A Novel Hybrid Approach for Chronic Disease Classification

Abstract

A two-phase diagnostic framework based on hybrid classification for the diagnosis of chronic disease is proposed. In the first phase, feature selection via ReliefF method and feature extraction via PCA method are incorporated. In the second phase, efficient optimization of SVM parameters via grid search method is performed. The proposed hybrid classification approach is then tested with seven popular chronic disease datasets using a cross-validation method. Experiments are then conducted to evaluate the presented classification method vis-à-vis four other existing classifiers that are applied on the same chronic disease datasets. Results show that the presented approach reduces approximately 40% of the extraneous and surplus features with substantial reduction in the execution time for mining all datasets, achieving the highest classification accuracy of 98.5%. It is concluded that with the presented approach, excellent classification accuracy is achieved for each chronic disease dataset while irrelevant and redundant features may be eliminated, thereby substantially reducing the diagnostic complexity and resulting computational time.