XAI-Augmented Voting Ensemble Models for Heart Disease Prediction: A SHAP and LIME-Based Approach
Bioengineering,
Год журнала:
2024,
Номер
11(10), С. 1016 - 1016
Опубликована: Окт. 12, 2024
Ensemble
Learning
(EL)
has
been
used
for
almost
ten
years
to
classify
heart
diseases,
but
it
is
still
difficult
grasp
how
the
“black
boxes”,
or
non-interpretable
models,
behave
inside.
Predicting
disease
crucial
healthcare,
since
allows
prompt
diagnosis
and
treatment
of
patient’s
true
state.
Nonetheless,
forecast
illness
with
any
degree
accuracy.
In
this
study,
we
have
suggested
a
framework
prediction
based
on
Explainable
artificial
intelligence
(XAI)-based
hybrid
such
as
LightBoost
XGBoost
algorithms.
The
main
goals
are
build
predictive
models
apply
SHAP
(SHapley
Additive
expPlanations)
LIME
(Local
Interpretable
Model-agnostic
Explanations)
analysis
improve
interpretability
models.
We
carefully
construct
our
systems
test
different
ensemble
learning
algorithms
determine
which
model
best
(HDP).
approach
promotes
transparency
when
examining
these
widespread
health
issues.
By
combining
XAI,
important
factors
risk
signals
that
underpin
co-occurrence
made
visible.
accuracy,
precision,
recall
were
evaluate
their
efficacy.
This
study
highlights
healthcare
be
transparent
recommends
inclusion
XAI
medical
decisionmaking.
Язык: Английский
Predicting major adverse cardiac events in diabetes and chronic kidney disease: a machine learning study from the Silesia Diabetes-Heart Project
Cardiovascular Diabetology,
Год журнала:
2025,
Номер
24(1)
Опубликована: Фев. 15, 2025
Язык: Английский
Novel framework of significant risk factor identification and cardiovascular disease prediction
Expert Systems with Applications,
Год журнала:
2024,
Номер
263, С. 125678 - 125678
Опубликована: Ноя. 12, 2024
Язык: Английский
Recent trends in diabetes mellitus diagnosis: an in-depth review of artificial intelligence-based techniques
Diabetes Research and Clinical Practice,
Год журнала:
2025,
Номер
unknown, С. 112221 - 112221
Опубликована: Май 1, 2025
Язык: Английский
Machine learning modelling and explainability of coronary heart disease based on Mediterranean diet
Mediterranean Journal of Nutrition and Metabolism,
Год журнала:
2025,
Номер
unknown
Опубликована: Май 22, 2025
Background
Coronary
heart
disease
(CHD)
occurs
due
to
the
narrowing
or
blockage
of
coronary
arteries
caused
by
atherosclerosis.
It
is
one
leading
factors
widespread
mortality
and
morbidity.
The
latest
research
highlighted
importance
Mediterranean
diet
(MD)
as
an
excellent
cardioprotective
nutritional
regimen
because
its
abundant
content
monounsaturated
fats,
antioxidant-rich
compounds,
anti-inflammatory
nutrients.
Conventional
CHD
risk
models
frequently
overlook
food
habits,
highlighting
need
for
sophisticated
predictive
modeling
that
includes
lifestyle
aspects.
Objectives
We
aim
use
machine
learning
(ML)
prediction
combining
adherence
MD
with
clinical
characteristics.
Method
For
present
study,
we
employed
Logistic
Regression
(LR),
Random
Forest
(RF),
Support
Vector
Machine
(SVM),
Decision
Tree
(DT),
Adaptive
Boosting
(AdaBoost),
Multilayer
Perceptron
(MLP)
Classifier,
Gaussian
Naive
Bayes
(GNB)
on
dataset
overall
diversity
cumulative
preventive
effects
against
CHD.
was
published
26
April
2021
Mendeley.
Result
results,
shown
in
this
indicate
RF
performed
excellently
0.90,
0.95,
0.90
accuracy,
precision,
recall,
F-1
score
values,
respectively.
Shapley
additive
explanations
(SHAP)
Local
Interpretable
Model-agnostic
Explanations
(LIME)
showed
Glucose,
high-density
lipoprotein
cholesterol
(HDL-C),
bread,
chocolate
have
a
high
impact
prediction.
Conclusion
ML
great
potential
has
Язык: Английский