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Genetic Algorithms as a Feature Selection Tool in Heart Failure Disease

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Intelligent Computing (SAI 2020)

Part of the book series: Advances in Intelligent Systems and Computing ((AISC,volume 1229))

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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.

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Author information

Authors and Affiliations

  1. Faculty of Science and Engineering, Computer Science Department, University of Hull, Hull, UK

    Asmaa Alabed

  2. Faculty of Science and Engineering, University of Hull, Hull, UK

    Chandrasekhar Kambhampati & Neil Gordon

Authors
  1. Asmaa Alabed
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  2. Chandrasekhar Kambhampati
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  3. Neil Gordon
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Corresponding author

Correspondence to Asmaa Alabed .

Editor information

Editors and Affiliations

  1. Faculty of Science and Engineering, Saga University, Saga, Japan

    Kohei Arai

  2. The Science and Information (SAI) Organization, Bradford, West Yorkshire, UK

    Supriya Kapoor

  3. The Science and Information (SAI) Organization, Bradford, West Yorkshire, UK

    Rahul Bhatia

Appendix A

Appendix A

Table 7. List of all features considered.
Full size table

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Alabed, A., Kambhampati, C., Gordon, N. (2020). Genetic Algorithms as a Feature Selection Tool in Heart Failure Disease. In: Arai, K., Kapoor, S., Bhatia, R. (eds) Intelligent Computing. SAI 2020. Advances in Intelligent Systems and Computing, vol 1229. Springer, Cham. https://doi.org/10.1007/978-3-030-52246-9_38

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