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er
n
a
p
p
r
o
ac
h
es
s
u
ch
as
d
ee
p
lear
n
in
g
,
o
f
f
e
r
s
ig
n
if
ican
t
ad
v
an
tag
es
f
o
r
p
r
o
ce
s
s
in
g
an
d
an
aly
zin
g
lar
g
e
v
o
lu
m
es
o
f
m
ed
ical
d
ata.
In
[
1
8
]
–
[
2
0
]
,
t
h
ese
m
eth
o
d
s
ar
e
ca
p
ab
le
o
f
id
en
tif
y
in
g
n
o
n
-
o
b
v
io
u
s
r
elatio
n
s
h
ip
s
an
d
p
at
ter
n
s
in
d
ata
t
h
at
m
ay
n
o
t
b
e
av
ailab
le
with
tr
ad
itio
n
al
s
tatis
tical
ap
p
r
o
ac
h
es.
Ho
wev
er
,
d
esp
ite
p
r
o
m
is
i
n
g
r
esu
lts
,
th
e
a
p
p
licatio
n
o
f
m
ac
h
in
e
lear
n
i
n
g
in
m
ed
icin
e
f
ac
es
s
ev
er
al
ch
all
en
g
es.
T
h
ese
in
clu
d
e
is
s
u
es
o
f
m
o
d
el
in
ter
p
r
eta
b
ilit
y
,
en
s
u
r
in
g
s
u
f
f
icie
n
t
ac
cu
r
ac
y
a
n
d
r
eliab
ilit
y
o
f
p
r
e
d
ictio
n
s
,
an
d
th
e
n
ee
d
to
ad
ap
t
m
o
d
els
to
clin
ical
n
ee
d
s
.
T
h
e
ar
ticle
d
is
cu
s
s
es
th
ese
an
d
o
th
e
r
p
r
o
b
lem
s
an
d
also
s
u
g
g
ests
p
o
s
s
ib
le
s
o
lu
tio
n
s
to
th
em
.
Fro
m
a
d
ata
p
o
i
n
t
o
f
v
iew,
th
e
u
s
e
o
f
a
wid
e
r
an
g
e
o
f
clin
ical,
lab
o
r
ato
r
y
,
an
d
d
e
m
o
g
r
ap
h
ic
in
d
icato
r
s
allo
ws
f
o
r
th
e
cr
ea
tio
n
o
f
in
cr
ea
s
in
g
ly
ac
cu
r
ate
an
d
tailo
r
ed
r
is
k
m
o
d
els.
Alo
n
g
with
tr
ad
itio
n
al
r
is
k
f
ac
to
r
s
s
u
ch
as
ag
e,
g
e
n
d
er
,
m
ed
ical
h
is
to
r
y
,
c
h
o
lest
er
o
l
lev
els,
an
d
b
l
o
o
d
p
r
ess
u
r
e,
m
o
r
e
s
p
ec
if
i
c
b
io
m
ar
k
e
r
s
an
d
g
en
etic
d
ata
a
r
e
also
in
clu
d
e
d
in
th
e
a
n
aly
s
is
,
h
elp
in
g
to
im
p
r
o
v
e
th
e
p
r
ed
ictiv
e
p
o
wer
o
f
t
h
e
m
o
d
els.
I
n
th
ei
r
wo
r
k
,
Yilm
az
an
d
Yağ
in
[
2
1
]
co
n
s
id
er
th
e
p
r
o
b
lem
o
f
d
iag
n
o
s
in
g
h
ea
r
t
d
is
ea
s
es
u
s
in
g
m
ac
h
in
e
lear
n
in
g
m
eth
o
d
s
.
T
h
ey
n
o
te
th
at
ca
r
d
io
v
ascu
l
ar
d
is
ea
s
e
r
em
ain
s
a
s
ig
n
if
ican
t
ch
allen
g
e
to
h
ea
lth
ca
r
e
s
y
s
tem
s
ar
o
u
n
d
t
h
e
wo
r
ld
.
I
n
t
h
is
r
eg
ar
d
,
th
e
y
co
n
d
u
cte
d
a
s
tu
d
y
in
wh
ich
t
h
ey
co
m
p
ar
e
d
th
e
p
er
f
o
r
m
an
ce
o
f
t
h
r
ee
d
if
f
e
r
e
n
t
m
o
d
els:
r
an
d
o
m
f
o
r
est
(
R
F),
lo
g
is
tic
r
eg
r
ess
io
n
(
L
R
)
,
an
d
s
u
p
p
o
r
t
v
ec
to
r
m
ac
h
in
e
(
SVM)
f
o
r
class
if
y
i
n
g
c
o
r
o
n
ar
y
h
ea
r
t
d
is
ea
s
es.
B
h
att
et
a
l.
[
2
2
]
d
is
cu
s
s
es
th
e
im
p
o
r
tan
ce
o
f
d
iag
n
o
s
in
g
an
d
p
r
o
g
n
o
s
ticatin
g
ca
r
d
io
v
ascu
lar
d
is
ea
s
es
to
en
s
u
r
e
co
r
r
ec
t
class
if
icatio
n
an
d
ap
p
r
o
p
r
iate
tr
ea
tm
en
t
f
o
r
p
atien
ts
.
T
h
e
a
u
th
o
r
s
p
r
o
p
o
s
e
th
e
u
s
e
o
f
th
e
k
-
m
o
d
e
clu
s
ter
in
g
m
eth
o
d
with
Hu
an
g
'
s
in
itial
ap
p
r
o
x
im
atio
n
to
im
p
r
o
v
e
clas
s
if
icatio
n
ac
cu
r
ac
y
u
s
in
g
m
ac
h
in
e
lear
n
in
g
.
Usi
n
g
m
o
d
els
s
u
ch
as
R
F,
d
ec
is
io
n
tr
ee
(
DT
)
,
m
u
ltil
ay
er
p
er
ce
p
tr
o
n
(
MP)
,
an
d
ex
tr
em
e
g
r
a
d
ie
n
t
b
o
o
s
tin
g
(
XGBo
o
s
t)
o
n
a
r
ea
l
Kag
g
le
d
ataset,
we
ac
h
iev
ed
h
ig
h
ac
c
u
r
ac
y
r
a
tes
ex
ce
ed
in
g
8
7
%,
an
d
th
e
b
est
p
er
f
o
r
m
an
ce
was
d
em
o
n
s
tr
ated
b
y
Mu
ltil
ay
er
p
er
ce
p
tr
o
n
with
cr
o
s
s
-
v
alid
ati
o
n
,
ac
h
ie
v
in
g
a
n
ac
cu
r
ac
y
o
f
8
7
.
2
8
%.
T
h
is
s
tu
d
y
[
2
3
]
f
o
c
u
s
es
o
n
th
e
d
ev
elo
p
m
en
t
o
f
a
wea
r
ab
le
b
io
m
ed
ical
p
r
o
to
ty
p
e
f
o
r
p
r
ed
ictin
g
th
e
p
r
esen
ce
o
f
ca
r
d
io
v
ascu
lar
d
is
ea
s
es.
T
h
e
r
esu
lts
o
f
th
is
s
tu
d
y
will
b
e
esp
ec
ially
im
p
o
r
tan
t
in
co
u
n
tr
ies
wh
er
e
th
e
d
o
cto
r
-
to
-
p
atien
t
r
atio
is
ex
tr
em
ely
lo
w,
as
wea
r
a
b
le
tech
n
o
lo
g
y
ca
n
b
e
u
s
ed
t
o
m
o
n
ito
r
p
atien
t
p
ar
am
eter
s
an
y
wh
er
e,
n
o
t
lim
ited
to
th
e
h
o
s
p
ital
en
v
ir
o
n
m
en
t.
