
in
g
3
,
1
5
5
m
ale
an
d
1
,
7
7
2
f
e
m
ale
r
ec
o
r
d
s
.
A
s
tr
atif
ied
7
0
:3
0
s
p
lit
ass
ig
n
ed
3
,
4
4
9
r
ec
o
r
d
s
to
tr
ain
in
g
a
n
d
1
,
4
7
8
r
ec
o
r
d
s
to
test
in
g
.
T
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to
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m
m
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g
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1
5
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m
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1
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1
4
4
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ad
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t
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ailu
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On
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f
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ted
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r
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l
d
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ch
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l
220
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t
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ate
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1
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m
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g
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ately
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9
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Fig
u
r
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2
.
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m
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ar
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Fig
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3
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Star
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p
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ictio
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o
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ailu
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r
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ac
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4351
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is
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r
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p
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0
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,
f
lat
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6
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4
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a)
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ale
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e
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d
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(
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C
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4
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4
.
M
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atr
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ased
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r
m
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Evaluation Warning : The document was created with Spire.PDF for Python.
I
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ased
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th
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d
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to
h
ea
lth
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r
e
p
r
ac
titi
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n
er
s
[
1
1
]
,
[
1
2
]
,
[
1
5
]
.
T
h
e
s
ex
-
s
p
ec
if
ic
s
u
m
m
ar
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s
h
o
w
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m
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5
5
;
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6
.
2
6
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a
n
f
e
m
ales
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1
2
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1
,
7
7
2
;
2
8
.
8
9
%)
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th
e
a
n
aly
ze
d
d
ata
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et.
W
h
ile
th
e
p
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ap
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p
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ely
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n
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ly
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is
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p
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ac
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n
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er
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e
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aily
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tial
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lig
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f
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la
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u
p
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ated
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atasets
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f
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r
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n
h
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ce
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ates
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R
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C
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k
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s
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-
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s
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ig
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d
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d
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en
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x
ter
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atasets
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eq
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m
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[
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.
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.
5
.
Dis
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T
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s
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d
d
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in
g
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d
co
m
p
ar
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g
f
o
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r
s
u
p
er
v
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ed
ML
m
o
d
els
—
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ec
is
io
n
tr
ee
,
r
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d
o
m
f
o
r
est,
g
r
ad
ien
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b
o
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ted
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s
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ay
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s
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f
u
s
io
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m
atr
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x
–
b
ased
p
er
f
o
r
m
an
ce
ev
alu
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n
,
af
ter
wh
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th
e
d
e
cisi
o
n
tr
ee
m
o
d
el
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s
elec
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d
as
th
e
f
in
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p
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ed
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m
o
d
e
l
b
ased
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n
b
alan
ce
d
p
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f
o
r
m
an
ce
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n
d
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f
h
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ailu
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ales
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4
4
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1
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;
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6
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2
6
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h
an
f
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ales
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5
1
2
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7
7
2
;
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8
9
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i
n
th
e
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d
d
ataset.
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th
e
p
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o
r
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i
s
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ac
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p
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k
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ased
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NF
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esear
ch
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DATA AV
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1
7
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.
RE
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NC
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[
1
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“
C
a
r
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v
a
sc
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[
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h
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3
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.
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u
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[
6
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M
.
N
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