Transforming Cardiovascular Risk Prediction: A Review of Machine Learning and Artificial Intelligence Innovations
Life,
Год журнала:
2025,
Номер
15(1), С. 94 - 94
Опубликована: Янв. 14, 2025
Cardiovascular
diseases
(CVDs)
remain
a
leading
cause
of
global
mortality
and
morbidity.
Traditional
risk
prediction
models,
while
foundational,
often
fail
to
capture
the
multifaceted
nature
factors
or
leverage
expanding
pool
healthcare
data.
Machine
learning
(ML)
artificial
intelligence
(AI)
approaches
represent
paradigm
shift
in
prediction,
offering
dynamic,
scalable
solutions
that
integrate
diverse
data
types.
This
review
examines
advancements
AI/ML
for
CVD
analyzing
their
strengths,
limitations,
challenges
associated
with
clinical
integration.
Recommendations
standardization,
validation,
future
research
directions
are
provided
unlock
potential
these
technologies
transforming
precision
cardiovascular
medicine.
Язык: Английский
Exploration and comparison of the effectiveness of swarm intelligence algorithm in early identification of cardiovascular disease
Scientific Reports,
Год журнала:
2025,
Номер
15(1)
Опубликована: Фев. 7, 2025
Due
to
the
aging
of
global
population
and
lifestyle
changes,
cardiovascular
disease
has
become
leading
cause
death
worldwide,
causing
serious
public
health
problems
economic
pressures.
Early
accurate
prediction
is
crucial
reducing
morbidity
mortality,
but
traditional
methods
often
lack
robustness.
This
study
focuses
on
integrating
swarm
intelligence
feature
selection
algorithms
(including
whale
optimization
algorithm,
cuckoo
search
flower
pollination
Harris
hawk
particle
genetic
algorithm)
with
machine
learning
technology
improve
early
diagnosis
disease.
systematically
evaluated
performance
each
algorithm
under
different
sizes,
specifically
by
comparing
their
average
running
time
objective
function
values
identify
optimal
subset.
Subsequently,
selected
subsets
were
integrated
into
ten
classification
models,
a
comprehensive
weighted
evaluation
was
performed
based
accuracy,
precision,
recall,
F1
score,
AUC
value
model
determine
configuration.
The
results
showed
that
random
forest,
extreme
gradient
boosting,
adaptive
boosting
k-nearest
neighbor
models
best
combined
dataset
(weighted
score
1),
where
set
consisted
9
key
features
when
size
25;
while
Framingham
dataset,
0.92),
its
derived
from
10
50.
this
show
can
effectively
screen
informative
sets,
significantly
provide
strong
support
for
diseases.
Язык: Английский
Machine Learning–Based Prediction for Incident Hypertension Based on Regular Health Checkup Data: Derivation and Validation in 2 Independent Nationwide Cohorts in South Korea and Japan
Journal of Medical Internet Research,
Год журнала:
2024,
Номер
26, С. e52794 - e52794
Опубликована: Ноя. 5, 2024
Background
Worldwide,
cardiovascular
diseases
are
the
primary
cause
of
death,
with
hypertension
as
a
key
contributor.
In
2019,
led
to
17.9
million
deaths,
predicted
reach
23
by
2030.
Objective
This
study
presents
new
method
predict
using
demographic
data,
6
machine
learning
models
for
enhanced
reliability
and
applicability.
The
goal
is
harness
artificial
intelligence
early
accurate
diagnosis
across
diverse
populations.
Methods
Data
from
2
national
cohort
studies,
National
Health
Insurance
Service-National
Sample
Cohort
(South
Korea,
n=244,814),
conducted
between
2002
2013
were
used
train
test
designed
anticipate
incident
within
5
years
health
checkup
involving
those
aged
≥20
years,
Japanese
Medical
Center
(Japan,
n=1,296,649)
extra
validation.
An
ensemble
was
identify
most
salient
features
contributing
presenting
feature
importance
analysis
confirm
contribution
each
future.
Results
Adaptive
Boosting
logistic
regression
showed
superior
balanced
accuracy
(0.812,
sensitivity
0.806,
specificity
0.818,
area
under
receiver
operating
characteristic
curve
0.901).
indicators
age,
diastolic
blood
pressure,
BMI,
systolic
fasting
glucose.
dataset
(extra
validation
set)
corroborated
these
findings
(balanced
0.741
0.824).
model
integrated
into
public
web
portal
predicting
onset
based
on
data.
Conclusions
Comparative
evaluation
our
against
classical
statistical
distinct
studies
emphasized
former’s
stability,
generalizability,
reproducibility
in
onset.
Язык: Английский