T
h
e
g
o
al
is
to
p
r
e
d
ict
th
e
p
o
s
s
ib
ilit
y
o
f
ca
r
d
io
v
ascu
lar
d
is
ea
s
e
u
s
in
g
m
ac
h
in
e
lear
n
in
g
alg
o
r
ith
m
s
b
ased
o
n
th
e
an
aly
s
is
o
f
elec
t
r
o
ca
r
d
io
g
r
am
s
.
T
h
e
p
r
o
p
o
s
ed
p
r
o
to
ty
p
e
with
th
e
r
an
d
o
m
f
o
r
est
alg
o
r
ith
m
s
h
o
wed
a
p
r
e
d
ictio
n
e
f
f
icien
cy
o
f
8
8
%,
wh
ich
c
o
n
f
ir
m
s
its
p
o
ten
tial
as
an
aid
f
o
r
o
ld
e
r
p
eo
p
le.
L
i
et
a
l.
[
2
4
]
p
r
o
p
o
s
e
s
an
ef
f
icie
n
t
an
d
ac
cu
r
ate
s
y
s
tem
f
o
r
d
iag
n
o
s
in
g
h
ea
r
t
d
is
ea
s
es
b
ased
o
n
m
ac
h
in
e
lear
n
in
g
tech
n
iq
u
es.
T
h
e
w
o
r
k
u
s
es
v
ar
io
u
s
class
if
icatio
n
alg
o
r
ith
m
s
s
u
ch
as
s
u
p
p
o
r
t
v
ec
t
o
r
m
ac
h
i
n
e,
lo
g
is
tic
r
eg
r
ess
io
n
,
ar
tific
ial
n
eu
r
al
n
etwo
r
k
s
,
n
ea
r
est
n
eig
h
b
o
r
s
,
n
aiv
e
B
ay
es
class
if
ier
,
an
d
d
ec
is
io
n
tr
ee
,
as
well
as
f
ea
tu
r
e
s
elec
tio
n
alg
o
r
ith
m
s
to
im
p
r
o
v
e
th
e
class
if
icatio
n
ac
cu
r
ac
y
a
n
d
r
ed
u
ce
t
h
e
ex
ec
u
tio
n
tim
e
o
f
t
h
e
s
y
s
tem
.
A
n
ew
f
ea
tu
r
e
s
ele
ctio
n
alg
o
r
ith
m
b
ased
o
n
f
ast
co
n
d
itio
n
al
m
u
tu
al
in
f
o
r
m
atio
n
(
FC
MI
M)
h
as
b
ee
n
p
r
o
p
o
s
ed
an
d
s
h
o
wn
to
b
e
ef
f
ec
tiv
e
wh
en
co
m
b
in
ed
wit
h
a
s
u
p
p
o
r
t
v
ec
to
r
m
ac
h
in
e
class
if
ier
to
cr
ea
te
a
h
ig
h
-
le
v
el
in
tellig
en
t
s
y
s
tem
f
o
r
h
ea
r
t
d
is
ea
s
e
id
en
tific
atio
n
.
E
x
p
er
im
en
ta
l
r
esu
lts
co
n
f
ir
m
th
at
th
e
p
r
o
p
o
s
ed
d
iag
n
o
s
tic
s
y
s
tem
ac
h
iev
es
g
o
o
d
ac
cu
r
ac
y
co
m
p
ar
ed
with
p
r
ev
io
u
s
ly
p
r
o
p
o
s
ed
m
eth
o
d
s
an
d
ca
n
b
e
ea
s
ily
im
p
lem
en
ted
in
h
ea
lth
ca
r
e
to
id
en
tify
h
ea
r
t
d
is
ea
s
es.
An
s
ar
i
et
a
l.
[
2
5
]
ad
d
r
ess
es
th
e
g
r
o
win
g
p
r
o
b
le
m
o
f
h
ea
r
t
d
is
ea
s
e,
wh
ich
is
b
ec
o
m
in
g
o
n
e
o
f
th
e
d
ea
d
lies
t
d
is
ea
s
es
n
o
wad
ay
s
.
R
esear
ch
er
s
d
escr
ib
e
th
e
n
ee
d
to
d
ev
elo
p
ea
r
ly
d
iag
n
o
s
is
o
f
h
ea
r
t
d
is
ea
s
e
with
g
r
ea
ter
ac
cu
r
ac
y
,
g
iv
en
th
at
ab
o
u
t
1
2
m
illi
o
n
p
eo
p
le
d
ie
f
r
o
m
it
ev
e
r
y
y
ea
r
.
T
h
e
p
a
p
er
ap
p
lies
v
ar
io
u
s
m
ac
h
in
e
lear
n
in
g
alg
o
r
ith
m
s
an
d
p
r
o
p
o
s
es
a
m
o
d
if
ied
alg
o
r
ith
m
u
s
in
g
lo
g
is
tic
r
eg
r
ess
io
n
w
ith
p
r
in
cip
al
co
m
p
o
n
e
n
t
an
al
y
s
is
to
p
r
ed
ict
h
ea
r
t
d
is
ea
s
e
with
g
r
ea
ter
ac
cu
r
ac
y
b
ased
o
n
v
a
r
io
u
s
c
h
ar
ac
ter
is
tic
f
ea
tu
r
es
s
u
c
h
as
a
g
e,
b
lo
o
d
p
r
ess
u
r
e,
ch
est
p
ain
,
b
lo
o
d
c
h
o
lest
er
o
l,
an
d
o
th
e
r
s
.
I
n
s
u
m
m
ar
y
,
t
h
is
r
ev
iew
h
ig
h
lig
h
ts
th
e
im
p
o
r
tan
ce
o
f
in
te
g
r
atin
g
m
ac
h
in
e
lear
n
in
g
tech
n
iq
u
es
in
to
ca
r
d
io
lo
g
y
p
r
ac
tice
an
d
r
e
p
r
e
s
en
ts
a
co
n
tr
ib
u
tio
n
t
o
th
e
d
e
v
elo
p
m
en
t
o
f
p
er
s
o
n
alize
d
m
ed
icin
e
ca
p
a
b
le
o
f
p
r
o
v
id
i
n
g
ac
cu
r
ate
a
n
d
tim
ely
s
o
lu
tio
n
s
f
o
r
ca
r
d
iac
h
ea
lth
m
an
ag
em
en
t.
I
n
o
u
r
s
cien
tific
wo
r
k
,
we
d
if
f
e
r
f
r
o
m
o
th
er
s
tu
d
ies
in
th
at
we
p
l
ac
e
s
p
ec
ial
em
p
h
asis
o
n
an
aly
zin
g
th
e
r
elatio
n
s
h
ip
s
b
et
wee
n
b
io
ch
em
ical
p
ar
am
eter
s
an
d
th
eir
im
p
ac
t
o
n
h
ea
lth
.
W
e
co
n
s
id
er
a
wi
d
e
r
an
g
e
o
f
f
ac
to
r
s
,
in
clu
d
in
g
ag
e,
c
h
o
lest
er
o
l,
f
er
r
itin
,
asp
ar
tate
am
in
o
tr
a
n
s
f
er
ase
(
AST)
,
h
o
m
o
cy
s
tein
e,
a
n
d
a
tar
g
et
v
ar
iab
le
th
at
r
e
f
lects
th
e
p
r
esen
ce
o
r
ab
s
en
ce
o
f
ca
r
d
io
v
ascu
lar
d
i
s
ea
s
e
r
is
k
.
T
h
is
m
u
lti
-
p
r
o
n
g
e
d
ap
p
r
o
ac
h
allo
ws
d
ee
p
er
c
o
r
r
elatio
n
s
b
etwe
en
th
ese
in
d
icato
r
s
to
b
e
id
en
tifie
d
an
d
ex
p
lo
r
e
d
,
p
r
o
v
id
in
g
a
u
n
iq
u
e
o
p
p
o
r
tu
n
ity
to
u
n
d
er
s
tan
d
an
d
p
r
ed
ic
t
p
o
ten
tial
r
is
k
s
.
