I
A
E
S
I
n
t
e
r
n
at
io
n
al
Jou
r
n
al
of
A
r
t
if
ic
ia
l
I
n
t
e
ll
ig
e
n
c
e
(
I
J
-
AI
)
V
ol
.
14
, N
o.
3
,
J
une
2025
, pp.
2072
~
2082
I
S
S
N
:
2252
-
8938
,
D
O
I
:
10.11591/
ij
a
i.
v
14
.i
3
.pp
2072
-
2082
2072
Jou
r
n
al
h
om
e
page
:
ht
tp
:
//
ij
ai
.
ia
e
s
c
or
e
.c
om
A
sym
p
t
om
-
d
r
i
ve
n
m
e
d
i
c
al
d
i
agn
osi
s
su
p
p
or
t
m
o
d
e
l
b
ase
d
on
m
ac
h
i
n
e
l
e
ar
n
i
n
g t
e
c
h
n
i
q
u
e
s
A
d
il
L
aab
id
i,
M
oh
am
m
e
d
A
is
s
aou
i
N
a
t
i
ona
l
S
c
hool
of
A
ppl
i
e
d S
c
i
e
nc
e
s
,
M
oha
m
m
e
d P
r
e
m
i
e
r
U
ni
ve
r
s
i
t
y, O
uj
da
,
M
or
oc
c
o
A
r
t
ic
le
I
n
f
o
A
B
S
T
R
A
C
T
A
r
ti
c
le
h
is
to
r
y
:
R
e
c
e
iv
e
d
A
pr
18
,
2024
R
e
vi
s
e
d
F
e
b
27
,
2025
A
c
c
e
pt
e
d
M
a
r
15
,
2025
Medicine
is
a
human
science
that
is
constantly
evolving,
and
this
ev
olution
generates
a
large
mass
of
data
that
needs
to
be
exploited
with
the
mu
ltitude
of
IT
resources
available
to
guarantee
and
maintain
this
scientific
pr
ogress.
Some
diseases
share
most
symptoms,
where
as
others
could
have
a
low
probability
of
being
identified
in
an
early
stage.
Thus,
when
facing
a
such
situation,
an
inexperience
d
doctor
may
have
difficulty
making
th
e
right
diagnosis
or
may
test
different
cases,
which
will
be
a
big
waste
o
f
ti
me.
In
this
paper,
we
are
going
to
m
ake
this
embarrassing
situation
less
comp
lex
by
giving
practitioners
every
probable
disease,
and
even
the
least
probabl
e
ones
according
to
the
given
sympto
ms.
Indeed,
this
work
will
push
the
dia
gnosis
deeper
to
reveal
hidden
symptoms
and
pathogenesis,
to
help
practi
tioners
make
the
right
decisions.
To
develop
such
a
solution,
the
data
is
org
anized
by
matching
each
disease
with
its
known
symptoms,
then
we
used
n
aive
Bayes
as
a
classific
ation
model,
and
different
met
ric
s
to
evalua
te
the
performance
of
this
experiment.
This
work
proves
that
machine
learni
ng
has
become
very
effective
in
the
medical
sector,
especially
when
we
noti
ce
that
the acc
uracy e
xceeds 9
0% in the de
tection of d
iseases.
K
e
y
w
o
r
d
s
:
D
is
e
a
s
e
pr
e
di
c
ti
on
F
-
s
c
or
e
M
a
c
hi
ne
l
e
a
r
ni
ng
M
e
di
c
a
l
di
a
gno
s
is
N
a
iv
e
B
a
ye
s
S
ym
pt
om
s
This is an
open
acce
ss artic
le unde
r the
CC BY
-
SA
license.
C
or
r
e
s
pon
di
n
g A
u
th
or
:
A
di
l
L
a
a
bi
di
N
a
ti
ona
l
S
c
hool
of
A
ppl
ie
d S
c
ie
nc
e
s
, M
oh
a
m
m
e
d P
r
e
m
ie
r
U
ni
ve
r
s
it
y
B
oul
e
va
r
d M
oha
m
e
d V
I
,
O
uj
da
,
M
or
oc
c
o
E
m
a
il
:
la
a
bi
di
.a
di
l@ump.a
c
.m
a
1.
I
N
T
R
O
D
U
C
T
I
O
N
T
he
m
a
s
s
iv
e
te
c
hnol
ogi
c
a
l
a
dva
nc
e
m
e
nt
ha
s
gr
e
a
tl
y
a
c
c
e
le
r
a
te
d
di
s
e
a
s
e
di
a
gno
s
is
a
nd
tr
e
a
tm
e
nt
pl
a
ns
.
V
a
r
io
us
hos
pi
ta
ls
ha
ve
a
dopt
e
d
th
e
te
c
hnol
ogi
c
a
l
a
ppr
oa
c
h
due
to
c
os
t
r
e
du
c
ti
on,
ti
m
e
s
a
vi
ng
a
nd
a
c
c
ur
a
te
r
e
s
ul
ts
[
1]
,
[
2]
.
M
or
e
ove
r
,
he
a
lt
h
in
f
or
m
a
ti
on
te
c
hnol
ogi
e
s
c
oul
d
r
e
duc
e
dr
ug
e
ve
nt
s
a
nd
pr
oduc
e
a
be
tt
e
r
r
e
la
ti
ons
hi
p
be
twe
e
n
doc
to
r
a
nd
pa
ti
e
nt
[
3]
.
W
e
a
l
s
o
not
ic
e
th
a
t
th
e
dyna
m
ic
c
om
m
uni
ty
of
bi
oi
nf
or
m
a
ti
c
s
r
e
s
e
a
r
c
he
r
s
,
w
ho
ha
v
e
e
le
v
a
te
d
da
ta
s
c
i
e
nc
e
to
a
f
ounda
ti
ona
l
c
om
pe
te
n
c
y,
ha
ve
c
ons
i
s
te
nt
ly
c
r
e
a
te
d
hi
ghl
y
r
e
pr
oduc
ib
le
a
ppr
oa
c
he
s
th
a
t
w
or
k
in
di
f
f
e
r
e
nt
s
e
tt
in
gs
a
nd
w
it
h
di
f
f
e
r
e
nt
pa
ti
e
nt
s
.
T
hi
s
gr
oup
of
r
e
s
e
a
r
c
he
r
s
e
m
pl
oye
d
c
om
put
e
r
s
a
s
th
e
ir
la
bor
a
to
r
ie
s
in
one
of
th
e
ba
s
ic
s
c
i
e
nc
e
s
,
a
nd
w
hi
c
h
ha
s
be
e
n
on
e
of
t
he
e
a
r
ly
doma
in
s
w
he
r
e
bi
g da
ta
w
a
s
s
e
qu
e
s
te
r
e
d a
s
m
e
di
c
a
l
s
c
ie
nc
e
[
4]
.
F
ur
th
e
r
m
or
e
,
b
ig
da
ta
a
nd
m
a
c
hi
ne
le
a
r
ni
ng
ha
v
e
th
e
pow
e
r
to
gui
de
pr
a
c
ti
ti
one
r
s
to
w
a
r
d
th
e
m
os
t
e
f
f
e
c
ti
ve
m
e
di
c
a
l
de
c
is
io
n a
nd
tr
e
a
tm
e
nt
s
,
a
s
th
e
y
c
oul
d
im
pr
ove
c
a
r
e
qu
a
li
ty
w
it
hout
ha
vi
ng
a
ny
ne
e
d
to
te
s
t
unne
c
e
s
s
a
r
y di
a
gnos
ti
c
a
nd t
he
r
a
pi
e
s
[
5]
. T
he
c
ol
le
c
ti
on of
m
a
s
s
iv
e
da
ta
ha
s
f
or
m
e
d l
a
r
ge
-
s
c
a
le
da
ta
s
e
ts
f
r
om
di
f
f
e
r
e
nt
m
e
di
c
a
l
s
e
c
to
r
s
s
uc
h
a
s
bi
ol
ogi
c
a
l
s
c
ie
nc
e
,
r
a
di
ol
ogi
c
a
l,
bi
oi
nf
or
m
a
ti
c
s
,
a
nd
e
ve
n
m
e
di
c
a
l
pr
e
s
c
r
ip
ti
ons
w
r
it
te
n
in
hum
a
n
n
a
tu
r
a
l
la
ngua
ge
.
T
he
r
e
f
or
e
,
t
hi
s
bi
g
qua
nt
it
y
of
da
ta
ne
e
d
s
a
n
in
te
ll
ig
e
nt
tr
e
a
tm
e
nt
to
pr
oduc
e
r
e
le
va
nt
r
e
s
ul
ts
in
th
e
a
im
of
he
lp
in
g
c
li
ni
c
a
l
de
c
is
io
ns
[
6]
.
T
o
pr
oduc
e
th
e
s
e
r
e
s
ul
ts
,
di
f
f
e
r
e
nt
te
c
hni
c
s
a
r
e
a
ppl
ie
d
w
hi
le
c
onve
r
gi
ng
to
th
e
s
a
m
e
obj
e
c
ti
ve
;
w
e
not
ic
e
m
a
c
hi
ne
le
a
r
ni
ng,
Evaluation Warning : The document was created with Spire.PDF for Python.
I
nt
J
A
r
ti
f
I
nt
e
ll
I
S
S
N
:
2252
-
8938
A
s
y
m
pt
om
-
dr
iv
e
n m
e
di
c
al
di
agno
s
is
s
uppo
r
t
m
ode
l
bas
e
d on
m
ac
hi
ne
le
ar
ni
ng t
e
c
hni
que
s
(
A
di
l
L
aabi
di
)
2073
de
e
p
le
a
r
ni
ng,
r
e
in
f
or
c
e
m
e
nt
le
a
r
ni
ng
,
a
nd
a
ls
o
na
tu
r
a
l
la
ngua
ge
pr
oc
e
s
s
in
g.
T
he
s
e
a
r
ti
f
ic
ia
l
in
te
ll
ig
e
nc
e
te
c
hnol
ogi
e
s
,
c
om
bi
ne
d
w
it
h
hum
a
n
in
te
ll
ig
e
nc
e
,
ha
ve
th
e
p
ow
e
r
to
a
c
c
om
pl
is
h
di
f
f
ic
ul
t
ta
s
k
s
w
it
h
e
a
s
e
.
T
he
y
c
a
n
he
lp
w
it
h
a
c
c
ur
a
te
di
a
gnos
is
,
m
a
ki
ng
e
f
f
ic
ie
nt
tr
e
a
tm
e
nt
a
nd
f
ol
lo
w
-
up
pl
a
ns
,
r
is
k
pr
e
di
c
ti
on,
pr
oduc
ti
vi
ty
i
m
pr
ove
m
e
nt
,
a
nd e
ve
n r
e
duc
in
g m
e
di
c
a
l
e
r
r
or
s
[
7]
, [
8]
.
U
s
in
g
th
e
s
e
m
a
c
hi
n
e
le
a
r
ni
ng
te
c
hni
que
s
,
w
e
c
oul
d
w
or
k
o
n
s
it
ua
ti
ons
w
it
h
di
f
f
e
r
e
nt
le
ve
ls
of
c
om
pl
e
xi
ty
,
s
uc
h
a
s
th
e
a
na
ly
s
is
of
r
a
di
ol
ogi
c
a
l
im
a
ge
s
w
hi
le
r
e
ve
a
li
ng
r
e
le
va
nt
r
e
s
ul
ts
th
a
t
c
oul
d
le
a
d
to
in
te
r
pr
e
ti
ng s
om
e
of
t
he
i
m
a
ge
s
i
n pr
io
r
it
y.
A
not
he
r
c
a
s
e
c
oul
d be
t
he
s
it
ua
ti
on of
pa
ti
e
nt
s
, s
om
e
of
t
he
m
m
a
y
ne
e
d
to
be
r
e
di
r
e
c
te
d
to
a
s
pe
c
ia
li
s
t
w
he
r
e
ot
he
r
s
w
il
l
s
im
pl
y
r
e
c
e
iv
e
ps
yc
hol
ogi
c
a
l
s
uppor
t.
