Performance evaluation of naive Bayes and support vector machine in type 2 Diabetes Mellitus gene expression microarray data

Abstract

Abstract Type 2 Diabetes Mellitus (T2DM) is a metabolic disorder that the number of diabetics increases every year. So that prevention is needed by knowing the trigger of T2DM. Gene expression microarray data contains information of gene that can be used to determine the causes of T2DM. It is necessary to use certain techniques to analyze gene expression microarray data because it has a large amount of data and attributes. This study aims to evaluate the performance of algorithms in classifying gene expression microarray data. Algorithms that were used in this study were Naive Bayes, and Support Vector Machine (SVM). SVM used many kernels function such as Linear, Radial Basis Function (RBF), Polynomial, and Sigmoid. Information gain was used to select the features in GSE18732 dataset by choosing top 10, 20, 30, 40, and 50 features. Performance of algorithms was evaluated and compared by using 30% testing set and 20% testing set. The results of the study indicated that SVM using Polynomial kernel had a high performance if it was compared to other algorithms. It achieved 98.15% accuracy using 30% testing set and achieved 100% accuracy using 20% testing set.

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