One
way
to
diagnose
PCOS,
a
hormonal
disorder
that
impacts
female
pregnancy,
is
ultrasound
imaging..
To
overcome
the
manual
difficulties
in
identifying
disorders
by
physicians
an
automated
deep
learning
approach
suggested
this
paper.
The
bulk
of
imaging
traits
are
used
determine
illness's
diagnosis.
Due
overlapping
follicles,
intrinsic
equipment
noise,
and
shortage
operator
knowledge,
it
primarily
based
on
expertise
execution,
typical
appearance
PCOS
image
becomes
more
challenging,
lengthening
diagnosis
process.
This
study
suggests
for
prediction
makes
use
transfer
tools
including
Alexnet,
VGG16,
Inception
V3,
hybrid
models.
classification
was
developed
using
proposed
approach.
Here,
effort
made
propose
process
would
train
model
improve
accuracy
Applying
performance
metrics
such
as
accuracy,
precision,
Recal,
F1score
each
network's
evaluated.
detection
method
produces
87%.
This
research
delves
into
the
factors
and
considerations
influencing
public’s
adoption
of
interactive
intelligent
health
promotion
equipment.
Utilizing
DEMATEL
method,
study
investigates
interrelations
significance
various
decision
criteria,
laying
groundwork
for
future
initiatives
promoting
such
The
objective
is
to
bolster
interest
in
recreational
sports,
enhance
physical
well-being,
alleviate
healthcare
burdens.
identifies
eight
primary
factors:
economic
traits,
personality
communication
behavior,
relative
advantage,
compatibility,
complexity,
observability,
social
support,
peer
relationships.
Through
comprehensive
analysis,
both
direct
indirect
impacts
these
are
examined,
along
with
computation
key
indicators.
Findings
suggest
that,
among
individuals
exercise
habits,
traits
perceived
benefits
product
pivotal
determining
their
Within
comfort
emerges
as
most
influential
criterion,
tending
influence
other
factors.
Similarly,
advantage
category,
personalization
stands
out
sub-criterion
a
ripple
effect
on
aspects.
Further
ANP
analysis
findings
highlights
characteristics
weighted
followed
by
traits.
Computational and Structural Biotechnology Journal,
Год журнала:
2025,
Номер
27, С. 1578 - 1599
Опубликована: Янв. 1, 2025
Recent
research
on
Polycystic
Ovary
Syndrome
(PCOS)
detection
increasingly
employs
intelligent
algorithms
to
assist
gynecologists
in
more
accurate
and
efficient
diagnoses.
However,
PCOS
faces
notable
challenges:
absence
of
standardized
feature
taxonomies,
limited
available
datasets,
insufficient
understanding
existing
tools'
capabilities.
This
paper
addresses
these
gaps
by
introducing
a
novel
analytical
framework
for
diagnostic
developing
comprehensive
taxonomy
comprising
108
features
across
8
categories.
Furthermore,
we
analyzed
datasets
assessed
current
tools.
Our
findings
reveal
that
12
publicly
accessible
cover
only
54%
the
identified
our
taxonomy.
These
frequently
lack
multimodal
integration,
regular
updates,
clear
license
information-constraints
potentially
limit
tool
development.
Additionally,
analysis
42
tools
identifies
several
limitations:
high
computational
resource
requirements,
inadequate
data
processing,
longitudinal
capabilities,
clinical
validation.
Based
observations,
highlight
critical
challenges
future
directions
advancing
Advances in medical technologies and clinical practice book series,
Год журнала:
2024,
Номер
unknown, С. 192 - 216
Опубликована: Фев. 23, 2024
The
application
of
AI
in
geriatric
healthcare
has
become
a
revolutionary
and
essential
solution
to
tackling
the
problems
faced
by
older
people.
An
important
step
toward
providing
patient-centered
cost-effective
treatment,
integration
will
ultimately
enhance
standard
life
for
This
chapter
is
systematic
review
rapidly
developing
field
applications
healthcare.
It
provides
thorough
analysis
impact
potential
technologies
addressing
needs
aging
population.
was
conducted
using
PRISMA
framework.
Thirty-three
articles
were
considered
final
from
which
five
themes
deduced.
study
facilitate
development
relevant
inclusive
solutions
individuals
hasten
possibility
greater
wellbeing
inclusion
adults
technological
innovativeness
facilities.
International Journal of ADVANCED AND APPLIED SCIENCES,
Год журнала:
2024,
Номер
11(12), С. 225 - 231
Опубликована: Дек. 1, 2024
Diabetes
mellitus,
a
global
health
concern,
includes
type
1
diabetes,
with
an
uncontrollable
risk,
and
2
where
risk
can
be
managed
through
lifestyle
modifications.
This
study
examines
the
impact
of
modifiable
factors—diet,
physical
activity,
body
mass
index
(BMI)—on
diabetes
development.
Using
fuzzy
logic,
binary
variables
from
healthcare
dataset
were
transformed
into
format,
generating
three
output
classes:
"no
risk,"
"possible
"diabetes
present."
The
intermediate
class,
serves
as
alert
for
adopting
healthier
lifestyles
to
mitigate
risk.
Machine
learning
was
applied
both
original
fuzzy-transformed
datasets.
While
provided
outputs
moderate
accuracy
higher
computation
times,
yielded
more
nuanced
predictions,
reduced
time,
improved
classifier
performance.
approach
enhances
assessment
supports
proactive
interventions.