O
u
r
f
o
cu
s
o
n
ex
p
lo
r
in
g
th
ese
c
o
n
n
ec
tio
n
s
in
d
etail
m
ak
es
o
u
r
wo
r
k
e
s
p
ec
ially
u
s
ef
u
l
f
o
r
s
h
ap
in
g
p
r
e
v
en
tio
n
an
d
tr
ea
tm
en
t stra
teg
ies.
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
7
3
4
-
6
7
4
2
6736
2.
M
E
T
H
O
D
T
h
is
s
tu
d
y
ex
am
in
ed
two
d
if
f
er
en
t
ap
p
r
o
ac
h
es
to
b
u
ild
in
g
a
n
d
tr
ain
in
g
p
r
e
d
ictiv
e
m
o
d
els:
o
n
e
b
ased
o
n
th
e
XGBo
o
s
t
alg
o
r
ith
m
an
d
th
e
o
th
er
u
s
in
g
a
co
n
v
o
l
u
tio
n
al
n
eu
r
al
n
etwo
r
k
(
C
NN)
o
n
th
e
T
en
s
o
r
Flo
w
p
latf
o
r
m
.
T
h
e
m
ain
g
o
al
was
to
co
m
p
ar
e
th
eir
p
er
f
o
r
m
a
n
ce
o
n
class
if
icatio
n
an
d
r
eg
r
ess
io
n
p
r
o
b
lem
s
u
s
in
g
d
ata
im
p
o
r
ted
f
r
o
m
E
x
ce
l
f
iles
.
T
h
e
f
o
llo
win
g
p
ar
am
eter
s
wer
e
s
et
f
o
r
th
e
XGBo
o
s
t
m
o
d
el.
′
:
′
was
ch
o
s
en
as
th
e
lo
s
s
f
u
n
ctio
n
to
m
in
im
ize
th
e
s
q
u
ar
ed
e
r
r
o
r
in
r
eg
r
ess
io
n
p
r
o
b
lem
s
.
T
h
e
p
r
o
p
o
r
tio
n
o
f
f
ea
tu
r
es
r
an
d
o
m
ly
s
elec
ted
f
o
r
ea
ch
tr
ee
(
_
)
was
s
et
to
0
.
3
,
wh
ich
h
elp
s
r
ed
u
ce
o
v
e
r
f
itti
n
g
an
d
im
p
r
o
v
e
th
e
g
en
er
aliza
tio
n
ab
ilit
y
o
f
th
e
m
o
d
el.
T
h
e
lear
n
in
g
r
ate
was
s
et
to
0
.
1
,
en
s
u
r
in
g
s
m
o
o
th
an
d
co
n
tr
o
lled
lear
n
in
g
.
T
h
e
d
ep
th
o
f
ea
c
h
tr
ee
(
_
ℎ
)
was
lim
ited
to
f
iv
e
lev
els
to
co
n
tr
o
l m
o
d
el
co
m
p
lex
ity
,
a
n
d
th
e
n
u
m
b
e
r
o
f
tr
ee
s
(
_
)
was
s
et
to
1
0
0
to
ac
h
iev
e
s
u
f
f
icien
t
d
ep
th
o
f
d
ata
an
al
y
s
is
.
Du
r
in
g
th
e
tr
ain
in
g
p
r
o
ce
s
s
,
b
o
th
tr
ain
i
n
g
a
n
d
v
alid
atio
n
d
ata
s
ets
wer
e
u
s
ed
,
wh
ich
m
ad
e
it
p
o
s
s
ib
le
to
tr
ac
k
o
v
er
f
itti
n
g
u
s
in
g
th
e
r
o
o
t m
ea
n
s
q
u
ar
e
e
r
r
o
r
(
R
MSE
)
m
etr
ic
as sh
o
wn
in
Fig
u
r
e
1.
Fig
u
r
e
1
.
XGBo
o
s
t
m
o
d
el
a
r
c
h
itectu
r
e
T
h
e
p
ar
am
eter
s
o
f
th
e
C
NN
m
o
d
el
wer
e
ch
o
s
en
as
f
o
llo
ws.
T
h
e
ar
ch
itectu
r
e
in
clu
d
ed
co
n
v
o
lu
tio
n
al
lay
er
s
,
s
u
b
s
am
p
lin
g
lay
er
s
(
Ma
x
Po
o
lin
g
)
,
f
latten
lay
e
r
(
Fl
atten
)
,
an
d
f
u
lly
co
n
n
ec
ted
la
y
er
s
(
Den
s
e)
,
wh
ic
h
ar
e
th
e
s
tan
d
ar
d
ch
o
ice
f
o
r
p
r
o
ce
s
s
in
g
im
ag
es
o
r
s
tr
u
ctu
r
e
d
d
ata.
R
eL
U
was
u
s
ed
as
a
n
ac
tiv
atio
n
f
u
n
ctio
n
o
n
th
e
co
n
v
o
lu
tio
n
al
an
d
f
u
lly
co
n
n
ec
ted
lay
er
s
to
p
r
ev
en
t
s
h
al
lo
w
g
r
ad
ien
ts
an
d
im
p
r
o
v
e
t
h
e
lear
n
in
g
p
r
o
ce
s
s
,
an
d
a
s
ig
m
o
id
f
u
n
ctio
n
was
u
s
ed
o
n
th
e
o
u
tp
u
t
lay
er
,
s
u
ita
b
le
f
o
r
b
in
a
r
y
class
if
icatio
n
p
r
o
b
lem
s
.
Ad
am
was
ch
o
s
en
as
th
e
o
p
tim
izer
d
u
e
to
its
ef
f
ec
tiv
en
ess
in
ad
ju
s
tin
g
th
e
lear
n
in
g
r
ate
b
ased
o
n
th
e
f
ir
s
t
an
d
s
ec
o
n
d
m
o
m
en
ts
o
f
g
r
ad
ie
n
ts
.
T
en
ep
o
ch
s
with
v
alid
atio
n
d
ata
wer
e
u
s
ed
to
tr
ain
th
e
m
o
d
el,
wh
ich
m
ad
e
it
p
o
s
s
ib
le
to
tr
ac
k
o
v
er
tr
ain
in
g
an
d
ev
alu
ate
th
e
p
r
o
g
r
ess
o
f
th
e
m
o
d
el
a
s
s
h
o
wn
in
Fig
u
r
e
2.
Fig
u
r
e
2
.
Van
illa
C
NN
m
o
d
el
ar
ch
itectu
r
e
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
s
es
s
in
g
r
is
k
fa
cto
r
s
fo
r
h
ea
r
t d
is
ea
s
e
u
s
in
g
ma
ch
in
e
lea
r
n
i
n
g
meth
o
d
s
…
(
N
a
ta
lya
Ma
xu
t
o
va
)
6737
T
h
e
p
er
f
o
r
m
a
n
ce
ev
alu
atio
n
p
r
o
ce
s
s
f
o
r
b
o
th
m
o
d
els
u
s
ed
MSE
an
d
R
²
m
etr
ics
to
an
aly
z
e
p
r
ed
ictio
n
ac
cu
r
ac
y
a
n
d
m
o
d
el
f
it
t
o
th
e
d
ata.
I
n
a
d
d
itio
n
,
th
e
AI
C
an
d
B
I
C
m
etr
ics
wer
e
u
s
ed
to
co
m
p
ar
e
th
e
q
u
ality
o
f
m
o
d
els
tak
in
g
in
to
ac
co
u
n
t
th
eir
co
m
p
lex
ity
.
