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28 August 2023 An efficient hybrid XGBLR-IMBODE model for heart disease prediction
Weijun Gao, Pengfei Fu, Zhenyu Wang
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Proceedings Volume 12724, Second International Conference on Biomedical and Intelligent Systems (IC-BIS 2023); 1272414 (2023) https://doi.org/10.1117/12.2687815
Event: Second International Conference on Biomedical and Intelligent Systems (IC-BIS2023), 2023, Xiamen, China
Abstract
Heart disease is one of the world’s deadliest health problems. An efficient and accurate approach to early prevention and detection of heart disease is becoming critical. Machine learning (ML) technology has demonstrated the capability and effectiveness to assist decision making in many fields. ML techniques have also been applied in heart disease prediction, but a single ML model usually cannot achieve good performance. Therefore, to improve the performance of the prediction model, a hybrid XGBoost and logistic regression (XGBLR) using improved hybridization of differential evolution and monarch butterfly optimization (IMBODE) are proposed. XGBLR reduces the complexity of feature engineering and enhances the predictive performance of the model, and IMBODE strengthens the ability to optimize model hyperparameters to achieve better solutions. The evaluation results demonstrate that the XGBLR is advanced in the prediction of heart disease, with an accuracy of 96.72% on the Cleveland dataset.
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Weijun Gao, Pengfei Fu, and Zhenyu Wang "An efficient hybrid XGBLR-IMBODE model for heart disease prediction", Proc. SPIE 12724, Second International Conference on Biomedical and Intelligent Systems (IC-BIS 2023), 1272414 (28 August 2023); https://doi.org/10.1117/12.2687815
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KEYWORDS
Cardiovascular disorders
Heart
Performance modeling
Machine learning
Particle swarm optimization
Engineering
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