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Investigation of machine learning algorithms on heart disease through dominant feature detection and feature selection

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Abstract

Heart diseases are an essential research topic in healthcare institutions around the world. Therefore, using machine learning and optimization algorithms attracts attention as an important method in detecting heart diseases. Additionally, the factors that affect heart disease are a matter of current debate. In this study, an effective DFD method is proposed using optimization techniques for classifying heart diseases and examining the factors affecting the disease. Initially, the study employs classical machine learning and ensemble algorithms for classification. Subsequently, feature selection is performed using BEO, BSPO, GA, and GFO methods, and the importance levels of features are determined utilizing the DFD approach. The results indicate that the ensemble model achieved an accuracy of 86.34% without optimization methods, whereas the proposed DFD method, when applied in conjunction with ensemble models, increased the accuracy to 99.08%. Therefore, it is observed that ensemble models yield the highest results when used in conjunction with optimization algorithms. The outcomes identified using the DFD method, which are clinically significant, are believed to hold great importance in reducing the number of heart patients and enhancing treatment.

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Data availability

The data that support the findings of this study are available from the corresponding author upon reasonable request.

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Acknowledgements

The author gratefully acknowledges the partial support of the Faculties of Engineering at Kırıkkale University.

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  1. Department of Computer Engineering, Faculty of Engineering, Kırıkkale University, 71100, Kırıkkale, Turkey

    Fuat Türk

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Fuat Türk (FT) carried out the most of implementations and simulations for this manuscript. FT provided core concepts and drafted the manuscript.

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Türk, F. Investigation of machine learning algorithms on heart disease through dominant feature detection and feature selection. SIViP 18, 3943–3955 (2024). https://doi.org/10.1007/s11760-024-03060-0

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