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Impact of preprocessing on medical data classification

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Abstract

The significance of the preprocessing stage in any data mining task is well known. Before attempting medical data classification, characteristics ofmedical datasets, including noise, incompleteness, and the existence of multiple and possibly irrelevant features, need to be addressed. In this paper, we show that selecting the right combination of preprocessing methods has a considerable impact on the classification potential of a dataset. The preprocessing operations considered include the discretization of numeric attributes, the selection of attribute subset(s), and the handling of missing values. The classification is performed by an ant colony optimization algorithm as a case study. Experimental results on 25 real-world medical datasets show that a significant relative improvement in predictive accuracy, exceeding 60% in some cases, is obtained.

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Authors and Affiliations

  1. Computer Science Department, College of Computer and Information Sciences, Prince Sultan University, Riyadh, 11586, Saudi Arabia

    Sarab Almuhaideb & Mohamed El Bachir Menai

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  1. Sarab Almuhaideb
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  2. Mohamed El Bachir Menai
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Correspondence to Sarab Almuhaideb.

Additional information

Sarab Almuhaideb is a PhD student in the Department of Computer Science, King Saud University, Saudi Arabia. She is a lecturer in the Department of Computer Science, Prince Sultan University, Saudi Arabia. Her research interests include issues related to machine learning, evolutionary computation, and hybrid metaheuristics.

Mohamed El Bachir Menai received his PhD degree in computer science from Mentouri University of Constantine, Algeria, and University of Paris VIII, France in 2005. He also received a “Habilitation universitaire” in computer science from Mentouri University of Constantine, in 2007 (it is the highest academic qualification in Algeria, France and Germany). He is currently a professor in the Department of Computer Science at King Saud University, Saudi Arabia. His main interests include evolutionary computing, data mining, machine learning, natural language processing, and satisfiability problems.

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Almuhaideb, S., Menai, M.E.B. Impact of preprocessing on medical data classification. Front. Comput. Sci. 10, 1082–1102 (2016). https://doi.org/10.1007/s11704-016-5203-5

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