T
he
s
e
da
ta
s
c
ie
nc
e
te
c
hni
que
s
w
il
l
not
be
a
bl
e
to
r
e
pl
a
c
e
hum
a
n
in
te
r
ve
nt
i
on,
but
w
il
l
be
u
s
e
d
a
s
a
c
om
pl
e
m
e
nt
a
nd
he
lp
pr
a
c
ti
ti
one
r
s
to
be
tt
e
r
m
a
na
g
e
th
e
ir
in
te
r
ve
nt
io
ns
a
nd
to
de
vot
e
th
e
m
s
e
lv
e
s
m
uc
h
m
or
e
to
th
e
m
os
t
c
om
pl
e
x
s
it
ua
ti
ons
. I
t
is
a
pr
a
c
ti
c
a
l
w
a
y t
o
c
r
e
a
te
c
ol
la
bor
a
ti
on be
twe
e
n
m
e
di
c
in
e
a
nd a
r
ti
f
ic
ia
l
in
te
ll
ig
e
nc
e
.
I
n
th
is
pa
pe
r
,
w
e
s
tu
dy
th
e
pow
e
r
of
m
a
c
hi
ne
le
a
r
ni
ng
c
la
s
s
if
ie
r
s
to
id
e
nt
if
y
di
s
e
a
s
e
s
a
c
c
or
di
ng
to
s
om
e
gi
ve
n
s
ym
pt
om
s
.
T
hi
s
w
or
k
w
on’
t
be
ju
s
t
a
s
im
pl
e
c
la
s
s
if
ic
a
ti
on
th
a
t
ha
ve
to
de
te
r
m
in
e
c
onc
e
r
ne
d
di
s
e
a
s
e
,
but
a
ls
o
to
b
e
a
bl
e
to
pr
e
di
c
t
e
ve
n
lo
w
pr
oba
bi
li
ty
di
s
e
a
s
e
s
.
I
n
s
om
e
c
a
s
e
s
,
a
n
un
e
xpe
r
ie
nc
e
d
do
c
to
r
or
e
ve
n a
n e
xpe
r
ie
nc
e
d one
c
oul
d be
c
onf
us
e
d w
he
n a
pa
ti
e
nt
h
a
s
s
ym
pt
om
s
f
or
a
c
om
m
on
di
s
e
a
s
e
, but
a
t
th
e
s
a
m
e
ti
m
e
th
e
s
e
s
ym
pt
om
s
c
a
n
r
e
v
e
a
l
a
s
e
r
io
us
di
s
e
a
s
e
in
a
n
e
a
r
ly
s
ta
ge
.
T
he
r
e
f
or
e
,
th
e
doc
to
r
m
us
t
e
xpe
r
im
e
nt
w
it
h
di
f
f
e
r
e
nt
c
li
ni
c
a
l
pl
a
ns
f
or
a
m
or
e
a
c
c
ur
a
te
di
a
gnos
is
to
de
f
in
e
th
e
di
f
f
e
r
e
nt
pos
s
ib
le
c
a
s
e
s
.
H
ow
e
ve
r
,
by
f
ol
lo
w
in
g
th
is
m
e
th
od,
w
e
m
a
y
w
a
s
te
to
o
m
uc
h
ti
m
e
th
a
t
c
oul
d
be
im
po
r
ta
nt
in
th
e
he
a
li
ng
pha
s
e
,
a
ti
m
e
w
he
r
e
a
di
s
e
a
s
e
in
a
n
e
a
r
ly
s
ta
ge
m
a
y
be
c
om
e
i
n
a
n
a
dva
nc
e
d
one
.
S
o,
th
is
w
or
k
pr
ovi
de
s
a
s
ol
ut
io
n
to
ove
r
c
om
e
th
is
s
it
ua
ti
on
a
nd
gi
ve
to
pr
a
c
ti
ti
one
r
s
e
n
ough
ti
m
e
a
nd
pos
s
ib
il
it
ie
s
to
d
e
e
pl
y
a
n
a
ly
s
e
s
e
ve
r
y
c
a
s
e
e
nc
ount
e
r
e
d.
T
hi
s
s
tu
dy
r
e
pr
e
s
e
nt
s
th
e
r
e
s
ul
t
s
ob
ta
in
e
d
by
a
ppl
yi
ng
n
a
iv
e
B
a
ye
s
a
s
a
m
ode
l
le
a
r
ni
ng
on
a
d
a
ta
s
e
t
f
or
m
e
d
f
r
om
da
ta
c
ol
le
c
te
d
on
th
e
w
e
b
p
or
ta
l
of
th
e
U
.S
.
N
a
ti
ona
l
L
ib
r
a
r
y
of
M
e
di
c
in
e
(
N
L
M
)
[
9]
.
T
he
m
a
in
obj
e
c
ti
ve
is
to
gi
ve
th
e
po
s
s
ib
il
it
y
to
de
t
e
r
m
in
e
a
hi
dde
n
di
s
e
a
s
e
be
hi
nd
th
e
s
ym
pt
om
s
of
ot
he
r
one
s
.
T
hi
s
w
or
k
i
s
de
s
c
r
ib
e
d
a
s
f
ol
lo
w
s
.
S
e
c
ti
on
2
de
s
c
r
ib
e
s
a
li
te
r
a
tu
r
e
r
e
vi
e
w
on
m
a
c
hi
ne
le
a
r
ni
ng
f
or
he
a
lt
hc
a
r
e
,
m
e
di
c
a
l
r
e
s
e
a
r
c
h
,
a
nd
s
om
e
pr
e
li
m
in
a
r
y
c
om
pa
r
is
o
ns
of
di
f
f
e
r
e
nt
m
ode
ls
.
S
e
c
ti
on
3
pr
e
s
e
nt
s
th
e
r
e
s
e
a
r
c
h m
e
th
odol
ogy whic
h e
xpl
a
in
s
t
he
ove
r
a
ll
s
tr
uc
tu
r
e
of
our
s
tu
dy a
s
w
e
ll
a
s
t
h
e
t
e
c
hni
que
s
a
nd me
th
ods
w
e
us
e
d
in
th
is
w
or
k.
T
he
n
in
s
e
c
ti
on
4,
w
e
w
il
l
pr
e
s
e
nt
a
nd
di
s
c
us
s
th
e
r
e
s
ul
ts
of
th
is
e
xpe
r
im
e
nt
.
F
in
a
ll
y,
in
c
onc
lu
s
io
n,
w
e
e
va
lu
a
te
th
e
m
ode
l
in
te
r
m
s
of
a
c
hi
e
vi
ng
th
e
obj
e
c
ti
ve
s
a
nd
w
e
w
il
l
ta
lk
a
bout
s
om
e
f
ut
ur
e
w
or
k.
2.
R
E
L
A
T
E
D
WORKS
T
a
ki
ng
a
dva
nt
a
ge
of
th
e
gr
e
a
t
te
c
hnol
ogi
c
a
l
a
dva
nc
e
s
in
ha
r
dw
a
r
e
a
nd
s
of
twa
r
e
,
a
s
w
e
ll
a
s
th
e
pow
e
r
of
va
r
io
us
m
a
-
c
hi
n
e
le
a
r
ni
ng
a
lg
or
it
hm
s
,
m
a
ny
s
tu
di
e
s
ha
ve
de
e
pl
y
e
xpl
oi
te
d
bi
g
da
t
a
in
or
de
r
to
pr
ove
th
e
be
ne
f
it
s
of
a
r
ti
f
ic
ia
l
in
te
ll
ig
e
nc
e
in
th
e
m
e
di
c
a
l
s
e
c
to
r
.
L
a
a
bi
di
a
nd
A
is
s
a
oui
[
10]
c
om
pa
r
in
g
th
e
pe
r
f
or
m
a
nc
e
of
di
f
f
e
r
e
nt
m
a
c
hi
ne
le
a
r
ni
ng
a
lg
or
it
hm
s
in
or
de
r
to
de
te
r
m
in
e
th
e
be
s
t
pe
r
f
or
m
in
g
m
ode
l
f
o
r
pr
e
di
c
ti
ng
di
a
be
te
s
a
nd
pr
os
ta
te
c
a
nc
e
r
.
T
hi
s
s
tu
dy
pr
ove
d
th
a
t
a
lg
or
it
hm
s
c
a
n
be
ve
r
y
a
c
c
ur
a
te
w
he
n
it
c
om
e
s
to
pr
e
di
c
ti
ng
di
s
e
a
s
e
s
.
I
n
th
e
s
a
m
e
c
ont
e
xt
,
S
ha
h
e
t
al
.
[
11]
c
onduc
te
d
a
s
tu
dy
w
it
h
th
e
obj
e
c
ti
ve
of
de
te
r
m
in
in
g
w
he
th
e
r
or
not
a
pa
ti
e
nt
c
oul
d
de
ve
lo
p
he
a
r
t
di
s
e
a
s
e
.
T
he
y
w
or
ke
d
on
f
our
di
f
f
e
r
e
nt
m
ode
ls
w
hi
le
c
om
pa
r
in
g
th
e
ir
pe
r
f
or
m
a
nc
e
.
T
he
m
a
in
obj
e
c
ti
ve
w
a
s
to
w
or
k
w
it
h
le
s
s
e
r
a
tt
r
ib
ut
e
s
a
nd
te
s
ts
[
11]
.
T
hi
s
s
tu
dy
a
ls
o
de
m
on
s
tr
a
te
d
th
a
t
c
la
s
s
if
ic
a
ti
on
m
ode
ls
a
r
e
ve
r
y
e
f
f
e
c
ti
ve
w
he
n
it
c
om
e
s
to
pr
oduc
in
g
m
e
di
c
a
l
out
c
om
e
s
w
it
h
good
a
c
c
ur
a
c
y.
K
o
s
a
r
ka
r
e
t
al
.
[
12]
im
pl
e
m
e
nt
e
d
a
s
ys
te
m
u
s
in
g
th
r
e
e
a
lg
or
it
hm
s
,
de
c
is
io
n
tr
e
e
,
r
a
ndom
f
or
e
s
t,
a
nd
na
iv
e
B
a
ye
s
.
T
h
e
ir
w
or
k
c
ons
is
ts
in
c
om
pa
r
in
g
th
e
pe
r
f
or
m
a
nc
e
s
of
th
e
th
r
e
e
c
la
s
s
if
ie
r
s
us
in
g
K
f
ol
d
c
r
os
s
va
li
da
ti
on,
w
hi
c
h
s
how
e
d
t
ha
t
th
e
s
e
a
lg
or
it
hm
s
ga
ve
ve
r
y
good
a
c
c
ur
a
c
y
w
it
h a
s
m
a
ll
a
dva
nt
a
ge
of
n
a
iv
e
B
a
ye
s
. H
ow
e
ve
r
, l
ooki
ng a
t
th
e
r
e
s
ul
t
of
t
he
e
xa
m
pl
e
gi
ve
n by the
a
ut
hor
,
w
e
f
in
d
th
a
t
na
iv
e
B
a
ye
s
a
nd
d
e
c
is
io
n
tr
e
e
c
la
s
s
if
ie
r
s
gi
ve
th
e
s
a
m
e
r
e
s
ul
t,
w
hi
le
r
a
ndom
f
or
e
s
t
gi
ve
s
a
di
f
f
e
r
e
nt
r
e
s
ul
t.
A
t
th
is
le
ve
l
a
nd
in
g
e
ne
r
a
l
w
e
w
il
l
c
hoo
s
e
th
e
f
a
vor
it
e
r
e
s
ul
t
of
th
e
m
o
s
t
of
th
e
c
la
s
s
if
ie
r
s
,
but
w
it
h
a
pr
e
c
is
io
n
a
s
c
lo
s
e
a
s
th
e
one
on
th
e
e
xa
m
pl
e
th
i
s
c
oul
d
put
th
e
pr
a
c
ti
ti
one
r
in
doubt
if
he
ha
s
to
de
c
id
e
w
hi
c
h
di
s
e
a
s
e
i
s
t
he
m
os
t
pr
oba
bl
e
.
