Abstract
Early identification of Cardiovascular issues is crucial due to the high mortality rate associated with heart disease. This research assesses machine learning algorithms for heart disease prediction using tabular datasets. Previous researchers suggested Tree-based models like Random Forest, XGBoost, and Decision Trees and various hyper-parameter optimization methods including Grid Search, Random Search, Swarm and Evolutionary Algorithms which excel in accuracy and robustness. However, they are computationally inefficient and less effective in dynamic settings. A novel Hybrid Swarm Evolution Optimization (HySEOpt) is introduced, which adjusts mutation rates based on performance curves and utilizes parallel processing for faster optimization achieving 98.01% accuracy. HySEOpt enhances model’s quality and robustness, addressing limitations of existing methods and contributes to hyper-parameter optimization in predictive healthcare modeling.
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Data Availibility Statement
The datasets used in this research is openly accessible for academic use on Kaggle [30].
Materials Availability
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V.P. conceived the research idea, designed experiments, implemented the various hyper-parameter optimization algorithms, performed data analysis, interpreted results, wrote the manuscript, and created visualizations. B. S. contributed to investigation, provided resources, participated in writing, reviewing, and editing the manuscript, and contributed to data analysis and interpretation of results. A.B. curated and prepared datasets for training and evaluation, and participated in writing, reviewing, and editing the manuscript. A.M. supervised the project, provided assistance in research, reviewed and edited the manuscript, and administered the project.
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Parikh, V., Sharma, B., Byotra, A. et al. Optimizing Heart Disease Prediction Using a Hybrid Dynamic Swarm Evolution Approach. SN COMPUT. SCI. 5, 1104 (2024). https://doi.org/10.1007/s42979-024-03484-y
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DOI: https://doi.org/10.1007/s42979-024-03484-y