A Hybrid Predictive Model Integrating C4.5 and Decision Table Classifiers for Medical Data Sets

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Author(s): Amit Kumar (Computer Science and Engineering, Birla Institute of Technology, Ranchi, India)and Bikash Kanti Sarkar (Birla Institute of Technology, Ranchi, India)
Copyright: 2021
Pages: 19
Source title: Research Anthology on Decision Support Systems and Decision Management in Healthcare, Business, and Engineering
Source Author(s)/Editor(s): Information Resources Management Association (USA)
DOI: 10.4018/978-1-7998-9023-2.ch016

Keywords: Business & Management / Decision Support Systems / Information Science Reference

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Abstract

This article describes how, recently, data mining has been in great use for extracting meaningful patterns from medical domain data sets, and these patterns are then applied for clinical diagnosis. Truly, any accurate, precise and reliable classification models significantly assist the medical practitioners to improve diagnosis, prognosis and treatment processes of individual diseases. However, numerous intelligent models have been proposed in this respect but still they have several drawbacks like, disease specificity, class imbalance, conflicting and lack adequacy for dimensionality of patient's data. The present study has attempted to design a hybrid prediction model for medical domain data sets by combining the decision tree based classifier (mainly C4.5) and the decision table based classifier (DT). The experimental results validate in favour of the claims.

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A Hybrid Predictive Model Integrating C4.5 and Decision Table Classifiers for Medical Data Sets

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