T
he
r
e
f
or
e
, a
c
om
pl
e
m
e
nt
a
r
y di
a
gn
os
is
w
il
l
be
ne
c
e
s
s
a
r
y.
C
he
n
e
t
al
.
[
13]
de
ve
lo
pe
d
a
he
a
r
t
d
is
e
a
s
e
pr
e
di
c
t
s
ys
te
m
by
us
in
g
a
c
om
bi
na
ti
on
o
f
14
a
tt
r
ib
ut
e
s
to
de
te
r
m
in
e
th
e
pr
e
s
e
nc
e
of
he
a
r
t
di
s
e
a
s
e
.
T
he
a
tt
r
ib
ut
e
s
w
e
r
e
a
m
ix
tu
r
e
of
pe
r
s
ona
l
in
f
o
r
m
a
ti
on’
s
a
nd
ot
he
r
s
a
bout
th
e
pr
e
s
e
nc
e
of
s
ym
pt
om
s
.
T
he
us
e
of
a
r
ti
f
ic
ia
l
ne
ur
a
l
ne
twor
k
(
ANN
)
a
s
a
n
a
lg
or
it
h
m
ge
ne
r
a
te
d
good
r
e
s
ul
ts
,
in
c
lu
di
ng
a
n
a
c
c
ur
a
c
y
of
ove
r
80%
.
T
hi
s
e
xpe
r
im
e
nt
le
a
ds
to
t
a
lk
a
bout
th
e
e
f
f
ic
ie
nc
y
of
m
a
c
hi
ne
le
a
r
ni
ng
te
c
hni
c
s
e
ve
n
in
s
om
e
of
th
e
m
os
t
a
dva
nc
e
d
a
nd
c
r
it
ic
a
l
m
e
di
c
a
l
f
ie
ld
s
s
uc
h
a
s
c
a
r
di
ol
ogy.
W
it
h
th
i
s
de
ve
lo
pm
e
nt
of
bi
g
d
a
ta
a
na
ly
s
is
,
di
s
e
a
s
e
pr
e
di
c
ti
on
a
nd
it
s
a
n
a
ly
s
is
m
e
th
ods
a
r
e
r
e
c
e
iv
in
g
m
or
e
a
tt
e
nt
io
n
in
s
c
ie
nt
if
ic
r
e
s
e
a
r
c
h.
I
nde
e
d,
di
f
f
e
r
e
nt
r
e
s
e
a
r
c
he
s
ha
v
e
be
e
n c
ond
uc
te
d
to
im
pr
ove
th
e
c
la
s
s
if
ic
a
ti
on
of
di
s
e
a
s
e
s
a
nd
e
ve
n
th
e
r
is
k
to
de
v
e
lo
p
f
ut
ur
e
di
s
e
a
s
e
s
[
14]
. C
he
n
e
t
al
.
[
1
5]
w
or
ke
d
on s
tr
uc
tu
r
e
d
a
nd
un
s
tr
uc
tu
r
e
d
da
ta
f
r
om
r
e
a
l
li
f
e
hos
pi
ta
l
da
ta
s
e
ts
in
or
de
r
to
be
a
bl
e
to
gi
ve
pr
e
di
c
ti
ons
w
it
h
a
n
e
f
f
e
c
ti
ve
a
c
c
ur
a
c
y.
T
h
e
ir
w
or
k
a
im
s
in
a
not
he
r
s
e
ns
e
to
f
in
d
a
s
ol
ut
io
n
to
th
e
pr
obl
e
m
o
f
m
is
s
in
g
da
ta
th
a
t
c
oul
d
im
pa
c
t
th
e
qua
li
ty
of
th
e
Evaluation Warning : The document was created with Spire.PDF for Python.
I
S
S
N
:
2252
-
8938
I
nt
J
A
r
ti
f
I
nt
e
ll
, V
ol
.
14
, N
o.
3
,
J
une
20
25
:
2072
-
2082
2074
r
e
s
ul
ts
,
th
e
y
f
in
a
ll
y
pr
opos
e
d
a
ne
w
c
onvolut
io
na
l
ne
ur
a
l
ne
t
w
or
k
ba
s
e
d
m
ul
ti
m
oda
l
di
s
e
a
s
e
r
is
k
pr
e
di
c
ti
on
(
C
N
N
-
M
D
R
P
)
th
a
t
ga
ve
a
n
a
c
c
ur
a
c
y
of
94.8%
[
15]
.
I
n
th
e
s
a
m
e
c
ont
e
xt
,
m
a
ny
s
tu
di
e
s
c
ondu
c
te
d
on
c
om
put
e
r
-
a
id
e
d
di
a
gnos
is
(
C
A
D
x)
de
m
ons
tr
a
te
d
th
e
gr
e
a
t
ut
il
it
y
of
m
e
di
c
a
l
di
a
gnos
ti
c
to
ol
s
in
im
pr
ovi
n
g
c
li
ni
c
a
l
de
c
is
io
ns
a
nd r
e
duc
in
g unne
c
e
s
s
a
r
y i
nt
e
r
ve
nt
io
ns
[
16]
, [
17]
.
S
ti
ll
i
n t
he
s
a
m
e
c
ont
e
xt
but
t
hi
s
t
im
e
w
i
th
a
pr
e
di
c
ti
on of
di
s
e
a
s
e
s
us
in
g s
ym
pt
om
s
e
xpr
e
s
s
e
d by the
us
e
r
hi
m
s
e
lf
,
ga
de
ka
r
e
t
al
.
[
18]
w
or
ke
d
on
a
pr
e
di
c
ti
on
s
y
s
te
m
ba
s
e
d
on
a
s
e
t
of
s
ym
pt
om
s
pr
ovi
de
d
by
th
e
pa
ti
e
nt
.
S
uc
h
a
s
ys
te
m
c
a
n
be
us
e
f
ul
to
in
f
or
m
th
e
pa
ti
e
nt
on
t
he
pos
s
ib
le
di
s
e
a
s
e
he
m
a
y
ha
ve
,
but
r
e
m
a
in
s
in
s
uf
f
ic
ie
nt
or
e
ve
n
r
is
ky
if
th
e
pa
ti
e
nt
is
not
pr
e
c
is
e
in
hi
s
di
a
gnos
is
or
if
he
ta
ke
s
th
e
in
it
ia
ti
ve
to
c
ons
um
e
dr
ugs
w
it
hout
m
e
di
c
a
l
opi
ni
on.
I
nde
e
d,
th
e
m
a
jo
r
it
y
of
m
ode
l
s
th
a
t
a
im
a
t
pr
e
di
c
ti
ng
di
s
e
a
s
e
s
a
r
e
b
a
s
e
d
on
a
s
ym
pt
om
/d
is
e
a
s
e
a
ppr
oa
c
h,
s
u
c
h
a
n
a
ppr
oa
c
h
c
a
n
r
e
a
c
h a
good
a
c
c
ur
a
c
y
but
r
e
m
a
in
s
una
bl
e
to
r
e
ve
a
l
if
th
e
r
e
is
a
di
s
e
a
s
e
hi
dde
n be
hi
nd t
he
s
ym
pt
om
s
of
a
not
he
r
or
i
f
a
di
s
e
a
s
e
i
s
pr
e
s
e
nt
a
t
a
n e
a
r
ly
s
ta
g
e
.
3.
M
E
T
H
O
D
3.1. P
r
op
os
e
d
e
xp
e
r
im
e
n
t
al
p
r
oc
e
s
s
O
ur
di
s
e
a
s
e
pr
e
di
c
ti
on
m
ode
l
is
ba
s
e
d
on
a
n
it
e
r
a
ti
ve
a
ppr
oa
c
h,
in
w
hi
c
h
th
e
m
ode
l
i
s
f
e
d
w
it
h
th
e
ne
c
e
s
s
a
r
y
pa
r
a
m
e
te
r
s
th
r
ough
s
e
ve
r
a
l
it
e
r
a
ti
ons
unt
il
th
e
opt
im
um
r
e
s
ul
t
is
obt
a
in
e
d,
a
s
s
how
n
in
th
e
di
a
gr
a
m
in
F
ig
ur
e
1.
I
nde
e
d,
w
he
n
a
pa
ti
e
nt
vi
s
it
s
a
doc
to
r
,
th
e
ir
pr
im
a
r
y
obj
e
c
ti
ve
is
to
a
c
c
ur
a
te
ly
c
onve
y
th
e
ir
s
ym
pt
om
s
to
r
e
c
e
iv
e
a
ppr
opr
ia
te
tr
e
a
tm
e
nt
.
F
or
th
e
doc
to
r
,
th
e
m
a
in
goa
l
is
to
e
li
c
it
c
le
a
r
a
nd
pr
e
c
is
e
in
f
or
m
a
ti
on
a
bout
th
e
pa
ti
e
nt
'
s
h
e
a
lt
h
to
m
a
k
e
a
n
a
c
c
ur
a
te
di
a
g
nos
is
.
O
ur
e
xpe
r
im
e
nt
,
il
lu
s
tr
a
te
d
in
F
ig
ur
e
1,
f
ol
lo
w
s
a
s
tr
uc
tu
r
e
d pr
oc
e
s
s
c
ons
is
ti
ng of
t
he
f
ol
lo
w
in
g s
te
ps
:
‒
S
te
p
1:
th
e
doc
to
r
in
qui
r
ie
s
a
bout
th
e
pa
ti
e
nt
'
s
he
a
lt
h
s
ta
t
us
,
gui
di
ng
th
e
pa
ti
e
nt
to
de
s
c
r
ib
e
th
e
ir
s
ym
pt
om
s
a
nd
f
e
e
li
ngs
. T
he
doc
to
r
'
s
ta
s
k
a
t
th
i
s
s
ta
ge
i
s
to
ve
r
if
y
th
e
pa
ti
e
nt
'
s
s
ta
te
m
e
nt
s
a
nd
g
e
ne
r
a
te
a
n
in
it
ia
l
s
e
t
of
s
ym
pt
om
s
a
nd s
ig
ns
.
‒
S
te
p
2:
th
e
in
it
ia
l
s
e
t
of
s
ym
pt
om
s
a
nd
s
ig
ns
id
e
nt
if
ie
d
by
th
e
doc
to
r
is
in
put
in
to
th
e
m
a
c
hi
ne
le
a
r
ni
ng
m
ode
l
f
or
th
e
f
ir
s
t
i
te
r
a
ti
on.
T
he
m
ode
l
out
put
s
pr
e
di
c
ti
ons
of
l
ik
e
ly
di
s
e
a
s
e
s
,
r
a
nke
d
in
de
s
c
e
ndi
ng
or
de
r
a
c
c
or
di
ng
to
th
e
ir
pr
oba
bi
li
ty
s
c
or
e
s
.
A
ddi
ti
ona
ll
y,
th
e
m
ode
l
s
ugge
s
ts
f
ur
th
e
r
s
ym
pt
om
s
a
nd
s
ig
ns
to
c
he
c
k f
or
e
a
c
h l
is
te
d di
s
e
a
s
e
, a
id
in
g t
he
pr
a
c
ti
ti
one
r
i
n r
e
f
in
in
g t
he
di
a
gnos
is
.
‒
S
te
p
3:
upon
r
e
c
e
iv
in
g
th
e
m
ode
l'
s
pr
e
di
c
ti
ons
,
th
e
doc
to
r
i
nt
e
r
pr
e
ts
th
e
r
e
s
ul
ts
in
th
e
c
ont
e
xt
of
th
e
pa
ti
e
nt
'
s
c
ondi
ti
on.
I
f
th
e
a
s
s
e
s
s
m
e
nt
is
s
uf
f
ic
ie
nt
to
m
a
ke
a
de
c
is
io
n,
th
e
di
a
gnos
is
i
s
c
ons
id
e
r
e
d
opt
im
a
l.
