An Intelligent Decision Support Ensemble Voting Model for Coronary
Artery Disease Prediction in Smart Healthcare Monitoring Environments
- URL: http://arxiv.org/abs/2210.14906v1
- Date: Tue, 25 Oct 2022 21:09:34 GMT
- Title: An Intelligent Decision Support Ensemble Voting Model for Coronary
Artery Disease Prediction in Smart Healthcare Monitoring Environments
- Authors: Anas Maach, Jamila Elalami, Noureddine Elalami, El Houssine El Mazoudi
- Abstract summary: Coronary artery disease (CAD) is one of the most common cardiac diseases worldwide.
E-diagnosis tool based on machine learning (ML) algorithms can be used in a smart healthcare monitoring system.
- Score: 0.0
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: Coronary artery disease (CAD) is one of the most common cardiac diseases
worldwide and causes disability and economic burden. It is the world's leading
and most serious cause of mortality, with approximately 80% of deaths reported
in low- and middle-income countries. The preferred and most precise diagnostic
tool for CAD is angiography, but it is invasive, expensive, and technically
demanding. However, the research community is increasingly interested in the
computer-aided diagnosis of CAD via the utilization of machine learning (ML)
methods. The purpose of this work is to present an e-diagnosis tool based on ML
algorithms that can be used in a smart healthcare monitoring system. We applied
the most accurate machine learning methods that have shown superior results in
the literature to different medical datasets such as RandomForest, XGboost,
MLP, J48, AdaBoost, NaiveBayes, LogitBoost, KNN. Every single classifier can be
efficient on a different dataset. Thus, an ensemble model using majority voting
was designed to take advantage of the well-performed single classifiers,
Ensemble learning aims to combine the forecasts of multiple individual
classifiers to achieve higher performance than individual classifiers in terms
of precision, specificity, sensitivity, and accuracy; furthermore, we have
benchmarked our proposed model with the most efficient and well-known ensemble
models, such as Bagging, Stacking methods based on the cross-validation
technique, The experimental results confirm that the ensemble majority voting
approach based on the top 3 classifiers: MultilayerPerceptron, RandomForest,
and AdaBoost, achieves the highest accuracy of 88,12% and outperforms all other
classifiers. This study demonstrates that the majority voting ensemble approach
proposed above is the most accurate machine learning classification approach
for the prediction and detection of coronary artery disease.
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