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Performance Analysis of Nature-Inspired Algorithms-Based Bayesian Prediction Models for Medical Data Sets

Performance Analysis of Nature-Inspired Algorithms-Based Bayesian Prediction Models for Medical Data Sets

Amit Kumar, Bikash Kanti Sarkar
Copyright: © 2021 |Pages: 22
ISBN13: 9781799880486|ISBN10: 1799880486|EISBN13: 9781799880998
DOI: 10.4018/978-1-7998-8048-6.ch044
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MLA

Kumar, Amit, and Bikash Kanti Sarkar. "Performance Analysis of Nature-Inspired Algorithms-Based Bayesian Prediction Models for Medical Data Sets." Research Anthology on Multi-Industry Uses of Genetic Programming and Algorithms, edited by Information Resources Management Association, IGI Global, 2021, pp. 874-895. https://doi.org/10.4018/978-1-7998-8048-6.ch044

APA

Kumar, A. & Sarkar, B. K. (2021). Performance Analysis of Nature-Inspired Algorithms-Based Bayesian Prediction Models for Medical Data Sets. In I. Management Association (Ed.), Research Anthology on Multi-Industry Uses of Genetic Programming and Algorithms (pp. 874-895). IGI Global. https://doi.org/10.4018/978-1-7998-8048-6.ch044

Chicago

Kumar, Amit, and Bikash Kanti Sarkar. "Performance Analysis of Nature-Inspired Algorithms-Based Bayesian Prediction Models for Medical Data Sets." In Research Anthology on Multi-Industry Uses of Genetic Programming and Algorithms, edited by Information Resources Management Association, 874-895. Hershey, PA: IGI Global, 2021. https://doi.org/10.4018/978-1-7998-8048-6.ch044

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Abstract

Research in medical data prediction has become an important classification problem due to its domain specificity, voluminous, and class imbalanced nature. In this chapter, four well-known nature-inspired algorithms, namely genetic algorithms (GA), genetic programming (GP), particle swarm optimization (PSO), and ant colony optimization (ACO), are used for feature selection in order to enhance the classification performances of medical data using Bayesian classifier. Naïve Bayes is most widely used Bayesian classifier in automatic medical diagnostic tools. In total, 12 real-world medical domain data sets are selected from the University of California, Irvine (UCI repository) for conducting the experiment. The experimental results demonstrate that nature-inspired Bayesian model plays an effective role in undertaking medical data prediction.

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