T
h
e
u
s
e
o
f
a
v
a
r
iety
o
f
m
etr
ics
allo
w
ed
f
o
r
an
in
-
d
ep
th
ass
es
s
m
en
t o
f
m
o
d
el
p
er
f
o
r
m
a
n
ce
,
wh
ich
m
ad
e
it p
o
s
s
ib
le
to
id
en
tify
ar
ea
s
f
o
r
f
u
r
t
h
er
o
p
ti
m
izatio
n
an
d
s
elec
t
p
ar
am
eter
s
th
at
ar
e
m
o
s
t
s
u
itab
le
f
o
r
s
o
lv
in
g
r
esear
ch
p
r
o
b
lem
s
.
T
h
is
ap
p
r
o
ac
h
p
r
o
v
id
e
d
a
m
o
r
e
co
m
p
lete
u
n
d
er
s
tan
d
i
n
g
o
f
t
h
e
ef
f
ec
tiv
e
n
ess
o
f
th
e
m
o
d
els
an
d
th
eir
a
p
p
licab
ilit
y
to
s
p
ec
if
ic
p
r
o
b
le
m
s
,
f
ac
ilit
atin
g
th
e
d
ev
elo
p
m
e
n
t o
f
m
eth
o
d
o
lo
g
y
an
d
im
p
r
o
v
in
g
r
esear
ch
r
esu
lts
.
3.
RE
SU
L
T
S AN
D
D
I
SCU
SS
I
O
N
I
n
th
is
wo
r
k
,
we
u
s
ed
a
d
ataset
th
at
co
n
tain
s
1
0
,
0
0
0
r
ec
o
r
d
s
,
d
iv
i
d
ed
i
n
to
s
ix
c
o
lu
m
n
s
.
T
h
e
d
ata
r
ep
r
esen
ts
v
ar
io
u
s
b
io
m
ar
k
er
s
an
d
d
em
o
g
r
ap
h
ic
in
f
o
r
m
atio
n
th
at
ca
n
b
e
u
s
ed
to
an
aly
ze
r
is
k
f
ac
to
r
s
f
o
r
h
ea
r
t
d
is
ea
s
e.
T
h
e
f
ir
s
t
co
l
u
m
n
in
d
icate
s
th
e
p
atien
t'
s
g
en
d
er
,
with
in
d
icato
r
s
1
f
o
r
m
en
a
n
d
2
f
o
r
wo
m
e
n
.
T
h
e
s
ec
o
n
d
co
lu
m
n
in
clu
d
es
in
f
o
r
m
atio
n
ab
o
u
t
th
e
p
atien
t'
s
ag
e
in
y
ea
r
s
.
C
h
o
lest
er
o
l
d
ata
p
r
esen
ted
in
th
e
th
ir
d
co
lu
m
n
in
d
icate
s
th
e
lev
el
o
f
th
is
in
d
icato
r
in
th
e
b
lo
o
d
,
m
ea
s
u
r
ed
in
m
m
o
l/l.
T
h
e
f
o
u
r
th
co
lu
m
n
c
o
n
tain
s
d
ata
o
n
th
e
lev
el
o
f
f
er
r
itin
in
th
e
b
lo
o
d
,
m
ea
s
u
r
ed
in
n
g
/m
l.
T
h
is
in
d
icato
r
s
er
v
es
as
a
m
ar
k
er
o
f
in
f
lam
m
atio
n
an
d
is
p
o
ten
tially
ass
o
ciate
d
with
th
e
r
is
k
s
o
f
ca
r
d
io
v
ascu
lar
d
is
ea
s
e.
T
h
e
f
if
th
co
lu
m
n
i
n
clu
d
es
th
e
b
lo
o
d
lev
el
o
f
asp
ar
tate
a
m
in
o
tr
an
s
f
er
ase
(
AST)
,
m
ea
s
u
r
ed
in
U/L
,
wh
ich
is
an
en
zy
m
e
in
v
o
lv
ed
in
am
in
o
ac
id
m
etab
o
lis
m
.
T
h
e
last
co
lu
m
n
co
n
tain
s
in
f
o
r
m
atio
n
ab
o
u
t
th
e
lev
el
o
f
h
o
m
o
cy
s
tein
e
in
th
e
b
lo
o
d
,
m
ea
s
u
r
ed
in
µm
o
l/l.
Ho
m
o
cy
s
tein
e
is
an
am
in
o
ac
id
ass
o
ciate
d
with
th
e
r
is
k
o
f
ca
r
d
io
v
a
s
cu
lar
d
is
ea
s
e.
T
h
is
d
ataset
m
ay
b
e
p
ar
ticu
lar
l
y
u
s
ef
u
l
f
o
r
b
u
ild
in
g
an
d
test
in
g
m
ac
h
in
e
lear
n
in
g
m
o
d
els
th
at
p
r
ed
ict
h
ea
r
t
d
is
ea
s
e
r
is
k
b
ased
o
n
b
i
o
m
ar
k
e
r
s
an
d
d
em
o
g
r
a
p
h
ic
c
h
ar
ac
ter
is
tics
.
B
y
an
aly
zin
g
th
ese
p
ar
a
m
eter
s
,
alg
o
r
ith
m
s
ca
n
b
e
d
ev
elo
p
e
d
th
at
ca
n
id
en
tify
p
a
tien
ts
at
in
cr
ea
s
ed
r
is
k
at
an
e
ar
ly
s
tag
e.
T
h
is
will
allo
w
u
s
to
f
o
cu
s
o
u
r
ef
f
o
r
ts
o
n
p
r
e
v
en
tin
g
d
is
ea
s
es a
n
d
im
p
r
o
v
in
g
th
e
o
v
er
all
h
ea
lth
o
f
t
h
e
p
o
p
u
latio
n
.
T
o
estab
lis
h
p
r
ed
ictiv
e
m
o
d
els
o
f
ca
r
d
io
v
ascu
lar
d
is
ea
s
e,
it
is
cr
itical
to
d
eter
m
in
e
th
r
esh
o
ld
v
alu
es
f
o
r
k
e
y
b
i
o
m
ar
k
e
r
s
.
T
h
e
f
o
l
lo
win
g
th
r
esh
o
l
d
lev
els
wer
e
u
s
ed
in
th
is
wo
r
k
:
f
e
r
r
itin
ab
o
v
e
1
0
0
n
g
/m
l,
h
o
m
o
c
y
s
tein
e
ab
o
v
e
1
5
µm
o
l/l,
AST
(
asp
ar
tate
am
in
o
tr
an
s
f
er
ase)
ab
o
v
e
5
0
U/l,
ch
o
lest
er
o
l
ab
o
v
e
8
m
m
o
l/l
an
d
g
l
u
co
s
e
ab
o
v
e
7
m
m
o
l/l.
T
h
ese
th
r
esh
o
l
d
s
wer
e
estab
l
is
h
ed
b
ased
o
n
liter
atu
r
e
an
d
clin
ical
g
u
id
elin
es
th
at
co
r
r
elate
with
a
n
in
c
r
ea
s
ed
r
is
k
o
f
ca
r
d
i
o
v
ascu
lar
d
is
ea
s
e.
E
x
ce
ed
in
g
th
ese
th
r
esh
o
l
d
s
allo
ws
p
atien
ts
to
b
e
class
if
ied
ac
co
r
d
in
g
to
t
h
ei
r
r
is
k
o
f
d
ev
elo
p
in
g
ca
r
d
i
o
v
ascu
lar
d
is
ea
s
e
u
s
in
g
a
b
in
ar
y
s
y
s
tem
.
“
0
”
in
d
icate
s
n
o
r
m
al
(
lo
w
r
is
k
)
an
d
“
1
”
in
d
icate
s
r
is
k
(
h
ig
h
r
is
k
)
.
