Genetic algorithm based wrapper feature selection on hybrid prediction model for analysis of high dimensional data

Abstract

Data mining concepts have been extensively used for disease prediction in the medical field. Many Hybrid Prediction Models (HPM) have been proposed and implemented in this area, however, there is always a need for increasing accuracy and efficiency. The existing methods take into account all the features to build the classifier model thus reducing the accuracy and increasing the overall processing time. This paper proposes a Genetic Algorithm based Wrapper feature selection Hybrid Prediction Model (GWHPM). This model initially uses k-means clustering technique to remove the outliers from the dataset. Further, an optimal set of features are obtained by using Genetic Algorithm based Wrapper feature selection. Finally, it is used to build the classifier models such as Decision Tree, Naive Bayes, k nearest neighbor and Support Vector Machine. A comparative study of GWHPM is carried out and it is observed that the proposed model performed better than the existing methods. � 2014 IEEE.