T
he
pr
a
c
ti
ti
one
r
m
a
y
r
e
que
s
t
a
ddi
ti
ona
l
te
s
ts
,
if
ne
c
e
s
s
a
r
y,
to
c
onf
ir
m
th
e
di
a
gnos
is
.
I
f
f
ur
th
e
r
in
ve
s
ti
ga
ti
on
is
r
e
qui
r
e
d,
th
e
doc
to
r
ve
r
if
ie
s
th
e
a
ddi
ti
ona
l
s
y
m
pt
om
s
s
ugge
s
te
d
by
th
e
m
ode
l
w
it
h
th
e
pa
ti
e
nt
.
T
hi
s
pr
oc
e
s
s
c
a
n
b
e
r
e
pe
a
te
d
it
e
r
a
ti
ve
ly
unt
il
th
e
doc
to
r
is
c
onf
id
e
nt
in
th
e
di
a
gnos
is
.
T
he
num
be
r
of
it
e
r
a
ti
ons
is
unl
im
it
e
d
a
nd
de
pe
nds
on
th
e
r
e
s
ul
ts
obt
a
in
e
d
a
nd
th
e
pr
a
c
ti
ti
one
r
'
s
ju
dgm
e
nt
.
T
hi
s
it
e
r
a
ti
ve
a
ppr
oa
c
h he
lp
s
e
li
m
in
a
te
doubtf
ul
c
a
s
e
s
a
nd i
d
e
nt
if
y da
nge
r
ous
c
ondi
ti
ons
a
c
c
ur
a
te
ly
.
‒
S
te
p
4:
onc
e
th
e
doc
to
r
f
in
a
li
z
e
s
th
e
di
a
gnos
is
a
nd
de
te
r
m
in
e
s
th
e
pa
ti
e
nt
'
s
il
ln
e
s
s
,
a
tr
e
a
tm
e
nt
pr
ot
oc
ol
c
a
n be
pl
a
nne
d a
nd i
m
pl
e
m
e
nt
e
d f
or
t
he
pa
ti
e
nt
'
s
c
a
r
e
a
nd f
ol
lo
w
-
up.
I
n
c
onc
lu
s
io
n,
F
ig
ur
e
1
s
um
m
a
r
iz
e
s
th
e
pr
oc
e
s
s
a
dopt
e
d
in
our
s
tu
dy.
I
t
is
a
m
e
di
c
a
l
di
a
gnos
i
s
s
uppor
t
pr
oc
e
s
s
de
s
ig
ne
d
to
he
lp
th
e
doc
to
r
,
w
he
th
e
r
be
gi
nne
r
or
e
xpe
r
ie
nc
e
d,
to
be
tt
e
r
m
a
s
te
r
th
e
di
a
gnos
is
pha
s
e
in
te
r
m
s
of
«T
im
e
m
a
na
ge
m
e
nt
» a
nd «
C
a
s
e
m
a
na
ge
m
e
nt
».
3.2. Dat
a
T
he
da
ta
u
s
e
d
in
th
is
s
tu
dy
c
om
e
f
r
om
th
e
N
L
M
,
lo
c
a
te
d
on
th
e
c
a
m
pus
of
th
e
N
a
ti
ona
l
I
ns
ti
tu
te
s
of
H
e
a
lt
h
in
B
e
th
e
s
da
,
M
a
r
yl
a
nd,
a
c
e
nt
e
r
of
in
f
or
m
a
ti
on
in
no
va
ti
on
s
in
c
e
it
s
f
ounding
in
1836
[
19]
.
T
hi
s
li
br
a
r
y
is
c
ons
id
e
r
e
d
th
e
la
r
ge
s
t
bi
om
e
di
c
a
l
li
br
a
r
y
in
th
e
w
or
ld
,
w
it
h
da
ta
c
ol
le
c
te
d
th
r
ough
a
ne
twor
k
of
ove
r
8000
c
ol
la
bor
a
to
r
s
pr
ovi
di
ng
a
c
c
e
s
s
to
in
f
or
m
a
ti
on
a
c
r
os
s
th
e
U
ni
te
d
S
ta
te
s
.
T
he
N
L
M
ha
s
de
ve
lo
pe
d
a
r
e
pos
it
or
y
c
a
ll
e
d
U
ni
f
ie
d
M
e
di
c
a
l
L
a
ngua
ge
S
ys
t
e
m
(
U
M
L
S
)
w
hi
c
h
is
a
s
e
t
of
f
il
e
s
a
nd
s
of
twa
r
e
th
a
t
br
in
gs
to
ge
th
e
r
m
a
ny
he
a
lt
h
a
nd
bi
om
e
di
c
a
l
voc
a
bul
a
r
ie
s
a
nd
s
ta
n
da
r
ds
to
e
na
bl
e
in
te
r
ope
r
a
bi
li
ty
be
twe
e
n
I
T
s
ys
te
m
s
[
20]
.
I
t
in
te
gr
a
te
s
m
or
e
th
a
n
2
m
il
li
on
na
m
e
s
f
or
a
ppr
o
xi
m
a
te
ly
900,000
c
onc
e
pt
s
f
r
om
m
or
e
th
a
n
60
f
a
m
il
ie
s
of
bi
om
e
di
c
a
l
voc
a
bul
a
r
ie
s
.
I
t
a
ls
o
in
te
gr
a
te
s
12
m
il
li
on
r
e
la
ti
ons
hi
ps
be
twe
e
n
th
e
s
e
c
on
c
e
pt
s
[
21]
.
V
oc
a
bul
a
r
ie
s
in
te
gr
a
te
d
in
to
th
e
U
M
L
S
m
e
ta
th
e
s
a
ur
us
in
c
lu
de
th
e
N
C
B
I
ta
xonomy
,
ge
ne
ont
ol
ogy
,
m
e
di
c
a
l
s
ubj
e
c
t
he
a
di
ng
s
(
M
e
S
H
)
,
onl
in
e
m
e
nde
li
a
n
in
he
r
it
a
nc
e
in
m
a
n
(
O
M
I
M
)
,
a
nd
th
e
di
gi
ta
l
a
na
to
m
is
t
s
ym
bol
ic
knowle
dge
ba
s
e
a
s
i
ll
us
tr
a
te
d i
n F
ig
ur
e
2
[
20]
, [
21]
.
F
or
our
s
tu
dy,
th
e
m
a
in
obj
e
c
ti
ve
i
s
to
e
xt
r
a
c
t
da
ta
f
r
om
U
M
L
S
da
ta
ba
s
e
in
or
de
r
to
a
s
s
oc
ia
te
di
s
e
a
s
e
s
w
it
h
th
e
ir
s
ym
pt
om
s
.
E
a
c
h
di
s
e
a
s
e
is
id
e
nt
if
ie
d
by
a
c
ode
“
C
U
I
”
m
a
de
up
of
a
n
a
lp
ha
be
ti
c
a
l
c
ha
r
a
c
te
r
,
w
hi
c
h
in
our
c
a
s
e
is
th
e
le
tt
e
r
“
C
”
,
f
ol
lo
w
e
d
by
a
num
be
r
of
s
e
ve
n
di
gi
ts
,
gi
vi
ng
a
c
ode
in
th
e
f
ol
lo
w
in
g
f
or
m
a
t
“
C
xxxxxxx”
.
f
or
e
xa
m
pl
e
,
if
w
e
ta
k
e
“
H
e
p
a
ti
ti
s
C
”
a
s
th
e
di
s
e
a
s
e
,
w
e
’
ll
f
in
d
th
e
f
ol
lo
w
in
g
c
ode
“
C
0019196”
a
s
s
oc
ia
te
d w
it
h i
t.
F
or
s
ym
pt
om
s
, w
e
us
e
t
he
s
a
m
e
c
ode
f
or
m
a
t,
i
.e
. t
he
l
e
tt
e
r
“
C
”
f
ol
lo
w
e
d
by
s
e
ve
n
di
gi
ts
,
e
.g.
f
or
“
H
e
a
da
c
h
e
”
w
e
ha
ve
th
e
f
ol
lo
w
in
g
c
ode
“
C
0018681”
,
but
th
e
pa
r
ti
c
ul
a
r
it
y
of
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de
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to
ge
t
th
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r
ig
h
t
da
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t
f
or
our
s
tu
dy,
th
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om
th
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N
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M
ne
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e
tr
a
ns
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ti
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ta
in
r
e
le
va
nt
da
ta
a
nd r
e
li
a
bl
e
r
e
s
ul
ts
.
F
ig
ur
e
1. G
lo
ba
l
s
c
he
m
e
of
t
he
e
xpe
r
im
e
nt
F
ig
ur
e
2. T
he
va
r
io
us
s
ubdoma
in
s
i
nt
e
gr
a
te
d i
n t
he
U
M
L
S
f
r
om
t
he
N
I
H
s
our
c
e
s
Evaluation Warning : The document was created with Spire.PDF for Python.
I
S
S
N
:
2252
-
8938
I
nt
J
A
r
ti
f
I
nt
e
ll
, V
ol
.
14
, N
o.
3
,
J
une
20
25
:
2072
-
2082
2076
T
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pos
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3.3. Dat
a
p
r
e
p
r
oc
e
s
s
in
g
A
s
pr
e
vi
ous
ly
m
e
nt
io
ne
d,
th
e
da
ta
c
ol
le
c
te
d
ne
e
de
d
s
om
e
pr
e
-
pr
oc
e
s
s
in
g
to
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xt
r
a
c
t
w
ha
t
w
a
s
ne
e
de
d
f
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th
is
w
or
k.
D
e
s
pi
te
ha
vi
ng
a
c
c
e
s
s
to
a
va
s
t
qua
nt
i
ty
of
da
ta
,
not
a
ll
of
it
w
a
s
e
s
s
e
nt
ia
l
f
or
our
pur
pos
e
s
.
O
ur
pr
im
a
r
y
c
h
a
ll
e
nge
w
a
s
to
e
xt
r
a
c
t
a
da
ta
s
e
t
th
a
t
a
c
c
ur
a
te
ly
li
nke
d
e
a
c
h
di
s
e
a
s
e
w
it
h
it
s
kno
w
n
s
ym
pt
om
s
.
O
f
c
our
s
e
,
to
a
c
hi
e
ve
a
c
c
ur
a
te
a
nd
r
e
le
v
a
nt
r
e
s
ul
ts
,
th
e
da
ta
n
e
e
de
d
to
be
th
or
oughly
c
le
a
ne
d
a
nd
ve
r
if
ie
d.
T
he
f
ir
s
t
s
te
p
in
our
pr
e
pr
oc
e
s
s
in
g
w
a
s
to
li
s
t
th
e
di
s
e
a
s
e
c
od
e
s
a
nd
s
ym
pt
om
c
ode
s
,
a
ll
ow
in
g
us
to
c
he
c
k
th
e
ir
f
or
m
a
ts
a
nd
id
e
nt
if
y
a
ny
m
is
s
in
g
va
lu
e
s
.
W
e
th
e
n
m
a
ppe
d
th
e
s
ym
pt
om
s
a
s
s
oc
ia
te
d
w
it
h
e
a
c
h
di
s
e
a
s
e
to
e
s
ta
bl
is
h
a
n
in
it
ia
l
m
od
e
l
th
a
t
c
oul
d
li
s
t
a
ll
known
s
ym
pt
om
s
f
or
a
gi
ve
n
di
s
e
a
s
e
.
T
he
r
e
s
ul
ti
ng
m
ode
l
is
i
ll
us
tr
a
te
d i
n F
ig
ur
e
3.
A
t
th
is
s
ta
ge
,
w
e
ha
d
a
n
in
it
ia
l
unde
r
s
t
a
ndi
ng
of
th
e
s
tr
uc
tu
r
e
of
our
da
ta
s
e
t;
how
e
ve
r
,
th
e
c
ur
r
e
nt
f
or
m
a
t
w
a
s
not
s
ui
ta
bl
e
f
or
in
put
in
to
th
e
m
a
c
hi
ne
le
a
r
ni
ng
m
ode
l.
