Preprint Article Version 1 Preserved in Portico This version is not peer-reviewed

Breast and Colon Cancer Classification from Gene Expression Profiles Using Data Mining Techniques

Version 1 : Received: 21 February 2020 / Approved: 23 February 2020 / Online: 23 February 2020 (13:21:40 CET)

A peer-reviewed article of this Preprint also exists.

Loey, M.; Jasim, M.W.; EL-Bakry, H.M.; Taha, M.H.N.; Khalifa, N.E.M. Breast and Colon Cancer Classification from Gene Expression Profiles Using Data Mining Techniques. Symmetry 2020, 12, 408. Loey, M.; Jasim, M.W.; EL-Bakry, H.M.; Taha, M.H.N.; Khalifa, N.E.M. Breast and Colon Cancer Classification from Gene Expression Profiles Using Data Mining Techniques. Symmetry 2020, 12, 408.

Journal reference: Symmetry 2020, 12, 408
DOI: 10.3390/sym12030408

Abstract

Early detection of cancer increases the probability of recovery. This paper presents an intelligent decision support system (IDSS) for the early diagnosis of cancer based on gene expression profiles collected using DNA microarrays. Such datasets pose a challenge because of the small number of samples (no more than a few hundred) relative to the large number of genes (on the order of thousands). Therefore, a method of reducing the number of features (genes) that are not relevant to the disease of interest is necessary to avoid overfitting. The proposed methodology uses the information gain (IG) to select the most important features from the input patterns. Then, the selected features (genes) are reduced by applying the grey wolf optimization (GWO) algorithm. Finally, the methodology employs a support vector machine (SVM) classifier for cancer type classification. The proposed methodology was applied to two datasets (Breast and Colon) and was evaluated based on its classification accuracy, which is the most important performance measure in disease diagnosis. The experimental results indicate that the proposed methodology is able to enhance the stability of the classification accuracy as well as the feature selection

Subject Areas

machine learning; cancer diagnosis; grey wolf; optimization algorithm; support vector machine; information gain; feature selection

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