ACO_NB-Based Hybrid Prediction Model for Medical Disease Diagnosis

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Author(s): Amit Kumar (Sanaka Educational Trust's Group of Institutions, India), Manish Kumar (Vellore Institute of Technology, Chennai, India)and Nidhya R. (Madanapalle Institute of Technology & Science, India)
Copyright: 2021
Pages: 11
Source title: Handbook of Research on Disease Prediction Through Data Analytics and Machine Learning
Source Author(s)/Editor(s): Geeta Rani (Manipal University Jaipur, India)and Pradeep Kumar Tiwari (Manipal University Jaipur, India)
DOI: 10.4018/978-1-7998-2742-9.ch026

Keywords: Knowledge Management and Library Science / Machine Learning / Medical Diagnosis and Practice / Medical Information Science Reference

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Abstract

In recent years, a huge increase in the demand of medically related data is reported. Due to this, research in medical disease diagnosis has emerged as one of the most demanding research domains. The research reported in this chapter is based on developing an ACO (ant colony optimization)-based Bayesian hybrid prediction model for medical disease diagnosis. The proposed model is presented in two phases. In the first phase, the authors deal with feature selection by using the application of a nature-inspired algorithm known as ACO. In the second phase, they use the obtained feature subset as input for the naïve Bayes (NB) classifier for enhancing the classification performances over medical domain data sets. They have considered 12 datasets from different organizations for experimental purpose. The experimental analysis advocates the superiority of the presented model in dealing with medical data for disease prediction and diagnosis.

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ACO_NB-Based Hybrid Prediction Model for Medical Disease Diagnosis

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