HDPF: Heart Disease Prediction Framework Based on Hybrid Classifiers and Genetic Algorithm

Supervised machine learning algorithms are powerful classification techniques commonly used to build prediction models that help diagnose the disease early. However, some challenges like overfitting and underfitting need to be overcome while building the model. This paper introduces hybrid classifie...

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Bibliographic Details
Published in:IEEE access 2021, Vol.9, p.146797-146809
Main Authors: Ashri, Sarria E. A., El-Gayar, M. M., El-Daydamony, Eman M.
Format: Article
Language:English
Subjects:
Cardiovascular disease
Classification
Classifiers
Data models
Diseases
ensembled model
Genetic algorithms
Heart
Heart diseases
Machine learning
Machine learning algorithms
majority voting technique
Prediction models
Predictive models
simple genetic algorithm
supervised machine learning algorithms
Support vector machines
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Summary:Supervised machine learning algorithms are powerful classification techniques commonly used to build prediction models that help diagnose the disease early. However, some challenges like overfitting and underfitting need to be overcome while building the model. This paper introduces hybrid classifiers using the ensembled model with a majority voting technique to improve prediction accuracy. Furthermore, a proposed preprocessing technique and features selection based on a genetic algorithm is suggested to enhance prediction performance and overall time consumption. In addition, the 10-folds cross-validation technique is used to overcome the overfitting problem. Experiments were performed on a dataset for cardiovascular patients from the UCI Machine Learning Repository. Through a comparative analytical approach, the study results indicated that the proposed ensemble classifier model achieved a classification accuracy of 98.18% higher than the rest of the relevant developments in the study.
ISSN:2169-3536
2169-3536
DOI:10.1109/ACCESS.2021.3122789