Skip to main content

Genetic Algorithms as a Feature Selection Tool in Heart Failure Disease

Alabed, Asmaa; Kambhampati, Chandrasekhar; Gordon, Neil

Authors

Asmaa Alabed



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.

Journal Article Type Conference Paper
Journal Advances in Intelligent Systems and Computing
Print ISSN 2194-5357
Electronic ISSN 2194-5365
Publisher Springer Verlag
Volume 1229 AISC
Pages 531-543
ISBN 9783030522452
APA6 Citation Alabed, A., Kambhampati, C., & Gordon, N. (in press). Genetic Algorithms as a Feature Selection Tool in Heart Failure Disease. Advances in Intelligent Systems and Computing, 1229 AISC, 531-543. https://doi.org/10.1007/978-3-030-52246-9_38
DOI https://doi.org/10.1007/978-3-030-52246-9_38
Keywords Feature selection; decision-making; algorithms; Genetic Algorithm
Publisher URL https://link.springer.com/chapter/10.1007%2F978-3-030-52246-9_38
;