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Open AccessArticle

A Comprehensive Medical Decision–Support Framework Based on a Heterogeneous Ensemble Classifier for Diabetes Prediction

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Department of Information and Communication Engineering, Inha University, Incheon 22212, Korea
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Information Systems Department, Faculty of Computers and Informatics, Benha University, Banha 13518, Egypt
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Information Technology Department, Faculty of Computers and Information, Mansura University, Mansura 35516, Egypt
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Computer Engineering Department, INHA University, Incheon 22212, Korea
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Department of Computer Science and Engineering, Sejong University, Seoul 05006, Korea
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Author to whom correspondence should be addressed.
Electronics 2019, 8(6), 635; https://doi.org/10.3390/electronics8060635
Received: 19 May 2019 / Revised: 31 May 2019 / Accepted: 3 June 2019 / Published: 5 June 2019
Early diagnosis of diabetes mellitus (DM) is critical to prevent its serious complications. An ensemble of classifiers is an effective way to enhance classification performance, which can be used to diagnose complex diseases, such as DM. This paper proposes an ensemble framework to diagnose DM by optimally employing multiple classifiers based on bagging and random subspace techniques. The proposed framework combines seven of the most suitable and heterogeneous data mining techniques, each with a separate set of suitable features. These techniques are k-nearest neighbors, naïve Bayes, decision tree, support vector machine, fuzzy decision tree, artificial neural network, and logistic regression. The framework is designed accurately by selecting, for every sub-dataset, the most suitable feature set and the most accurate classifier. It was evaluated using a real dataset collected from electronic health records of Mansura University Hospitals (Mansura, Egypt). The resulting framework achieved 90% of accuracy, 90.2% of recall = 90.2%, and 94.9% of precision. We evaluated and compared the proposed framework with many other classification algorithms. An analysis of the results indicated that the proposed ensemble framework significantly outperforms all other classifiers. It is a successful step towards constructing a personalized decision support system, which could help physicians in daily clinical practice. View Full-Text
Keywords: diabetes mellitus; ensemble classifier; medical diagnosis; clinical decision support system diabetes mellitus; ensemble classifier; medical diagnosis; clinical decision support system
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El-Sappagh, S.; Elmogy, M.; Ali, F.; ABUHMED, T.; Islam, S.M.R.; Kwak, K.-S. A Comprehensive Medical Decision–Support Framework Based on a Heterogeneous Ensemble Classifier for Diabetes Prediction. Electronics 2019, 8, 635.

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