Early detection for elderly people with musculoskeletal aging related diseases based on artificial intelligence model
Abstract
Late-diagnosis
is
one
of
the
main
bottlenecks
in
musculoskeletal
aging-related
diseases
prevention,
and
it
urgent
to
build
early
detection
model.
Twenty-two
features
were
included
models
based
on
binary
multiple
classification
respectively
by
XGBoost.
In
testing,
accuracy
rate
(63.74%~92.40%)
AUC
(0.74
~
0.96)
binary-classification
higher
than
(61.40%
~85.96%)
(0.63
0.86)
multiple-classification
models.
The
optimal
model
had
an
87.13%
0.92
including
cooking,
drinking
milk,
electronic
devices
use
time,
dental
implant,
decay,
professional
oral
cleaning,
falls
past
year,
life
satisfaction,
degree
pain
or
discomfort,
indoor
air
improvement,
drinking,
body
mass
index,
time
spent
indoors,
grip
grouping,
SARC-F
calf
girth
grouping
bone
density
examination.
elderly,
can
be
detected
epidemiological
factors.
Research Square (Research Square), Journal Year: 2025, Volume and Issue: unknown
Published: April 16, 2025
Language: Английский