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Unsupervised Feature Selection Approaches for Medical Dataset Using Soft Computing Techniques

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Evolution in Computational Intelligence

Part of the book series: Smart Innovation, Systems and Technologies ((SIST,volume 267))

Abstract

An electrocardiogram (ECG) measures the heart's electrical function and has been widely used to detect heart disorders because of their simplicity and non-obtrusive nature. Nowadays, in medical decision support systems, soft computing methods are commonly used. Medical diagnosis helps to acquire various characteristics reflecting the disease's various variations. It is likely to have important, irrelevant, and redundant features to reflect disease, with the aid of various diagnostic procedures. It is a non-trivial job to define a good feature subset for efficient classification. The objective of this research is to assess three different soft computing-based unsupervised feature reduction approaches namely Soft Set-Based Unsupervised Feature Reduction, Unsupervised Quick Reduction, and Unsupervised Relative Reduction, to evaluate the best subset of features with enhanced diagnostic classification accuracy for cardiovascular disease. The output of feature selection algorithms is evaluated with the heart disease dataset from the UCI repository. The experimental findings indicate that the Soft Set-based Unsupervised Feature Redact algorithm achieved good classification accuracy compared to other feature reduction approaches.

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Authors and Affiliations

  1. Department of BCA, Sona College of Arts and Science, Salem, Tamil Nadu, India

    G. Jothi

  2. Department of IT, Sona College of Technology (Autonomous), Salem, Tamil Nadu, India

    J. Akilandeswari, S. David Samuel Azariya & A. Naveenkumar

Authors
  1. G. Jothi
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  2. J. Akilandeswari
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  3. S. David Samuel Azariya
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  4. A. Naveenkumar
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Corresponding author

Correspondence to G. Jothi .

Editor information

Editors and Affiliations

  1. Department of Electronics and Communication Engineering, Shri Ramswaroop Memorial Group of Professional Colleges (SRMGPC), Lucknow, Uttar Pradesh, India

    Vikrant Bhateja

  2. College of Computing, Michigan Technological University, Michigan, MI, USA

    Jinshan Tang

  3. School of Computer Engineering, Kalinga Institute of Industrial Technology (KIIT), Bhubaneswar, India

    Suresh Chandra Satapathy

  4. Faculty of Computer and Information Science, University of Ljubljana, Ljubljana, Slovenia

    Peter Peer

  5. Department of Computer Science and Engineering, National Institute of Technology (NIT) Mizoram, Aizawl, India

    Ranjita Das

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The competent authorities allowed us to use the images/data as provided in the study. We shall be entirely responsible for any dispute in the future.

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© 2022 The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

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Jothi, G., Akilandeswari, J., David Samuel Azariya, S., Naveenkumar, A. (2022). Unsupervised Feature Selection Approaches for Medical Dataset Using Soft Computing Techniques. In: Bhateja, V., Tang, J., Satapathy, S.C., Peer, P., Das, R. (eds) Evolution in Computational Intelligence. Smart Innovation, Systems and Technologies, vol 267. Springer, Singapore. https://doi.org/10.1007/978-981-16-6616-2_10

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