A
ddi
ti
ona
ll
y,
th
e
e
xi
s
ti
ng
f
or
m
a
t
c
ont
a
in
e
d
s
ig
ni
f
ic
a
nt
dupl
ic
a
ti
ons
,
w
hi
c
h
w
e
r
e
unn
e
c
e
s
s
a
r
y
a
nd
c
oul
d
pot
e
nt
ia
ll
y
hi
nde
r
th
e
a
na
ly
s
is
.
T
o
a
ddr
e
s
s
th
is
is
s
u
e
,
w
e
tr
a
n
s
f
or
m
e
d
th
e
da
ta
s
e
t
to
a
dopt
a
bi
na
r
y
lo
gi
c
,
in
di
c
a
ti
ng
th
e
pr
e
s
e
n
c
e
or
a
b
s
e
nc
e
of
s
ym
pt
om
s
f
or
e
a
c
h
gi
ve
n
di
s
e
a
s
e
.
T
hi
s
tr
a
ns
f
or
m
a
ti
on
a
ll
ow
e
d
us
to
c
r
e
a
te
a
da
ta
s
e
t
th
a
t
c
oul
d
be
e
f
f
e
c
ti
ve
ly
ut
il
iz
e
d
by
m
a
c
hi
ne
le
a
r
ni
ng
a
lg
or
it
hm
s
w
hi
le
e
li
m
in
a
ti
ng
dupl
ic
a
ti
ons
th
a
t
m
ig
ht
a
f
f
e
c
t
r
e
s
ul
t
a
c
c
ur
a
c
y.
T
he
ne
w
da
ta
s
e
t
is
or
ga
ni
z
e
d
in
a
ta
bul
a
r
f
or
m
a
t,
w
it
h
r
ow
s
r
e
p
r
e
s
e
nt
in
g
di
s
e
a
s
e
s
a
nd
c
ol
um
ns
r
e
pr
e
s
e
nt
in
g
s
ym
pt
om
s
.
T
o
be
tt
e
r
e
xpl
a
in
how
th
e
da
ta
s
e
t
w
or
ks
,
le
t’
s
ta
ke
th
e
c
a
s
e
of
he
pa
ti
ti
s
C
a
ga
in
,
f
or
known
s
ym
pt
om
s
of
t
hi
s
di
s
e
a
s
e
w
e
’
ll
put
one
s
‘
1
’
, ot
he
r
w
is
e
w
e
’
ll
pu
t
z
e
r
os
‘
0’
.
A
s
a
r
e
s
ul
t,
our
da
ta
s
e
t
w
il
l
a
ppe
a
r
in
th
e
f
or
m
pr
e
s
e
nt
e
d
in
th
e
F
ig
ur
e
4.
F
in
a
ll
y,
our
d
a
ta
s
e
t
c
ont
a
in
s
398
r
ow
s
a
s
di
s
e
a
s
e
s
a
nd
1016
c
ol
um
ns
a
s
s
ym
pt
om
s
w
it
h
no
dupl
ic
a
te
a
tt
r
ib
ut
e
s
.
I
t
s
houl
d
be
not
e
d
th
a
t
th
e
s
e
va
lu
e
s
a
r
e
s
ubj
e
c
t
to
c
ha
nge
,
a
s
da
ta
is
pe
r
io
di
c
a
ll
y upda
te
d a
t
s
our
c
e
.
F
ig
ur
e
3. F
ir
s
t
r
e
s
ul
t
of
l
is
ti
ng s
om
e
di
s
e
a
s
e
s
a
nd t
he
ir
s
ym
pt
o
m
s
3.4. Naive
B
aye
s
N
a
iv
e
B
a
ye
s
is
a
s
im
pl
e
s
upe
r
vi
s
e
d
a
lg
or
it
hm
ba
s
e
d
on B
a
ye
s
’
th
e
or
e
m
,
a
m
a
th
e
m
a
ti
c
a
l
c
onc
e
pt
f
or
obt
a
in
in
g
pr
oba
bi
li
ti
e
s
.
I
t
a
s
s
um
e
s
s
tr
ong
(
na
iv
e
)
in
de
p
e
nde
nc
e
be
twe
e
n
a
tt
r
ib
ut
e
s
a
nd
a
tt
e
m
pt
s
to
m
a
xi
m
iz
e
th
e
pos
te
r
io
r
pr
oba
bi
li
ty
in
de
te
r
m
in
in
g
th
e
c
l
a
s
s
.
P
r
e
di
c
to
r
s
a
r
e
unr
e
la
te
d
a
nd
ha
ve
no
c
or
r
e
la
ti
on
to
one
Evaluation Warning : The document was created with Spire.PDF for Python.
I
nt
J
A
r
ti
f
I
nt
e
ll
I
S
S
N
:
2252
-
8938
A
s
y
m
pt
om
-
dr
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n m
e
di
c
al
di
agno
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t
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ode
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ac
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c
hni
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s
(
A
di
l
L
aabi
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)
2077
a
not
he
r
,
but
a
ll
a
tt
r
ib
ut
e
s
c
ont
r
ib
ut
e
in
de
pe
nde
nt
ly
to
m
a
xi
m
iz
e
th
e
pr
oba
bi
li
ty
[
11]
.
W
e
c
a
n
us
e
th
e
na
iv
e
B
a
ye
s
m
od
e
l
w
it
hout
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in
g
a
ny
B
a
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ia
n
m
e
th
od
s
.
H
ow
e
ve
r
,
na
iv
e
B
a
ye
s
s
ti
ll
one
of
th
e
be
s
t
c
la
s
s
if
ie
r
s
f
or
de
a
li
ng w
it
h m
e
di
c
a
l
r
e
a
l
-
w
or
ld
da
ta
s
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ts
a
nd e
ve
n c
om
pl
e
x on
e
s
[
22]
.
(
/
)
=
(
⁄
)
×
(
)
(
)
(
1)
W
he
r
e
P
(
X
/Y)
is
th
e
pos
te
r
io
r
p
r
oba
bi
li
ty
,
P
(
X
)
is
th
e
c
la
s
s
pr
io
r
pr
oba
bi
li
ty
,
P
(
Y
)
is
th
e
pr
e
di
c
to
r
pr
io
r
pr
oba
bi
li
ty
,
a
nd
P
(
Y
/X
)
is
th
e
li
ke
li
hood
p
r
oba
bi
li
ty
of
pr
e
di
c
to
r
.
N
a
iv
e
B
a
ye
s
r
e
m
a
in
s
a
s
im
pl
e
,
e
a
s
y
-
to
-
im
pl
e
m
e
nt
a
nd
e
f
f
ic
ie
nt
a
lg
or
it
hm
th
a
t
c
a
n
ha
ndl
e
non
-
li
ne
a
r
a
nd
c
om
pl
ic
a
te
d
d
a
ta
.
H
ow
e
ve
r
,
a
c
c
or
di
ng
to
s
om
e
s
tu
di
e
s
,
th
e
r
e
is
a
lo
s
s
of
a
c
c
ur
a
c
y
due
to
th
e
f
a
c
t
th
a
t
th
is
a
lg
or
it
hm
is
ba
s
e
d
on
th
e
a
s
s
um
pt
io
n a
nd c
la
s
s
c
ondi
ti
ona
l
in
de
pe
nde
nc
e
[
11]
.
F
ig
ur
e
4. D
is
e
a
s
e
/s
ym
pt
om
s
da
ta
s
e
t
f
in
a
l
f
or
m
3.5. M
e
t
h
od
ol
ogy
T
he
f
ol
lo
w
in
g
s
e
c
ti
on
pr
e
s
e
nt
s
th
e
m
a
in
obj
e
c
ti
ve
s
,
da
ta
s
tr
uc
tu
r
e
,
a
nd
a
ppl
ic
a
ti
on
of
our
d
is
e
a
s
e
de
te
c
ti
on mode
l
w
it
hi
n t
he
c
ont
e
xt
of
e
nha
nc
e
d di
a
gno
s
is
.
‒
M
a
in
obj
e
c
ti
ve
:
O
ur
pr
im
a
r
y
obj
e
c
ti
ve
is
not
onl
y
to
de
te
c
t
di
s
e
a
s
e
s
a
s
s
o
c
ia
te
d
w
it
h
obs
e
r
ve
d
s
ym
pt
om
s
but
a
ls
o
to
id
e
nt
if
y
di
s
e
a
s
e
s
th
a
t
m
a
y
be
pr
e
s
e
nt
a
t
a
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e
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r
ly
s
ta
ge
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th
o
s
e
th
a
t
a
r
e
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e
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s
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r
e
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o
a
c
hi
e
ve
t
hi
s
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e
e
m
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oye
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n i
nput
/o
ut
put
m
ode
l
w
he
r
e
s
ym
pt
om
s
a
r
e
us
e
d
a
s
in
put
to
ge
ne
r
a
te
a
n
out
put
r
e
pr
e
s
e
nt
in
g
th
e
d
e
te
c
te
d
di
s
e
a
s
e
s
.
O
ur
a
ppr
oa
c
h
is
uni
que
in
th
a
t
w
e
do
not
s
ol
e
ly
id
e
nt
if
y
th
e
m
os
t
pr
oba
bl
e
di
s
e
a
s
e
;
in
s
t
e
a
d,
w
e
pr
oduc
e
a
n
out
put
th
a
t
in
c
lu
de
s
va
r
io
us
pot
e
nt
ia
l
di
s
e
a
s
e
s
r
a
nke
d i
n de
s
c
e
ndi
ng or
de
r
of
t
he
ir
pr
oba
bi
li
ty
s
c
or
e
s
.
‒
D
a
ta
a
nd s
tr
a
te
gy:
T
o i
m
pl
e
m
e
nt
t
hi
s
w
or
k, our
da
ta
s
e
t
is
s
tr
uc
t
ur
e
d i
n a
di
s
e
a
s
e
s
/s
ym
pt
om
s
f
or
m
a
t,
w
it
h
r
ow
s
r
e
pr
e
s
e
nt
in
g
di
s
e
a
s
e
s
a
nd
c
ol
um
ns
r
e
pr
e
s
e
nt
in
g
s
ym
pt
om
s
.
E
a
c
h
di
s
e
a
s
e
is
a
s
s
oc
ia
te
d
w
it
h
it
s
known
s
ym
pt
om
s
us
in
g
bi
na
r
y
lo
gi
c
:
'
1'
in
di
c
a
te
s
th
e
pr
e
s
e
n
c
e
of
a
s
ym
pt
om
,
w
hi
le
'
0'
in
di
c
a
te
s
it
s
a
bs
e
nc
e
,
a
s
s
how
n
in
T
a
bl
e
2.
T
he
da
t
a
s
e
t
is
th
e
n
di
vi
de
d
in
to
tr
a
in
in
g
a
nd
te
s
ti
ng
s
ubs
e
ts
,
w
it
h
a
ppr
oxi
m
a
te
ly
70%
of
th
e
da
ta
a
ll
oc
a
te
d
f
or
tr
a
in
in
g
a
nd
30%
f
or
te
s
ti
ng
(
te
s
t
-
s
iz
e
=
0.3)
.
T
hi
s
s
tr
a
te
gy
e
ns
ur
e
s
th
a
t
our
m
ode
l
c
a
n
be
e
f
f
e
c
ti
ve
ly
tr
a
in
e
d
a
nd
va
li
d
a
te
d
f
or
a
c
c
ur
a
te
di
s
e
a
s
e
de
te
c
ti
on
a
nd
di
a
gnos
is
.
‒
A
ppl
ic
a
ti
on
a
nd
di
a
gnos
ti
c
e
nha
nc
e
m
e
nt
:
C
on
s
id
e
r
a
pa
ti
e
n
t
pr
e
s
e
nt
in
g
w
it
h
va
r
io
us
s
ym
pt
om
s
of
di
f
f
e
r
in
g
s
e
ve
r
it
y.
W
e
f
ir
s
t
c
a
ta
lo
g
th
e
s
e
s
ym
pt
om
s
a
nd
c
onve
r
t
th
e
m
in
to
c
ode
d
f
or
m
f
or
in
put
in
to
our
m
ode
l.
