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

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

Amit Kumar, Bikash Kanti Sarkar
Copyright: © 2018 |Volume: 11 |Issue: 2 |Pages: 18
ISSN: 1938-7857|EISSN: 1938-7865|EISBN13: 9781522543213|DOI: 10.4018/JITR.2018040109
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MLA

Kumar, Amit, and Bikash Kanti Sarkar. "A Hybrid Predictive Model Integrating C4.5 and Decision Table Classifiers for Medical Data Sets." JITR vol.11, no.2 2018: pp.150-167. http://doi.org/10.4018/JITR.2018040109

APA

Kumar, A. & Sarkar, B. K. (2018). A Hybrid Predictive Model Integrating C4.5 and Decision Table Classifiers for Medical Data Sets. Journal of Information Technology Research (JITR), 11(2), 150-167. http://doi.org/10.4018/JITR.2018040109

Chicago

Kumar, Amit, and Bikash Kanti Sarkar. "A Hybrid Predictive Model Integrating C4.5 and Decision Table Classifiers for Medical Data Sets," Journal of Information Technology Research (JITR) 11, no.2: 150-167. http://doi.org/10.4018/JITR.2018040109

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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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