T
h
is
ap
p
r
o
ac
h
n
o
t
o
n
l
y
s
im
p
lifie
s
th
e
d
ata
an
aly
s
is
p
r
o
ce
s
s
b
u
t
also
in
cr
ea
s
es
its
v
is
ib
ilit
y
an
d
p
r
ac
tical
ap
p
lica
b
ilit
y
in
clin
ical
s
ettin
g
s
.
Data
b
in
ar
izatio
n
allo
ws
th
e
u
s
e
o
f
v
ar
io
u
s
s
tatis
tica
l
a
n
d
m
ac
h
in
e
m
et
h
o
d
s
to
id
e
n
tify
th
e
m
o
s
t
s
ig
n
if
ican
t
r
is
k
f
ac
t
o
r
s
an
d
f
o
r
ec
asti
n
g
m
o
d
els.
T
h
is
m
ay
in
clu
d
e
lo
g
is
tic
r
eg
r
ess
io
n
,
d
ec
is
io
n
tr
e
es,
an
d
en
s
em
b
le
m
eth
o
d
s
th
at
p
r
o
v
id
e
n
o
t
o
n
ly
class
if
icatio
n
ac
cu
r
ac
y
b
u
t
also
th
e
ab
ilit
y
t
o
in
te
r
p
r
et
m
o
d
e
ls
in
ter
m
s
o
f
th
e
c
o
n
tr
ib
u
tio
n
o
f
ea
ch
b
io
m
ar
k
er
to
d
is
ea
s
e
r
is
k
.
Ad
ju
s
tin
g
th
r
esh
o
ld
s
b
ased
o
n
an
aly
s
is
o
f
r
ea
l
p
atien
t
d
ata
h
elp
s
r
ef
in
e
p
r
ed
ictio
n
m
o
d
els,
m
ak
in
g
th
em
m
o
r
e
s
en
s
itiv
e
to
ea
r
l
y
s
ig
n
s
o
f
p
ath
o
lo
g
y
.
T
h
is
is
esp
ec
ially
im
p
o
r
ta
n
t
in
p
er
s
o
n
alize
d
m
ed
icin
e,
wh
e
r
e
in
d
iv
id
u
al
p
atien
t
ch
ar
ac
ter
is
tics
m
ay
in
f
l
u
en
ce
r
is
k
s
a
n
d
r
eq
u
ir
e
ad
ju
s
tm
en
ts
to
s
tan
d
ar
d
th
r
esh
o
ld
s
.
T
h
u
s
,
th
e
a
p
p
licatio
n
o
f
estab
lis
h
ed
th
r
esh
o
ld
s
i
n
m
ac
h
in
e
lear
n
in
g
m
o
d
els
is
a
k
ey
s
tep
t
o
war
d
s
m
o
r
e
ac
cu
r
ate
an
d
p
er
s
o
n
alize
d
d
iag
n
o
s
is
o
f
ca
r
d
io
v
ascu
lar
d
is
ea
s
es.
C
o
r
r
elatio
n
an
aly
s
is
in
Fig
u
r
e
3
is
a
f
u
n
d
am
en
tal
s
tatis
tic
al
to
o
l
th
at
allo
ws
o
n
e
to
ex
am
in
e
th
e
ex
ten
t
an
d
n
atu
r
e
o
f
r
elatio
n
s
h
ip
s
b
etwe
en
v
ar
io
u
s
v
ar
iab
le
s
.
I
n
th
e
s
tu
d
y
o
f
r
is
k
f
ac
to
r
s
f
o
r
ca
r
d
io
v
ascu
lar
d
is
ea
s
es,
s
p
ec
ial
at
ten
tio
n
is
p
aid
to
th
e
an
aly
s
is
o
f
co
n
n
e
ctio
n
s
b
etwe
en
b
io
ch
em
ical
p
ar
am
eter
s
an
d
th
eir
im
p
ac
t
o
n
h
ea
lth
s
tatu
s
.
T
h
is
an
aly
s
is
ex
am
in
es
ag
e,
ch
o
lest
er
o
l,
f
er
r
itin
,
AST,
h
o
m
o
cy
s
tein
e,
an
d
a
tar
g
et
v
ar
iab
le
th
at
r
ef
lects th
e
p
r
esen
ce
o
r
a
b
s
en
ce
o
f
ca
r
d
io
v
ascu
lar
d
is
ea
s
e
r
is
k
.
T
h
e
s
ig
n
if
ican
t
p
o
s
itiv
e
co
r
r
el
atio
n
b
etwe
en
h
o
m
o
cy
s
tein
e
a
n
d
th
e
tar
g
et
v
ar
ia
b
le
(
0
.
4
8
)
i
n
d
icate
s
a
p
o
s
s
ib
le
d
ir
ec
t
ef
f
ec
t
o
f
h
o
m
o
cy
s
tein
e
lev
els
o
n
th
e
r
is
k
o
f
ca
r
d
io
v
ascu
lar
d
is
ea
s
e.
Hig
h
lev
els
o
f
th
is
am
in
o
ac
id
ar
e
ass
o
ciate
d
with
d
am
ag
e
to
th
e
v
ascu
lar
wall
an
d
m
ay
co
n
tr
ib
u
te
to
th
e
d
ev
elo
p
m
en
t
o
f
ath
er
o
s
cler
o
s
is
.
T
h
e
p
o
s
itiv
e
co
r
r
elatio
n
b
etwe
en
AC
T
an
d
th
e
tar
g
et
v
ar
iab
le
(
0
.
4
1
)
is
also
s
ig
n
if
ican
t.
AS
T
is
an
en
zy
m
e
f
o
u
n
d
in
s
id
e
ce
l
ls
,
b
u
t
elev
ated
lev
els
in
th
e
b
lo
o
d
ca
n
in
d
icate
ce
ll
d
ea
th
,
in
d
icatin
g
p
o
s
s
ib
le
d
am
ag
e
to
th
e
h
ea
r
t
m
u
s
cle
o
r
liv
er
.
Mo
d
er
ate
c
o
r
r
elatio
n
s
b
etwe
en
f
er
r
itin
an
d
th
e
tar
g
et
v
ar
iab
le
(
0
.
1
5
)
a
n
d
b
etwe
en
ch
o
lest
er
o
l
a
n
d
th
e
tar
g
et
v
ar
iab
le
(
0
.
1
3
)
s
u
g
g
est
t
h
at
th
ese
b
io
m
ar
k
er
s
m
ay
b
e
ass
o
ciate
d
with
th
e
r
is
k
o
f
d
ev
elo
p
i
n
g
ca
r
d
io
v
ascu
lar
d
is
ea
s
es.
Ho
wev
er
,
h
ig
h
f
er
r
itin
lev
els,
alth
o
u
g
h
ass
o
c
iated
with
r
is
k
,
ar
e
n
o
t
an
in
d
e
p
en
d
e
n
t
p
r
e
d
icto
r
an
d
lik
ely
r
e
f
lect
a
g
en
er
al
in
f
lam
m
ato
r
y
s
tate
o
f
th
e
b
o
d
y
.
T
h
e
ch
o
lest
er
o
l
co
r
r
elatio
n
h
i
g
h
lig
h
ts
its
im
p
ac
t
o
n
th
e
ca
r
d
io
v
ascu
lar
s
y
s
tem
,
b
u
t
th
e
r
elativ
ely
lo
w
c
o
ef
f
icien
t
in
d
icate
s
th
e
im
p
o
r
tan
ce
o
f
n
o
t o
n
l
y
to
tal
c
h
o
lest
er
o
l le
v
els b
u
t a
ls
o
th
e
r
atio
o
f
its
f
r
ac
tio
n
s
.
T
h
e
co
r
r
elatio
n
b
etwe
en
ag
e
a
n
d
th
e
tar
g
et
v
ar
iab
le
(
0
.