T
he
m
ode
l
th
e
n ge
ne
r
a
te
s
a
n output
l
is
t
of
pot
e
nt
ia
l
di
s
e
a
s
e
s
, e
a
c
h a
c
c
om
p
a
ni
e
d by it
s
pr
oba
bi
li
ty
of
be
in
g
pr
e
s
e
nt
in
th
e
pa
ti
e
nt
.
A
n
e
s
s
e
nt
ia
l
a
s
pe
c
t
of
our
a
ppr
oa
c
h
is
th
a
t
f
or
e
a
c
h
de
t
e
c
te
d
di
s
e
a
s
e
,
th
e
m
ode
l
s
ugge
s
ts
a
ddi
ti
ona
l
s
ym
pt
om
s
f
or
th
e
pr
a
c
ti
ti
one
r
to
v
e
r
if
y.
T
hi
s
pr
ovi
de
s
th
e
pr
a
c
ti
ti
one
r
w
it
h
c
om
pr
e
he
ns
iv
e
in
f
or
m
a
ti
on,
f
a
c
il
it
a
ti
ng
a
m
or
e
th
or
ough
a
nd
a
c
c
ur
a
te
di
a
gno
s
is
a
nd
e
n
a
bl
in
g
th
e
m
os
t
a
ppr
opr
ia
te
c
li
ni
c
a
l
de
c
is
io
n
-
m
a
ki
ng.
3.6. P
e
r
f
or
m
an
c
e
e
val
u
at
io
n
c
r
it
e
r
ia
T
o
e
va
lu
a
te
th
e
pe
r
f
or
m
a
nc
e
of
a
c
la
s
s
if
ie
r
,
it
is
n
e
c
e
s
s
a
r
y
to
in
tr
oduc
e
two
im
por
ta
nt
pa
r
a
m
e
te
r
s
:
s
e
ns
it
iv
it
y
a
nd
s
pe
c
if
ic
it
y.
W
h
e
n
s
e
ns
it
iv
it
y
r
e
pr
e
s
e
nt
s
th
e
c
l
a
s
s
if
ie
r
’
s
e
f
f
e
c
ti
ve
ne
s
s
in
id
e
nt
if
yi
ng
pos
it
iv
e
s
a
m
pl
e
s
to
th
e
to
ta
l
a
c
tu
a
l
pos
it
iv
e
in
s
ta
n
c
e
s
,
s
p
e
c
if
ic
it
y
de
f
in
e
s
th
e
r
a
ti
o
of
in
c
or
r
e
c
tl
y
pr
e
di
c
te
d
pos
it
iv
e
in
s
ta
nc
e
s
to
th
e
to
ta
l
a
c
tu
a
l
ne
ga
ti
ve
in
s
ta
nc
e
s
.
O
n
th
e
ot
he
r
ha
nd,
th
e
a
c
c
ur
a
c
y
m
e
a
s
ur
e
s
th
e
ove
r
a
ll
c
or
r
e
c
tn
e
s
s
of
th
e
m
ode
l’
s
pr
e
di
c
ti
ons
.
I
t
c
ons
id
e
r
s
bot
h
tr
ue
pos
it
iv
e
a
nd
tr
ue
n
e
ga
ti
ve
in
s
t
a
nc
e
s
.
I
t
c
a
n
th
e
r
e
f
or
e
be
c
a
lc
ul
a
te
d a
s
f
ol
lo
w
s
:
Evaluation Warning : The document was created with Spire.PDF for Python.
I
S
S
N
:
2252
-
8938
I
nt
J
A
r
ti
f
I
nt
e
ll
, V
ol
.
14
, N
o.
3
,
J
une
20
25
:
2072
-
2082
2078
S
e
n
s
it
iv
it
y
=
+
×
100
(
2)
S
p
e
c
if
ic
it
y
=
+
×
100
(
3)
=
+
+
+
+
(
4)
I
n
a
ddi
ti
on,
th
e
F
-
s
c
or
e
or
F
1
s
c
or
e
is
us
e
d
to
m
e
a
s
ur
e
th
e
pe
r
f
or
m
a
nc
e
of
our
m
ode
l.
I
t
is
a
m
e
tr
ic
c
om
m
onl
y
us
e
d
in
m
a
c
hi
ne
le
a
r
ni
ng
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nd
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ta
ti
s
ti
c
s
to
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v
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lu
a
te
t
he
pe
r
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a
nc
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ti
on
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ode
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a
nd
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f
or
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e
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l
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ba
la
nc
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pr
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m
s
. I
t
c
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a
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tw
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n pr
e
c
is
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e
c
a
ll
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nd
is
pa
r
ti
c
ul
a
r
ly
us
e
f
ul
w
he
n t
he
r
e
i
s
a
n une
v
e
n c
la
s
s
di
s
tr
ib
ut
io
n. F
1
s
c
or
e
i
s
obt
a
in
e
d a
s
f
ol
lo
w
s
:
P
r
e
c
is
io
n
=
+
(
5)
R
e
c
a
l
l
=
+
(
6)
F
-
s
c
or
e
i
s
t
he
ha
r
m
oni
c
m
e
a
n
of
pr
e
c
is
io
n a
nd r
e
c
a
ll
.
F
−
s
c
o
r
e
=
2
×
×
+
(
7)
T
a
bl
e
2. P
r
e
di
c
te
d di
s
e
a
s
e
s
s
or
te
d by de
s
c
e
ndi
ng pr
oba
bi
li
ty
s
c
or
e
(
I
te
r
a
ti
on 1)
S
ym
pt
om
s
P
r
e
di
c
t
e
d di
s
e
a
s
e
s
P
r
oba
bi
l
i
t
y s
c
or
e
A
ddi
t
i
ona
l
s
i
gns
or
s
ym
pt
om
s
t
o
c
he
c
k
-
F
e
ve
r
-
C
ough
-
H
e
a
da
c
he
-
F
a
t
i
gue
A
c
ut
e
br
onc
hi
t
i
s
e
3.165
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R
hi
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t
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s
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a
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4.
R
E
S
U
L
T
S
A
N
D
D
I
S
C
U
S
S
I
O
N
O
ur
s
tu
dy
w
a
s
de
s
ig
ne
d
to
f
a
c
il
it
a
te
it
e
r
a
ti
ve
de
e
p
di
a
gnos
is
f
or
pr
a
c
ti
ti
one
r
s
.
I
ni
ti
a
ll
y,
w
e
ut
il
iz
e
d
onl
y f
our
s
ym
pt
om
s
f
or
our
c
la
s
s
if
ic
a
ti
on, a
s
de
pi
c
te
d i
n T
a
bl
e
3, a
nd s
ubs
e
que
nt
ly
us
e
d our
m
ode
l
to
pr
e
di
c
t
th
e
di
s
e
a
s
e
s
.
A
s
il
lu
s
tr
a
te
d
in
T
a
bl
e
3,
th
e
out
put
is
a
li
s
t
of
di
s
e
a
s
e
s
r
a
nke
d
in
de
s
c
e
ndi
ng
or
de
r
ba
s
e
d
on
th
e
ir
pr
oba
bi
li
ty
s
c
or
e
s
,
s
ta
r
ti
ng
w
it
h
th
e
di
s
e
a
s
e
ha
vi
ng
th
e
hi
ghe
s
t
pr
oba
bi
li
ty
s
c
or
e
,
f
ol
lo
w
e
d
by
th
e
ne
x
t
hi
ghe
s
t
.
A
t
th
is
in
it
ia
l
s
ta
ge
,
e
a
c
h
pr
e
di
c
te
d
di
s
e
a
s
e
i
s
not
e
va
lu
a
te
d
f
or
it
s
s
e
ve
r
it
y
but
r
a
th
e
r
f
o
r
it
s
pr
oba
bi
li
ty
of
be
in
g
pr
e
s
e
nt
.
T
he
pr
a
c
ti
ti
one
r
c
a
n
th
e
n
c
hoos
e
to
de
e
pe
n
th
e
di
a
gno
s
is
by
e
xa
m
in
in
g
th
e
pr
oba
bi
li
ty
s
c
or
e
s
a
nd
a
na
ly
z
in
g
e
a
c
h
di
s
e
a
s
e
f
r
om
a
s
e
ve
r
it
y
pe
r
s
pe
c
ti
ve
.
T
a
bl
e
3
in
di
c
a
te
s
th
a
t
th
e
f
ir
s
t
it
e
r
a
ti
on
r
e
ve
a
le
d
di
s
e
a
s
e
s
of
va
r
yi
ng
s
e
ve
r
it
y,
a
nd
th
e
m
ode
l
s
ugge
s
ts
a
ddi
ti
ona
l
s
ig
ns
a
nd
s
ym
pt
om
s
to
ve
r
if
y, t
he
r
e
by guidi
ng t
he
pr
a
c
ti
ti
one
r
t
ow
a
r
ds
a
m
or
e
c
om
pr
e
he
ns
iv
e
di
a
gnos
i
s
.
F
ir
s
t,
l
e
t'
s
ha
ve
a
l
ook to t
he
r
e
s
ul
ts
ge
ne
r
a
te
d by our
m
a
c
hi
ne
l
e
a
r
ni
ng mode
l
in
t
e
r
m
s
of
pr
oba
bi
li
ty
of
oc
c
ur
r
e
nc
e
a
nd
s
e
ve
r
it
y.
I
n
our
f
ir
s
t
it
e
r
a
ti
on,
th
e
m
os
t
p
r
oba
bl
e
di
s
e
a
s
e
is
‘
A
c
ut
e
b
r
onc
hi
ti
s
’
w
it
h
a
pr
oba
bi
li
ty
of
3.165
%
.
A
c
ut
e
br
onc
hi
ti
s
is
not
in
it
s
e
lf
a
da
nge
r
ous
di
s
e
a
s
e
,
but
r
a
th
e
r
a
s
e
lf
-
li
m
it
in
g
c
ondi
ti
on
th
a
t
w
il
l
di
s
a
pp
e
a
r
a
f
te
r
a
f
e
w
da
y
s
or
w
e
e
k
[
23]
.
B
ut
th
is
doe
s
not
pr
e
v
e
nt
th
e
di
s
e
a
s
e
f
r
om
ha
vi
ng
c
om
pl
ic
a
ti
ons
in
c
e
r
ta
in
c
a
s
e
s
[
23]
,
a
nd
it
is
w
hy
th
e
pr
a
c
ti
ti
one
r
m
us
t
e
ns
ur
e
th
a
t
it
is
in
de
e
d
th
e
di
s
e
a
s
e
in
que
s
ti
on
in
or
de
r
to
a
nt
ic
ip
a
te
c
om
pl
ic
a
ti
ons
.
I
n
s
e
c
ond
pl
a
c
e
,
w
e
f
in
d
m
yoc
a
r
di
ti
s
w
it
h
a
pr
oba
bi
li
ty
of
1.588%
,
but
th
e
s
c
or
e
is
now
he
r
e
ne
a
r
th
a
t
o
f
a
c
ut
e
br
onc
hi
ti
s
,
s
in
c
e
th
e
r
e
is
a
di
f
f
e
r
e
nc
e
of
m
o
r
e
th
a
n
ha
lf
,
w
hi
c
h
is
e
nor
m
ous
,
e
s
pe
c
ia
ll
y
w
he
n
it
c
onc
e
r
ns
th
e
m
e
di
c
a
l
f
ie
ld
.
I
n
f
a
c
t,
th
e
pr
oba
bi
l
it
y
of
de
ve
lo
pi
ng
m
yoc
a
r
di
ti
s
is
s
m
a
ll
,
but
s
ti
ll
a
la
r
m
in
g,
gi
ve
n
th
a
t
th
e
di
s
e
a
s
e
is
da
nge
r
ous
,
e
s
pe
c
ia
ll
y
if
you
know
th
a
t
it
r
e
m
a
in
s
a
s
ym
pt
om
a
ti
c
in
m
os
t
c
a
s
e
s
,
or
unnoti
c
e
a
bl
e
s
in
c
e
th
e
s
ym
pt
om
s
a
r
e
e
c
li
ps
e
d
by
th
o
s
e
of
th
e
Evaluation Warning : The document was created with Spire.PDF for Python.
