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Open AccessArticle

A Comparative Performance Evaluation of Classification Algorithms for Clinical Decision Support Systems

1
Data Science Group, Center for Mathematical and Computational Sciences, Institute for Basic Science (IBS), Daejeon 34141, Korea
2
Department of Industrial Engineering, Ulsan National Institute of Science and Technology, Ulsan 44919, Korea
*
Author to whom correspondence should be addressed.
Mathematics 2020, 8(10), 1814; https://doi.org/10.3390/math8101814
Received: 8 September 2020 / Revised: 1 October 2020 / Accepted: 6 October 2020 / Published: 16 October 2020
(This article belongs to the Special Issue Recent Advances in Data Mining and Their Applications)
Classification algorithms are widely taken into account for clinical decision support systems. However, it is not always straightforward to understand the behavior of such algorithms on a multiple disease prediction task. When a new classifier is introduced, we, in most cases, will ask ourselves whether the classifier performs well on a particular clinical dataset or not. The decision to utilize classifiers mostly relies upon the type of data and classification task, thus making it often made arbitrarily. In this study, a comparative evaluation of a wide-array classifier pertaining to six different families, i.e., tree, ensemble, neural, probability, discriminant, and rule-based classifiers are dealt with. A number of real-world publicly datasets ranging from different diseases are taken into account in the experiment in order to demonstrate the generalizability of the classifiers in multiple disease prediction. A total of 25 classifiers, 14 datasets, and three different resampling techniques are explored. This study reveals that the classifier that is likely to become the best performer is the conditional inference tree forest (cforest), followed by linear discriminant analysis, generalize linear model, random forest, and Gaussian process classifier. This work contributes to existing literature regarding a thorough benchmark of classification algorithms for multiple diseases prediction. View Full-Text
Keywords: disease prediction; classification algorithm; multiple diseases; comparative study; significance test disease prediction; classification algorithm; multiple diseases; comparative study; significance test
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Tama, B.A.; Lim, S. A Comparative Performance Evaluation of Classification Algorithms for Clinical Decision Support Systems. Mathematics 2020, 8, 1814.

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