Genetic Algorithms as a Feature Selection Tool in Heart Failure Disease
Alabed, Asmaa; Kambhampati, Chandrasekhar; Gordon, Neil
Dr Chandrasekhar Kambhampati C.Kambhampati@hull.ac.uk
Reader in Computer Science
Dr Neil Gordon N.A.Gordon@hull.ac.uk
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|
|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|
|Keywords||Feature selection; decision-making; algorithms; Genetic Algorithm|
This file is under embargo until Jul 5, 2021 due to copyright reasons.
Contact N.A.Gordon@hull.ac.uk to request a copy for personal use.
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