I
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J
A
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ti
f
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e
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I
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S
N
:
2252
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8938
A
s
y
m
pt
om
-
dr
iv
e
n m
e
di
c
al
di
agno
s
is
s
uppo
r
t
m
ode
l
bas
e
d on
m
ac
hi
ne
le
ar
ni
ng t
e
c
hni
que
s
(
A
di
l
L
aabi
di
)
2079
in
f
la
m
m
a
ti
on
th
a
t
tr
ig
ge
r
e
d
th
e
di
s
e
a
s
e
[
24]
.
T
he
pr
e
s
e
nc
e
of
s
u
c
h
a
di
s
e
a
s
e
in
a
n
a
dva
n
c
e
d
s
ta
ge
(
R
a
nk
2
in
our
c
a
s
e
)
w
il
l
c
e
r
ta
in
ly
le
a
d
th
e
doc
to
r
to
di
a
gn
os
e
it
m
or
e
th
or
oughly,
in
or
de
r
to
pr
e
ve
nt
c
om
pl
ic
a
ti
ons
s
u
c
h
a
s
he
a
r
t
f
a
il
ur
e
a
nd,
in
r
a
r
e
c
a
s
e
s
,
c
a
r
di
og
e
ni
c
s
hoc
k
[
24]
.
F
or
th
e
ne
xt
th
r
e
e
r
a
nks
,
th
e
pr
oba
bi
li
ty
of
di
s
e
a
s
e
i
s
r
e
la
ti
ve
ly
c
lo
s
e
to
th
a
t
of
m
yoc
a
r
di
ti
s
,
e
xc
e
pt
th
a
t
th
e
s
e
v
e
r
it
y
of
th
e
th
r
e
e
di
s
e
a
s
e
s
va
r
ie
s
w
id
e
ly
. P
ont
ia
c
f
e
ve
r
i
s
r
a
nke
d t
hi
r
d w
it
h a
s
c
or
e
of
1.57
6%
, t
hi
s
di
s
e
a
s
e
i
s
c
ons
id
e
r
e
d be
ni
gn a
nd a
ls
o
s
e
lf
-
li
m
it
in
g
a
nd
c
a
n
be
m
is
ta
ke
n
f
or
s
im
pl
e
f
lu
[
25]
.
I
n
f
o
r
th
p
os
it
io
n
s
c
a
r
le
t
f
e
ve
r
is
pr
e
s
e
nt
w
it
h
a
s
c
or
e
of
1.552%
,
it
is
a
c
hi
ld
hood
il
ln
e
s
s
c
ur
r
e
nt
ly
c
ons
id
e
r
e
d
a
s
m
il
d
di
s
e
a
s
e
if
c
or
r
e
c
tl
y
tr
e
a
te
d
[
26]
.
L
ik
e
m
yoc
a
r
di
ti
s
,
L
e
gi
onna
ir
e
s
’
di
s
e
a
s
e
(
L
e
gi
one
ll
a
pne
um
oni
a
)
is
pr
e
s
e
nt
w
it
h
a
pr
oba
bi
li
ty
of
1.546
%
.
T
hi
s
is
not
a
ve
r
y
hi
gh
s
c
or
e
,
but
it
’
s
im
por
ta
nt
f
or
th
e
doc
to
r
not
t
o
ove
r
lo
ok
th
is
r
e
s
ul
t,
a
s
th
e
di
s
e
a
s
e
i
s
ve
r
y
da
nge
r
ous
,
a
nd
a
s
th
e
pr
ognos
is
of
pa
ti
e
nt
s
de
pe
nd
s
a
bov
e
a
ll
on
two
f
a
c
to
r
s
:
“
w
he
n
th
e
di
s
e
a
s
e
w
a
s
di
a
gnos
e
d”
a
nd
“
w
h
e
n
tr
e
a
tm
e
nt
w
a
s
in
s
ti
tu
te
d”
[
27]
.
I
t
s
houl
d
be
m
e
nt
io
ne
d
th
a
t
th
e
m
or
ta
li
ty
r
a
te
a
m
ong
s
e
ni
or
c
it
iz
e
ns
va
r
ie
s
be
twe
e
n
10
a
nd
50%
[
28]
,
w
hi
c
h
is
ve
r
y
a
la
r
m
in
g,
he
nc
e
th
e
ne
e
d
f
or
e
a
r
ly
di
a
gnos
is
.
T
he
r
e
s
t
of
our
li
s
t
in
c
lu
de
s
br
onc
hopne
um
oni
a
w
it
h
a
s
c
or
e
of
1.493%
,
a
d
is
e
a
s
e
th
a
t
is
ve
r
y
c
om
m
on
in
c
hi
ld
r
e
n
a
nd
r
e
qui
r
e
s
s
p
e
c
ia
l
a
tt
e
nt
io
n
to
pr
e
ve
nt
c
om
pl
ic
a
ti
o
ns
.
I
t’
s
a
pot
e
nt
ia
ll
y
f
a
ta
l
di
s
e
a
s
e
,
a
s
s
ta
ti
s
ti
c
s
s
how
th
a
t
m
or
e
th
a
n
920,000
c
hi
ld
r
e
n
unde
r
th
e
a
ge
of
5
di
e
d
of
pne
um
oni
a
,
m
a
in
ly
br
onc
hopne
um
oni
a
,
in
2015
[
29]
.
T
he
n
w
e
ha
ve
C
hi
c
ke
npox
(
va
r
ic
e
ll
a
)
a
nd
s
a
r
c
oi
d
os
is
w
it
h
0.803%
,
th
e
two
di
s
e
a
s
e
s
h
a
ve
th
e
s
a
m
e
pr
oba
bi
li
ty
but
not
s
e
ve
r
it
y.
C
hi
c
k
e
npox
is
us
ua
ll
y
c
ons
id
e
r
e
d
a
s
a
c
hi
ld
hood
m
il
d
di
s
e
a
s
e
[
30]
,
w
he
r
e
a
s
s
a
r
c
oi
dos
is
is
a
c
hr
oni
c
di
s
e
a
s
e
th
a
t
is
of
te
n
a
s
ym
pt
o
m
a
ti
c
a
t
a
c
e
r
ta
in
le
ve
l
but
r
e
qui
r
e
s
f
ol
lo
w
-
up
a
nd
tr
e
a
tm
e
nt
by
dr
ug
th
e
r
a
py
if
s
ym
pt
om
s
a
ppe
a
r
[
31]
.
F
or
pa
ti
e
nt
s
in
a
n
a
dv
a
nc
e
d s
ta
ge
of
th
e
di
s
e
a
s
e
w
ho
a
r
e
unt
r
e
a
te
d,
th
e
m
or
ta
li
ty
r
a
te
is
a
bout
5%
[
31]
.
T
he
la
s
t
c
a
s
e
in
our
r
a
nki
ng
is
a
nt
hr
a
x
w
it
h
0.8%
,
it
is
a
not
if
ia
bl
e
in
f
e
c
ti
ous
di
s
e
a
s
e
be
c
a
u
s
e
it
is
v
e
r
y
da
nge
r
ous
a
n
d
r
e
qui
r
e
s
a
s
pe
c
if
ic
pr
ot
oc
ol
[
32]
.
S
ta
ti
s
ti
c
s
r
e
por
t
a
m
or
ta
li
ty
r
a
te
of
50%
e
ve
n w
it
h t
r
e
a
tm
e
nt
[
32]
.
T
he
in
it
ia
l
r
e
s
ul
ts
f
r
om
our
m
ode
l
in
di
c
a
te
th
a
t
w
hi
le
th
e
s
y
m
pt
om
s
obs
e
r
ve
d
in
th
e
pa
ti
e
nt
a
r
e
f
a
m
il
ia
r
a
nd c
om
m
on a
c
r
os
s
a
ll
a
ge
gr
oups
, i
t
i
s
c
r
uc
ia
l
to
r
e
m
a
in
vi
gi
la
nt
f
o
r
s
e
r
io
us
pa
th
ol
ogi
e
s
t
ha
t
m
a
y be
c
onc
e
a
le
d be
hi
nd t
he
s
e
s
e
e
m
in
gl
y be
ni
gn s
ym
pt
om
s
. F
or
t
he
pur
pos
e
s
of
our
a
na
ly
s
is
, w
e
f
oc
us
e
d on the
f
ir
s
t
e
ig
ht
pr
e
di
c
te
d
r
e
s
ul
ts
,
w
hi
c
h
pr
ovi
de
d
s
uf
f
ic
ie
nt
da
ta
to
e
va
lu
a
te
th
e
m
ode
l'
s
pe
r
f
or
m
a
nc
e
.
H
ow
e
ve
r
,
pr
a
c
ti
ti
one
r
s
ha
ve
th
e
opt
io
n
to
r
e
vi
e
w
a
ll
r
e
s
ul
ts
in
de
s
c
e
ndi
ng
or
de
r
to
ga
in
a
c
om
pr
e
he
ns
iv
e
unde
r
s
ta
ndi
ng
of
th
e
c
a
s
e
.
O
ur
f
in
di
ngs
r
e
ve
a
l
th
a
t
a
m
ong
th
e
e
ig
ht
pr
e
di
c
t
e
d
r
e
s
ul
ts
,
a
ppr
oxi
m
a
te
ly
f
our
w
e
r
e
f
a
ta
l
a
nd
da
nge
r
ous
di
s
e
a
s
e
s
,
unde
r
s
c
or
in
g
th
e
im
por
ta
nc
e
of
e
a
r
ly
di
a
g
nos
is
a
nd
ur
ge
nt
tr
e
a
tm
e
nt
.
T
hi
s
de
m
ons
tr
a
te
s
th
a
t
our
m
ode
l
is
a
n
e
f
f
e
c
ti
ve
to
ol
f
or
pr
a
c
ti
ti
one
r
s
to
e
nh
a
nc
e
th
e
ir
di
a
gnos
ti
c
pr
oc
e
s
s
a
nd
id
e
nt
if
y
pot
e
nt
ia
l
hi
dde
n
pa
th
ol
ogi
e
s
pr
om
pt
ly
,
th
e
r
e
by
s
a
vi
ng
va
lu
a
bl
e
ti
m
e
.
F
o
r
in
s
ta
nc
e
,
e
a
r
ly
de
te
c
ti
on
of
a
nt
hr
a
x
c
a
n
s
a
ve
m
a
ny
li
ve
s
,
in
c
lu
di
ng
th
e
pa
ti
e
nt
s
.
S
im
il
a
r
ly
,
ti
m
e
ly
id
e
nt
i
f
ic
a
ti
on
of
L
e
gi
onna
ir
e
’
s
di
s
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ur
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r
. F
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th
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ddi
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ona
l
Evaluation Warning : The document was created with Spire.PDF for Python.
I
S
S
N
:
2252
-
8938
I
nt
J
A
r
ti
f
I
nt
e
ll
, V
ol
.
14
, N
o.
3
,
J
une
20
25
:
2072
-
2082
2080
s
ym
pt
om
s
e
le
c
te
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s
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ke
y i
ndi
c
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to
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f
or
t
hi
s
c
ondi
ti
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he
m
os
t
not
a
bl
e
r
e
s
ul
t
w
a
s
f
or
L
e
gi
onna
ir
e
’
s
di
s
e
a
s
e
,
w
hi
c
h
s
how
e
d
a
pr
oba
bi
li
ty
in
c
r
e
a
s
e
to
2.94%
f
r
om
1.546
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in
th
e
f
ir
s
t
it
e
r
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a
r
ly
doubli
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nd
hi
ghl
ig
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s
e
ve
r
it
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ondi
ti
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F
or
th
e
r
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m
a
in
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g
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s
ul
ts
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nki
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te
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di
s
e
a
s
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a
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or
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s
how
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m
in
im
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l
c
ha
nge
,
a
s
de
pi
c
te
d
in
T
a
bl
e
4.
