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

ABSTRACT: 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 employ

SUBMITTER: Adanur Dedeturk B 

PROVIDER: S-EPMC11639265 | biostudies-literature | 2024

REPOSITORIES: biostudies-literature