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