Feature selection for medical diagnosis : Evaluation for cardiovascular diseases

Machine learning has emerged as an effective medical diagnostic support system. In a medical diagnosis problem, a set of features that are representative of all the variations of the disease are necessary. The objective of our work is to predict more accurately the presence of cardiovascular disease with reduced number of attributes. We investigate intelligent system to generate feature subset with improvement in diagnostic performance. Features ranked with distance measure are searched through forward inclusion, forward selection and backward elimination search techniques to find subset that gives improved classification result. We propose hybrid forward selection technique for cardiovascular disease diagnosis. Our experiment demonstrates that this approach finds smaller subsets and increases the accuracy of diagnosis compared to forward inclusion and back-elimination techniques.

► Hybrid feature subset selection system is modelled for dimensionality reduction and improved classification. ► We employ algorithm for three data sets related to heart disease. ► Performance of feature selection techniques is compared using parameters accuracy and area under curve. ► The results are promising when hybrid forward feature selection technique is used.