Genetic Algorithms as a Feature Selection Tool in Heart Failure Disease

Alabed, Asmaa; Kambhampati, Chandrasekhar; Gordon, Neil

Abstract

A great wealth of information is hidden in clinical datasets, which could be analyzed to support decision-making processes or to better diagnose patients. Feature selection is one of the data pre-processing that selects a set of input features by removing unneeded or irrelevant features. Various algorithms have been used in healthcare to solve such problems involving complex medical data. This paper demonstrates how Genetic Algorithms offer a natural way to solve feature selection amongst data sets, where the fittest individual choice of variables is preserved over different generations. In this paper, a Genetic Algorithm is introduced as a feature selection method and shown to be effective in aiding understanding of such data.

Citation

Alabed, A., Kambhampati, C., & Gordon, N. Genetic Algorithms as a Feature Selection Tool in Heart Failure Disease. Presented at Computing 2020, London

• Presentation Conference Type: Conference Paper (published)
• Conference Name: Computing 2020
• Acceptance Date: Feb 14, 2020
• Online Publication Date: Jul 4, 2020
• Deposit Date: Feb 24, 2020
• Publicly Available Date: Jul 5, 2021
• Journal: Advances in Intelligent Systems and Computing
• Print ISSN: 2194-5357
• Electronic ISSN: 2194-5357
• Publisher: Springer Verlag
• Volume: 1229 AISC
• Pages: 531-543
• ISBN: 9783030522452
• DOI: https://doi.org/10.1007/978-3-030-52246-9_38
• Keywords: Feature selection; decision-making; algorithms; Genetic Algorithm
• Public URL: https://hull-repository.worktribe.com/output/3444926
• Publisher URL: https://link.springer.com/chapter/10.1007%2F978-3-030-52246-9_38