1
1
)
w
as f
o
u
n
d
to
b
e
lo
w,
wh
ich
m
a
y
b
e
d
u
e
to
th
e
v
ar
iety
o
f
b
io
lo
g
ical
an
d
s
o
cial
f
ac
to
r
s
in
f
l
u
en
cin
g
t
h
e
h
ea
lth
o
f
i
n
d
iv
id
u
als.
T
h
e
lo
w
to
m
o
d
er
ate
co
r
r
elatio
n
s
b
etwe
en
f
e
r
r
itin
an
d
AST
(
0
.
1
6
)
an
d
b
etwe
en
f
er
r
itin
an
d
c
h
o
lest
er
o
l
(
0
.
2
0
)
d
em
o
n
s
tr
ate
th
e
co
m
p
lex
ity
o
f
th
e
in
ter
ac
tio
n
s
in
th
e
b
io
lo
g
ical
p
ath
way
s
in
v
o
lv
ed
in
t
h
e
d
ev
elo
p
m
en
t
o
f
ca
r
d
io
v
ascu
la
r
Evaluation Warning : The document was created with Spire.PDF for Python.
I
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2
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8
8
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I
n
t J E
lec
&
C
o
m
p
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n
g
,
Vo
l.
14
,
No
.
6
,
Dec
em
b
e
r
20
24
:
6
7
3
4
-
6
7
4
2
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d
is
ea
s
e.
C
o
r
r
elatio
n
an
aly
s
is
allo
ws
u
s
to
id
en
tify
p
o
ten
tial
b
io
m
ar
k
er
s
f
o
r
f
u
r
th
er
s
tu
d
y
an
d
u
s
e
in
clin
ical
p
r
ac
tice.
Ho
wev
er
,
it
is
wo
r
th
r
em
em
b
e
r
in
g
t
h
at
co
r
r
elatio
n
d
o
es
n
o
t
m
ea
n
ca
u
s
atio
n
.
Ad
d
itio
n
al
r
esear
ch
is
n
ee
d
ed
to
estab
lis
h
th
is
,
in
clu
d
in
g
p
r
o
s
p
ec
tiv
e
s
tu
d
ies an
d
c
lin
ical
tr
ials
.
Ov
er
all,
th
is
an
aly
s
is
co
n
tr
ib
u
tes to
a
b
etter
u
n
d
er
s
tan
d
in
g
o
f
d
is
ea
s
e
m
ec
h
an
is
m
s
an
d
ca
n
b
e
u
s
ed
to
d
ev
elo
p
p
er
s
o
n
alize
d
s
tr
ateg
ies
f
o
r
th
e
p
r
ev
en
tio
n
an
d
tr
ea
tm
en
t
o
f
c
ar
d
io
v
ascu
lar
d
is
ea
s
es.
Fig
u
r
e
4
s
h
o
ws
th
e
d
y
n
am
ics
o
f
ch
a
n
g
es
in
th
e
R
MSE
o
f
th
e
XGBo
o
s
t
m
o
d
el
d
u
r
in
g
tr
ain
in
g
b
y
ep
o
c
h
f
o
r
th
e
t
r
ain
i
n
g
(
s
o
lid
lin
e)
an
d
test
in
g
(
d
as
h
ed
lin
e
)
s
am
p
les.
R
MSE
is
a
k
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in
d
icato
r
th
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ch
ar
ac
ter
izes
th
e
av
e
r
ag
e
d
ev
i
atio
n
o
f
p
r
e
d
icted
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alu
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m
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T
h
e
o
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s
er
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ed
d
ec
r
ea
s
e
in
R
MSE
d
u
r
in
g
tr
ai
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in
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in
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icate
s
an
im
p
r
o
v
em
en
t i
n
m
o
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q
u
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.
Fig
u
r
e
3
.
C
o
r
r
elatio
n
an
aly
s
is
o
f
b
io
c
h
em
ical
p
ar
am
ete
r
s
Fig
u
r
e
4
.
Dy
n
am
ics o
f
c
h
an
g
e
s
in
th
e
R
MSE
o
f
th
e
XGBo
o
s
t m
o
d
el
d
y
n
am
ics o
f
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h
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g
es i
n
th
e
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MSE
o
f
th
e
XGBo
o
s
t m
o
d
el
T
h
e
lear
n
in
g
c
u
r
v
e
p
lo
t
s
h
o
ws
a
g
r
ad
u
al
an
d
co
n
s
is
ten
t
d
ec
r
ea
s
e
in
R
MSE
o
n
b
o
th
th
e
tr
ain
in
g
an
d
test
s
e
ts
,
in
d
icatin
g
g
o
o
d
lear
n
in
g
an
d
g
en
e
r
aliza
tio
n
ab
ilit
y
o
f
th
e
m
o
d
el.
I
n
th
e
in
itial
s
tag
es,
th
e
d
ec
r
ea
s
e
in
R
MSE
o
cc
u
r
s
q
u
ick
ly
as
th
e
m
o
d
el
ac
tiv
ely
ad
ap
ts
to
th
e
s
tr
u
ctu
r
e
o
f
th
e
d
ata.
Su
b
s
eq
u
en
tly
,
th
e
cu
r
v
es
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r
ad
u
ally
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n
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er
g
e,
in
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th
e
s
tab
ilizatio
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o
f
th
e
lear
n
in
g
p
r
o
ce
s
s
an
d
th
e
ac
h
iev
e
m
en
t
o
f
an
o
p
tim
al
b
alan
ce
b
etwe
en
b
ias
an
d
d
i
s
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er
s
io
n
.
Mo
d
el
p
er
f
o
r
m
a
n
ce
m
etr
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h
ig
h
lig
h
t
its
ac
cu
r
a
cy
an
d
p
r
e
d
ictiv
e
p
o
wer
.
T
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e
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Evaluation Warning : The document was created with Spire.PDF for Python.
I
SS
N
:
2
0
8
8
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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
7
3
4
-
6
7
4
2
6740
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m
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m
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a
v
er
y
s
m
all
p
r
ed
ictio
n
er
r
o
r
.
T
h
e
co
ef
f
icien
t
o
f
d
eter
m
in
ati
o
n
(
R
²)
is
0
.
8
6
5
,
in
d
icatin
g
th
at
t
h
e
m
o
d
el
f
it
th
e
d
ata
well,
ex
p
lain
in
g
8
6
.
5
%
o
f
th
e
v
ar
ian
ce
in
th
e
tar
g
et
v
ar
iab
le
.
T
h
e
AI
C
an
d
B
I
C
o
f
th
is
m
o
d
el
ar
e
s
ig
n
if
ican
tly
lo
we
r
c
o
m
p
ar
ed
t
o
v
an
illa
C
NN,
in
d
icatin
g
a
g
o
o
d
tr
ad
e
-
o
f
f
b
etwe
en
m
o
d
el
c
o
m
p
le
x
ity
an
d
g
o
o
d
n
ess
o
f
f
it.
On
th
e
o
th
er
h
an
d
,
t
h
e
v
an
illa
C
NN
m
o
d
el
p
er
f
o
r
m
s
s
ig
n
if
ican
tly
wo
r
s
e.
T
h
e
m
e
an
s
q
u
ar
e
er
r
o
r
(
MSE
)
is
0
.
4
5
,
wh
ich
is
m
u
ch
h
ig
h
er
co
m
p
ar
ed
to
XGBo
o
s
t,
in
d
icatin
g
less
ac
cu
r
ate
p
r
e
d
ictio
n
s
.
T
h
e
co
ef
f
icien
t
o
f
d
eter
m
in
atio
n
(
R
²)
is
-
0
.
8
7
5
,
r
ef
lectin
g
an
ex
t
r
em
el
y
p
o
o
r
r
esu
lt.
T
h
is
m
ea
n
s
th
a
t
th
e
m
o
d
el
is
n
o
t
a
b
le
to
ac
c
u
r
ately
p
r
e
d
ict
an
d
d
o
es
n
o
t
ev
en
r
ea
c
h
th
e
lev
el
o
f
a
b
asic
m
ea
n
p
r
ed
ictio
n
m
o
d
el.
