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Article Details

Copyright Statement: This is an open access article licensed under a Creative Commons Attribution 4.0 International License, which permits unrestricted use, distribution, and reproduction in any medium, even commercially as long as the original work is properly cited.

Hybrid Feature Selection Algorithm and Ensemble Stacking for Heart Disease Prediction

Author 1: Nureen Afiqah Mohd Zaini
Author 2: Mohd Khalid Awang

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Digital Object Identifier (DOI) : 10.14569/IJACSA.2023.0140220

Article Published in International Journal of Advanced Computer Science and Applications(IJACSA), Volume 14 Issue 2, 2023.

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Abstract: In cardiology, as in other medical specialties, early and accurate diagnosis of heart disease is crucial as it has been the leading cause of death over the past few decades. Early prediction of heart disease is now more crucial than ever. However, the state-of-the-art heart disease prediction strategy put more emphasis on classifier selection in enhancing the accuracy and performance of heart disease prediction, and seldom considers feature reduction techniques. Furthermore, there are several factors that lead to heart disease, and it is critical to identify the most significant characteristics in order to achieve the best prediction accuracy and increase prediction performance. Feature reduction reduces the dimensionality of the information, which may allow learning algorithms to work quicker and more efficiently, producing predictive models with the best rate of accuracy. In this study, we explored and suggested a hybrid of two distinct feature reduction techniques, chi-squared and analysis of variance (ANOVA). In addition, using the ensemble stacking method, classification is performed on selected features to classify the data. Using the optimal features based on hybrid features combination, the performance of a stacking ensemble based on logistic regression yields the best result with 93.44%. This can be summarized as the feature selection method can take into account as an effective method for the prediction of heart disease.

Keywords: Heart disease prediction; feature selection; stacking; accuracy

Nureen Afiqah Mohd Zaini and Mohd Khalid Awang, “Hybrid Feature Selection Algorithm and Ensemble Stacking for Heart Disease Prediction” International Journal of Advanced Computer Science and Applications(IJACSA), 14(2), 2023. http://dx.doi.org/10.14569/IJACSA.2023.0140220

@article{Zaini2023,
title = {Hybrid Feature Selection Algorithm and Ensemble Stacking for Heart Disease Prediction},
journal = {International Journal of Advanced Computer Science and Applications},
doi = {10.14569/IJACSA.2023.0140220},
url = {http://dx.doi.org/10.14569/IJACSA.2023.0140220},
year = {2023},
publisher = {The Science and Information Organization},
volume = {14},
number = {2},
author = {Nureen Afiqah Mohd Zaini and Mohd Khalid Awang}
}


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