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The present research proposes a two-step framework in which an entropy-based feature-selection approach is introduced in the first step and a rule-base hybrid model using Perfect Rule Induction by Sequential Method (PRISM) is explored in the subsequent step for effective diagnosis of diseases. The suggested feature-selection technique is validated using five state-of-the-art classifiers namely C4.5 (a decision tree-based classifier), na\u00efve Bayes (NB), Repeated Incremental Pruning to Produce Error Reduction (RIPPER), neural network (NN) and support vector machine (SVM) over fourteen benchmark diseases that are very common and the leading causes of deaths. Next, on the basis of top three performance metrics, viz., prediction accuracy, sensitivity and false positive rate, the performance of the hybrid model over the datasets is compared with its base learner: PRISM, two other competent learners namely C4.5 and NN, and some specialized models. The empirical outcomes positively demonstrate that the hybrid model with application of feature-selection method is a generic model and effective in diagnosing diseases. More importantly, the model not only is able to produce good results but also to elucidate its knowledge in understandable: IF-THEN form (convenient for medical practitioners).<\/jats:p>","DOI":"10.1093\/comjnl\/bxz034","type":"journal-article","created":{"date-parts":[[2019,3,18]],"date-time":"2019-03-18T08:29:23Z","timestamp":1552897763000},"page":"364-382","source":"Crossref","is-referenced-by-count":6,"title":["A Two-Step Knowledge Extraction Framework for Improving Disease Diagnosis"],"prefix":"10.1093","volume":"63","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-3677-2649","authenticated-orcid":false,"given":"Bikash Kanti","family":"Sarkar","sequence":"first","affiliation":[{"name":"Department of Computer Science and Engineering, Birla Institute of Technology, Mesra, Ranchi, India"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"286","published-online":{"date-parts":[[2019,4,23]]},"reference":[{"volume-title":"Machine Learning","year":"1997","author":"Mitchell","key":"2020042105454011100_bxz034C1"},{"volume-title":"Handbook of Data Mining and Knowledge Discovery","year":"2002","author":"Klosgen","key":"2020042105454011100_bxz034C2"},{"volume-title":"C4.5: Programs for Machine Learning","year":"1993","author":"Quinlan","key":"2020042105454011100_bxz034C3"},{"key":"2020042105454011100_bxz034C4","doi-asserted-by":"crossref","first-page":"47","DOI":"10.1145\/319382.319391","article-title":"Knowledge discovery based on neural networks","volume":"42","author":"Fu","year":"1999","journal-title":"Commun. 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