T
h
e
AI
C
a
n
d
B
I
C
in
f
o
r
m
atio
n
cr
iter
ia
ar
e
also
m
u
ch
h
ig
h
er
,
in
d
icatin
g
o
v
er
f
itti
n
g
o
r
in
a
d
eq
u
ate
s
p
ec
if
icatio
n
o
f
th
e
m
o
d
el.
As
a
r
esu
lt,
XGBo
o
s
t
d
em
o
n
s
tr
ates
s
ig
n
if
ican
tly
b
etter
p
er
f
o
r
m
an
ce
ac
r
o
s
s
all
k
ey
m
etr
ics.
Du
e
to
its
h
ig
h
ac
cu
r
ac
y
a
n
d
g
en
er
aliza
tio
n
ab
ilit
y
,
th
is
m
o
d
el
o
u
tp
er
f
o
r
m
s
v
an
illa
C
NN,
wh
ich
s
u
f
f
er
s
f
r
o
m
o
v
er
f
itti
n
g
an
d
an
in
ab
ilit
y
to
ad
eq
u
ately
g
en
er
alize
d
ata,
wh
ich
is
r
ef
lecte
d
in
h
ig
h
v
alid
a
tio
n
lo
s
s
an
d
n
eg
ativ
e
R
²
v
alu
e.
4.
CO
NCLU
SI
O
N
I
n
co
n
clu
s
io
n
,
r
esear
ch
o
n
c
ar
d
io
v
ascu
lar
d
is
ea
s
e
r
is
k
f
a
cto
r
ass
ess
m
en
t
u
s
in
g
m
ac
h
i
n
e
lear
n
in
g
tech
n
iq
u
es
is
im
p
o
r
tan
t
in
s
elec
tin
g
th
e
r
i
g
h
t
alg
o
r
ith
m
s
a
n
d
p
a
r
am
eter
s
to
e
n
s
u
r
e
ac
c
u
r
ate
p
r
ed
ictio
n
.
A
co
m
p
ar
is
o
n
o
f
th
e
XGBo
o
s
t
an
d
C
NN
m
o
d
els
r
ev
ea
led
cl
ea
r
ad
v
an
ta
g
es
o
f
XGBo
o
s
t
wh
en
wo
r
k
i
n
g
with
tab
u
lar
d
ata
s
u
ch
as
b
io
m
ar
k
er
s
an
d
d
em
o
g
r
ap
h
ic
ch
ar
a
cter
is
tics
.
T
h
is
m
o
d
el
h
as
d
em
o
n
s
tr
ated
h
ig
h
p
r
ed
ictiv
e
ac
cu
r
ac
y
d
u
e
to
its
ab
ilit
y
to
ef
f
icien
tly
m
o
d
el
co
m
p
lex
n
o
n
lin
ea
r
r
elatio
n
s
h
ip
s
an
d
its
r
o
b
u
s
tn
ess
to
s
p
ar
s
e
d
ata.
On
th
e
o
th
er
h
an
d
,
th
e
C
NN
m
o
d
el,
d
esp
ite
its
p
o
ten
tial
ad
v
an
tag
es
in
an
aly
zin
g
s
p
atially
co
r
r
elate
d
d
ata,
s
h
o
we
d
s
ig
n
s
o
f
o
v
er
f
itti
n
g
an
d
u
n
d
e
r
p
er
f
o
r
m
an
ce
in
b
io
m
ar
k
er
class
if
icatio
n
task
s
.
T
h
is
in
d
icate
s
th
e
n
ee
d
f
o
r
f
u
r
th
er
o
p
tim
izatio
n
o
f
th
e
ar
c
h
itectu
r
e
an
d
tr
ain
in
g
al
g
o
r
ith
m
,
as
well
as
th
e
u
s
e
o
f
r
eg
u
lar
izatio
n
m
eth
o
d
s
an
d
in
cr
ea
s
in
g
th
e
s
ize
o
f
th
e
tr
ain
in
g
s
et.
T
h
e
u
s
e
o
f
MSE
m
etr
ics,
co
ef
f
icien
t
o
f
d
eter
m
i
n
atio
n
(
R
²)
,
AI
C
,
an
d
B
I
C
p
r
o
v
id
ed
in
-
d
ep
th
an
aly
s
is
an
d
co
m
p
ar
is
o
n
o
f
m
o
d
els,
r
ev
ea
lin
g
th
eir
s
tr
en
g
th
s
a
n
d
wea
k
n
ess
es.
T
h
e
XGBo
o
s
t
ap
p
r
o
ac
h
h
as
d
em
o
n
s
tr
ated
r
o
b
u
s
t
p
r
e
d
ictio
n
a
b
ilit
y
b
y
ef
f
ec
tiv
el
y
b
ala
n
cin
g
b
ias
an
d
v
ar
ia
n
ce
,
wh
i
le
th
e
C
NN
m
o
d
e
l
r
eq
u
ir
es
ad
ju
s
tm
en
t
a
n
d
p
ar
a
m
eter
o
p
tim
izatio
n
.
T
h
ese
f
in
d
in
g
s
h
ig
h
li
g
h
t
th
e
im
p
o
r
ta
n
c
e
o
f
p
r
o
p
e
r
ch
o
ice
o
f
m
o
d
el
ar
ch
itectu
r
e
an
d
tr
ain
in
g
p
ar
am
eter
s
in
ca
r
d
io
v
ascu
lar
r
is
k
p
r
ed
ictio
n
task
s
.
I
n
th
e
f
u
tu
r
e,
r
esear
ch
s
h
o
u
ld
co
n
tin
u
e
in
th
e
d
ir
e
ctio
n
o
f
p
er
s
o
n
alize
d
m
e
d
icin
e,
o
p
tim
izin
g
m
o
d
els
f
o
r
id
en
tif
y
in
g
r
is
k
f
ac
to
r
s
an
d
d
ev
elo
p
in
g
s
tr
ateg
ies f
o
r
ea
r
ly
d
iag
n
o
s
is
an
d
p
r
ev
e
n
tio
n
o
f
c
ar
d
io
v
ascu
lar
d
is
ea
s
es.
RE
F
E
R
E
NC
E
S
[
1
]
A
.
C
.
O
’
K
e
l
l
y
e
t
a
l
.
,
“
P
r
e
g
n
a
n
c
y
a
n
d
r
e
p
r
o
d
u
c
t
i
v
e
r
i
s
k
f
a
c
t
o
r
s
f
o
r
c
a
r
d
i
o
v
a
sc
u
l
a
r
d
i
s
e
a
s
e
i
n
w
o
m
e
n
,
”
C
i
rc
u
l
a
t
i
o
n
R
e
se
a
rc
h
,
v
o
l
.
1
3
0
,
n
o
.
4
,
p
p
.
6
5
2
–
6
7
2
,
F
e
b
.
2
0
2
2
,
d
o
i
:
1
0
.
1
1
6
1
/
c
i
r
c
r
e
sa
h
a
.
1
2
1
.
3
1
9
8
9
5
.
[
2
]
J.
L.
S
i
l
v
e
i
r
a
R
o
ssi
,
S
.
M
.
B
a
r
b
a
l
h
o
,
R
.
d
e
A
r
a
u
j
o
,
M
.
D
.
B
e
c
h
a
r
a
,
K
.
P
.
S
l
o
a
n
,
a
n
d
L.
A
.
