CSA-DE-LR: enhancing cardiovascular disease diagnosis with a novel hybrid machine learning approach

作     者：Dedeturk, Beyhan Adanur Dedeturk, Bilge Kagan Bakir-Gungor, Burcu 

作者机构：Abdullah Gul Univ Dept Comp Engn Kayseri Turkiye Erciyes Univ Dept Software Engn Kayseri Turkiye 

出 版 物：《PEERJ COMPUTER SCIENCE》 (PeerJ Comput. Sci.)

年 卷 期：2024年第10卷

页      面：e2197-e2197页

核心收录：

基　　金：The authors received no funding for this work 

主　　题：Cardiovascular diseases Machine learning Clonal selection algorithm Differential evolution Logistic regression Medical diagnostics 

摘      要：Cardiovascular diseases (CVD) are a leading cause of mortality globally, necessitating the development of efficient diagnostic tools. Machine learning (ML) and metaheuristic algorithms have become prevalent in addressing these challenges, providing promising solutions in medical diagnostics. However, traditional ML approaches often need to be improved in feature selection and optimization, leading to suboptimal performance in complex diagnostic tasks. To overcome these limitations, this study introduces a new hybrid method called CSA-DE-LR, which combines the clonal selection algorithm (CSA) and differential evolution (DE) with logistic regression. This integration is designed to optimize logistic regression weights efficiently for the accurate classification of CVD. The methodology employs three optimization strategies based on the F1 score, the Matthews correlation coefficient (MCC), and the mean absolute error (MAE). Extensive evaluations on benchmark datasets, namely Cleveland and Statlog, reveal that CSA-DELR outperforms state-of-the-art ML methods. In addition, generalization is evaluated using the Breast Cancer Wisconsin Original (WBCO) and Breast Cancer Wisconsin Diagnostic (WBCD) datasets. Significantly, the proposed model demonstrates superior efficacy compared to previous research studies in this domain. This study s findings highlight the potential of hybrid machine learning approaches for improving diagnostic accuracy, offering a significant advancement in the fields of medical data analysis and CVD diagnosis.