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hi
s
c
ons
is
t
e
nc
y
s
ugge
s
ts
th
e
m
ode
l'
s
s
ta
bi
li
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r
e
li
a
bi
li
ty
in
id
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yi
ng
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nki
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di
s
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s
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ba
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ir
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,
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ve
n
w
it
h
pa
r
a
m
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te
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dj
us
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e
nt
s
.
T
a
bl
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4. M
e
a
s
ur
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m
e
nt
r
e
s
ul
ts
M
e
t
r
i
c
S
c
or
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(
%
)
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c
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o a
vg (
%
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W
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ght
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%
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A
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ur
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97.72
P
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s
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96
97
R
e
c
a
l
l
96
98
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-
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or
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96
97
I
n
th
is
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tu
dy,
our
m
ode
l
de
m
ons
tr
a
te
d
e
xc
e
pt
io
na
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pe
r
f
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ta
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s
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vi
de
nc
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d
by
th
e
r
e
s
ul
ts
in
T
a
bl
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4
.
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he
m
ode
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a
c
hi
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c
c
ur
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c
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of
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ppr
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te
ly
97.72%
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di
c
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ti
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gh
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of
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t
pr
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di
c
ti
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a
c
r
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a
ll
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l
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s
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s
.
H
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ve
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it
is
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por
ta
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to
not
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t
a
c
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ur
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di
f
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nt
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be
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e
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ls
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pos
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a
ls
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n
e
ga
ti
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e
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,
w
hi
c
h
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a
c
r
it
ic
a
l
c
on
s
id
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r
a
ti
on
in
m
e
di
c
a
l
di
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gnos
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yi
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e
r
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h
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1
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or
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m
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tr
ic
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w
hi
c
h
ba
la
nc
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c
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d
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hi
s
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gh
s
c
or
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c
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ir
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s
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t
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ode
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m
a
in
ta
in
s
a
hi
gh
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ve
l
of
pr
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c
is
io
n
in
pos
it
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pr
e
di
c
ti
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a
l
s
o
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f
f
e
c
t
iv
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ly
c
a
pt
ur
e
s
a
ll
r
e
le
v
a
nt
pos
it
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in
s
ta
nc
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s
.
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uc
h
a
ba
l
a
nc
e
is
c
r
uc
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l
in
m
e
di
c
a
l
di
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gnos
ti
c
s
,
e
n
s
ur
in
g
th
a
t
th
e
m
ode
l
r
e
li
a
bl
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id
e
nt
if
ie
s
tr
ue
po
s
it
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e
s
w
hi
le
m
in
im
iz
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g
f
a
ls
e
ne
ga
ti
ve
s
.
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ve
r
a
ll
,
th
e
F
1
s
c
or
e
r
e
s
ul
ts
unde
r
s
c
or
e
th
e
m
ode
l'
s
r
obus
tn
e
s
s
a
nd
r
e
li
a
bi
li
ty
,
m
a
ki
ng
it
a
va
lu
a
bl
e
to
ol
f
or
m
e
di
c
a
l
pr
a
c
ti
ti
one
r
s
w
ho
r
e
qui
r
e
a
c
c
ur
a
te
a
nd
c
om
pr
e
he
ns
iv
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di
a
gnos
ti
c
s
uppor
t.
A
s
a
c
on
c
lu
s
io
n
to
th
is
s
e
c
ti
on
of
our
pa
pe
r
,
w
e
c
a
n s
e
e
th
a
t
our
m
ode
l
w
a
s
ve
r
y
u
s
e
f
ul
in
a
c
hi
e
vi
ng
th
e
m
a
in
obj
e
c
ti
ve
e
xpr
e
s
s
e
d
in
th
e
pr
e
vi
ous
s
e
c
ti
on.
I
t
w
a
s
a
bl
e
to
pr
e
di
c
t
pos
s
ib
le
di
s
e
a
s
e
s
ba
s
e
d
on t
he
gi
ve
n s
ym
pt
om
s
w
hi
le
a
c
hi
e
vi
ng ve
r
y good pe
r
f
or
m
a
nc
e
.
5.
C
O
N
C
L
U
S
I
O
N
T
hi
s
s
tu
dy
e
nha
nc
e
s
m
e
di
c
a
l
di
a
gnos
ti
c
s
by
im
pl
e
m
e
nt
in
g
a
m
a
c
hi
ne
le
a
r
ni
ng
m
ode
l
to
pr
e
di
c
t
di
s
e
a
s
e
s
ba
s
e
d on obs
e
r
ve
d s
ym
pt
om
s
, us
in
g a
da
ta
s
e
t
li
nki
ng dis
e
a
s
e
s
t
o s
ym
pt
om
s
a
nd e
m
pl
oyi
ng t
he
n
a
iv
e
B
a
ye
s
c
l
a
s
s
if
ic
a
ti
on
a
lg
or
it
hm
.
T
he
it
e
r
a
ti
ve
a
ppr
oa
c
h
in
vol
v
e
s
f
e
e
di
ng
th
e
m
ode
l
w
it
h
ne
w
or
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ddi
ti
ona
l
s
ym
pt
om
s
unt
il
a
n
opt
im
a
l
di
a
gnos
is
is
a
c
hi
e
ve
d.
T
he
m
ode
l
h
a
s
pr
ove
n
e
f
f
e
c
ti
ve
,
e
s
ta
bl
is
hi
ng
tr
us
t
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1]
A
.
P
r
oba
nda
r
i
,
L
.
L
i
ndhol
m
,
H
.
S
t
e
nl
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.
U
t
a
r
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ni
,
a
nd
A
.
K
.
H
ur
t
i
g,
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uni
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s
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or
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r
e
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s
t
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gy
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n
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ndone
s
i
a
:
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c
r
os
s
-
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t
i
ona
l
s
t
udy,”
B
M
C
H
e
al
t
h
Se
r
v
i
c
e
s
R
e
s
e
ar
c
h
,
vol
.
10,
M
a
y.
2010,
doi
:
10.1186/
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2]
M
.
P
uppa
l
a
e
t
al
.
,
“
M
E
T
E
O
R
:
A
n
e
nt
e
r
pr
i
s
e
he
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l
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h
i
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or
m
a
t
i
c
s
e
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r
onm
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nt
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o
s
uppor
t
e
vi
de
nc
e
-
ba
s
e
d
m
e
di
c
i
ne
,”
I
E
E
E
T
r
ans
ac
t
i
ons
on B
i
om
e
di
c
al
E
ngi
ne
e
r
i
ng
, vol
. 62, no. 12, pp. 2776
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:
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T
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E
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3]
M
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N
.
A
l
ol
a
yy
a
n,
M
.
S
.
A
l
ya
hya
,
A
.
H
.
A
l
a
l
a
w
i
n,
A
.
S
houka
t
,
a
nd
F
.
T
.
N
us
a
i
r
a
t
,
“
H
e
a
l
t
h
i
nf
or
m
a
t
i
on
t
e
c
hnol
ogy
a
nd
hos
pi
t
a
l
pe
r
f
or
m
a
nc
e
t
he
r
o
l
e
of
he
a
l
t
h
i
nf
o
r
m
a
t
i
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qua
l
i
t
y
i
n
t
e
a
c
hi
ng
hos
pi
t
a
l
s
,”
H
e
l
i
y
on
,
vol
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no.
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:
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j
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l
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yon.2020.e
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H
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M
.
K
r
um
hol
z
,
“
B
i
g
da
t
a
a
nd
ne
w
know
l
e
dge
i
n
m
e
di
c
i
ne
:
T
he
t
hi
nki
ng,
t
r
a
i
ni
ng,
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nd
t
ool
s
ne
e
de
d
f
o
r
a
l
e
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r
ni
ng
he
a
l
t
h
s
ys
t
e
m
,”
H
e
al
t
h A
f
f
ai
r
s
, vol
. 33, no. 7, pp. 1163
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hl
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[
5]
G
.
S
.
G
i
ns
bur
g
a
nd
K
.
A
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hi
l
l
i
ps
,
“
P
r
e
c
i
s
i
on
m
e
di
c
i
ne
:
f
r
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s
c
i
e
nc
e
t
o
va
l
ue
,”
H
e
al
t
h
A
f
f
ai
r
s
,
vol
.
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ha
f
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[
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T
.
B
.
M
ur
doc
h
a
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A
.
S
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D
e
t
s
ky,
“
T
he
i
ne
vi
t
a
bl
e
a
ppl
i
c
a
t
i
on
of
bi
g
da
t
a
t
o
he
a
l
t
h
c
a
r
e
,”
J
A
M
A
:
T
he
J
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nal
of
t
he
A
m
e
r
i
c
an
M
e
di
c
al
A
s
s
oc
i
at
i
on
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J
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xt
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r
,
J
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X
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J
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X
u,
X
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Z
hou,
a
nd
K
.
Z
ha
ng,
“
T
he
p
r
a
c
t
i
c
a
l
i
m
pl
e
m
e
nt
a
t
i
on
of
a
r
t
i
f
i
c
i
a
l
i
nt
e
l
l
i
ge
nc
e
t
e
c
hnol
ogi
e
s
i
n
m
e
di
c
i
ne
,”
N
at
ur
e
M
e
di
c
i
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, vol
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F
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J
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a
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e
t
al
.
,
“
A
r
t
i
f
i
c
i
a
l
i
nt
e
l
l
i
ge
nc
e
i
n
he
a
l
t
hc
a
r
e
:
P
a
s
t
,
pr
e
s
e
nt
a
nd
f
ut
ur
e
,”
St
r
ok
e
and
V
as
c
ul
ar
N
e
ur
ol
ogy
,
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[
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N
L
M
,
“
U
M
L
S
m
e
t
a
t
he
s
a
ur
us
br
ow
s
e
r
,”
U
.S.
N
at
i
onal
L
i
br
ar
y
of
M
e
di
c
i
ne
.
2022.
[
O
nl
i
ne
]
.
A
va
i
l
a
bl
e
:
ht
t
ps
:
/
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ut
s
.nl
m
.ni
h.gov/
ut
s
/
um
l
s
[
10]
A
. L
a
a
bi
di
a
nd
M
. A
i
s
s
a
oui
,
“
P
e
r
f
or
m
a
nc
e
a
na
l
ys
i
s
of
m
a
c
hi
ne
l
e
a
r
ni
ng c
l
a
s
s
i
f
i
e
r
s
f
or
pr
e
di
c
t
i
ng di
a
be
t
e
s
a
nd pr
os
t
a
t
e
c
a
nc
e
r
,”
i
n
2020
1s
t
I
nt
e
r
nat
i
onal
C
onf
e
r
e
nc
e
on
I
nnov
at
i
v
e
R
e
s
e
ar
c
h
i
n
A
ppl
i
e
d
Sc
i
e
nc
e
,
E
ngi
ne
e
r
i
ng
and
T
e
c
hnol
ogy
(
I
R
A
SE
T
)
,
2020,
pp. 1
–
6. doi
:
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I
R
A
S
E
T
48871.2020.9092255.
[
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D
.
S
ha
h,
S
.
P
a
t
e
l
,
a
nd
S
.
K
.
B
ha
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t
i
,
“
H
e
a
r
t
di
s
e
a
s
e
pr
e
di
c
t
i
on
us
i
ng
m
a
c
hi
ne
l
e
a
r
ni
ng
t
e
c
hni
que
s
,”
SN
C
om
put
e
r
Sc
i
e
nc
e
,
vol
.
1,
2020, doi
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hi
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I
nt
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r
nat
i
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C
onf
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r
e
nc
e
on
E
m
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gi
ng
T
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e
nd
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n
E
ngi
ne
e
r
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and
T
e
c
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Si
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I
nf
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m
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P
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c
t
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hi
ne
l
e
a
r
ni
ng
ove
r
bi
g
da
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f
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t
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nt
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r
nat
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ni
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ge
s
y
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m
(
U
M
L
S
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l
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a
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ge
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ys
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M
L
S
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t
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a
l
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