S
l
o
a
n
,
“
M
e
t
a
b
o
l
i
c
sy
n
d
r
o
m
e
a
n
d
c
a
r
d
i
o
v
a
sc
u
l
a
r
d
i
se
a
ses
:
g
o
i
n
g
b
e
y
o
n
d
t
r
a
d
i
t
i
o
n
a
l
r
i
sk
f
a
c
t
o
r
s,”
D
i
a
b
e
t
e
s/
Me
t
a
b
o
l
i
sm
Re
s
e
a
r
c
h
a
n
d
Re
v
i
e
w
s
,
v
o
l
.
3
8
,
n
o
.
3
,
O
c
t
.
2
0
2
1
,
d
o
i
:
1
0
.
1
0
0
2
/
d
mr
r
.
3
5
0
2
.
[
3
]
J.
H
u
e
t
a
l
.
,
“
S
o
c
i
a
l
i
s
o
l
a
t
i
o
n
,
s
o
c
i
a
l
su
p
p
o
r
t
,
l
o
n
e
l
i
n
e
ss
a
n
d
c
a
r
d
i
o
v
a
sc
u
l
a
r
d
i
se
a
se
r
i
s
k
f
a
c
t
o
r
s:
a
c
r
o
ss
-
sec
t
i
o
n
a
l
s
t
u
d
y
a
mo
n
g
o
l
d
e
r
a
d
u
l
t
s,
”
I
n
t
e
rn
a
t
i
o
n
a
l
J
o
u
r
n
a
l
o
f
G
e
ri
a
t
r
i
c
Psy
c
h
i
a
t
ry
,
v
o
l
.
3
6
,
n
o
.
1
1
,
p
p
.
1
7
9
5
–
1
8
0
9
,
Ju
l
.
2
0
2
1
,
d
o
i
:
1
0
.
1
0
0
2
/
g
p
s.
5
6
0
1
.
[
4
]
A
.
S
h
a
i
t
o
e
t
a
l
.
,
“
H
e
r
b
a
l
me
d
i
c
i
n
e
f
o
r
c
a
r
d
i
o
v
a
sc
u
l
a
r
d
i
s
e
a
s
e
s:
e
f
f
i
c
a
c
y
,
mec
h
a
n
i
sms
,
a
n
d
safe
t
y
,
”
Fr
o
n
t
i
e
rs
i
n
Ph
a
rm
a
c
o
l
o
g
y
,
v
o
l
.
1
1
,
A
p
r
.
2
0
2
0
,
d
o
i
:
1
0
.
3
3
8
9
/
f
p
h
a
r
.
2
0
2
0
.
0
0
4
2
2
.
[
5
]
Y
.
D
e
n
g
e
t
a
l
.
,
“
A
p
p
l
i
c
a
t
i
o
n
o
f
t
h
e
n
a
n
o
-
d
r
u
g
d
e
l
i
v
e
r
y
sy
s
t
e
m
i
n
t
r
e
a
t
m
e
n
t
o
f
c
a
r
d
i
o
v
a
s
c
u
l
a
r
d
i
se
a
ses
,
”
Fr
o
n
t
i
e
rs
i
n
Bi
o
e
n
g
i
n
e
e
r
i
n
g
a
n
d
Bi
o
t
e
c
h
n
o
l
o
g
y
,
v
o
l
.
7
,
J
a
n
.
2
0
2
0
,
d
o
i
:
1
0
.
3
3
8
9
/
f
b
i
o
e
.
2
0
1
9
.
0
0
4
8
9
.
[
6
]
N
.
L.
F
i
t
r
i
y
a
n
i
,
M
.
S
y
a
f
r
u
d
i
n
,
G
.
A
l
f
i
a
n
,
a
n
d
J.
R
h
e
e
,
“
H
D
P
M
:
a
n
e
f
f
e
c
t
i
v
e
h
e
a
r
t
d
i
s
e
a
s
e
p
r
e
d
i
c
t
i
o
n
m
o
d
e
l
f
o
r
a
c
l
i
n
i
c
a
l
d
e
c
i
si
o
n
su
p
p
o
r
t
s
y
s
t
e
m,
”
I
E
EE
A
c
c
e
ss
,
v
o
l
.
8
,
p
p
.
1
3
3
0
3
4
–
1
3
3
0
5
0
,
2
0
2
0
,
d
o
i
:
1
0
.
1
1
0
9
/
a
c
c
e
s
s.
2
0
2
0
.
3
0
1
0
5
1
1
.
[
7
]
R
.
TR
,
U
.
K
.
L
i
l
h
o
r
e
,
P
.
M
,
S
.
S
i
ma
i
y
a
,
A
.
K
a
u
r
,
a
n
d
M
.
H
a
md
i
,
“
P
r
e
d
i
c
t
i
v
e
a
n
a
l
y
s
i
s
o
f
h
e
a
r
t
d
i
s
e
a
s
e
s
w
i
t
h
mac
h
i
n
e
l
e
a
r
n
i
n
g
a
p
p
r
o
a
c
h
e
s,
”
Ma
l
a
y
si
a
n
J
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p
p
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4
9
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RS
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iv
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fter
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u
m
il
y
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v
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re
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d
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a
m
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ste
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re
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c
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m
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ra
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ti
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m
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fter
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il
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v
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o
c
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ra
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sp
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c
ialty
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1
0
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fo
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ti
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m
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ra
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m
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fter
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il
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rm
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ti
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o
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p
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ter
tec
h
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sy
ste
m
s,
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ficia
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telli
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c
a
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tac
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th
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ti
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s
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9
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1
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).
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1
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).
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th
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m
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ti
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s
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1
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9
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a
t
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irs
k
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t
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te
Un
iv
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d
e
fe
n
d
e
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h
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o
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t
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2
0
0
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a
t
Al
-
F
a
ra
b
i
Ka
z
a
k
h
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ti
o
n
a
l
Un
i
v
e
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o
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th
e
to
p
ic
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P
ro
b
lem
s
o
f
d
e
fi
n
a
b
il
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ty
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n
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l
g
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rit
h
m
ic
c
o
m
p
lex
it
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o
f
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o
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v
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r
a
lg
e
b
ra
ic
stru
c
t
u
re
s.
”
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c
o
m
p
lete
d
a
sc
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ti
fic
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tern
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ip
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t
t
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Un
iv
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o
f
No
tre
Da
m
m
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in
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d
ian
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,
USA,
in
2
0
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1
,
b
e
in
g
a
sc
h
o
lars
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ip
re
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ip
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n
t
o
f
t
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e
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las
h
a
k
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l
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ro
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.
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2
0
1
1
,
h
e
wa
s
a
wa
rd
e
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ti
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o
f
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ro
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y
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h
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M
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istry
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ti
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z
a
k
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sta
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c
a
n
b
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c
o
n
tac
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a
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m
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:
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u
ss
u
p
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v
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il
.
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a
m
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a
In
2
0
0
7
sh
e
g
ra
d
u
a
ted
fro
m
t
h
e
Ka
z
a
k
h
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ti
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n
a
l
M
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d
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iv
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a
m
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d
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fter
S
.
D.
As
fe
n
d
iy
a
ro
v
,
sp
e
c
ialty
“
g
e
n
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r
a
l
m
e
d
icin
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,
q
u
a
li
fica
ti
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n
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to
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2
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ra
d
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sid
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li
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2
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2
,
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e
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ra
d
u
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z
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k
h
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l
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m
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fter
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n
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ro
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0
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-
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ro
m
2
0
1
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t
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re
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tern
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se
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lt
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rk
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s
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o
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tere
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h
e
c
a
n
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o
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tac
ted
a
t
e
m
a
il
:
k
a
m
il
la
-
k
m
@m
a
il
.
ru
.
Evaluation Warning : The document was created with Spire.PDF for Python.
I
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N
:
2
0
8
8
-
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7
0
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I
n
t J E
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&
C
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m
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g
,
Vo
l.
14
,
No
.
6
,
Dec
em
b
e
r
20
24
:
6
7
3
4
-
6
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4
2
6742
As
se
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.
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