Comparative Study on the Performance of MMIFS and DMIFS Feature Selection Algorithms on Medical Datasets

Abstract

Background/Objectives: Feature selection is one of the preprocessing techniques used for removing redundant and irrelevant features. The objective of this research work is to show that small set of relevant features can improve the performance of classification algorithms. Methods/Statistical Analysis: This paper compares the performance of two feature selection algorithms, Modified Mutual Information based Feature Selection (MMIFS) and Dynamic Mutual Information based Feature Selection (DMIFS) The performance of these feature selection algorithms on the medical datasets is analyzed. The performances of c4.5 classification algorithm before and after feature selection are analyzed. Findings: The comparative study show that the feature selection algorithms have selected prominent features of the medical datasets. The percentage of feature reduction and the improvement in the accuracy of the classification algorithm are used for validation. The result shows an improvement in the accuracy of the classification algorithm. Applications/ Improvements: The reduction in the number of features means diagnosis of the disease with limited number of relevant features. Integrating feature selection techniques and machine learning algorithms will give a better decision making tool which is appreciable in medical domain.