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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.<\/jats:p>","DOI":"10.4018\/jitr.2018040109","type":"journal-article","created":{"date-parts":[[2018,3,22]],"date-time":"2018-03-22T10:22:29Z","timestamp":1521714149000},"page":"150-167","source":"Crossref","is-referenced-by-count":11,"title":["A Hybrid Predictive Model Integrating C4.5 and Decision Table Classifiers for Medical Data Sets"],"prefix":"10.4018","volume":"11","author":[{"given":"Amit","family":"Kumar","sequence":"first","affiliation":[{"name":"Computer Science and Engineering, Birla Institute of Technology, Ranchi, India"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Bikash Kanti","family":"Sarkar","sequence":"additional","affiliation":[{"name":"Birla Institute of Technology, Ranchi, India"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"2432","reference":[{"key":"JITR.2018040109-0","doi-asserted-by":"publisher","DOI":"10.1007\/s00521-012-1196-7"},{"key":"JITR.2018040109-1","doi-asserted-by":"publisher","DOI":"10.4018\/JITR.2017010106"},{"key":"JITR.2018040109-2","doi-asserted-by":"publisher","DOI":"10.1007\/s12011-011-9306-4"},{"key":"JITR.2018040109-3","first-page":"1","article-title":"Statistical Comparisons of Classifiers over Multiple Data Sets","volume":"7","author":"J.Demsar","year":"2006","journal-title":"Journal of Machine Learning Research"},{"key":"JITR.2018040109-4","unstructured":"Fayyad, U. 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