A KLD-LSSVM based computational method applied for feature ranking and classification of primary biliary cirrhosis stages

Abstract

Medical datasets having variety of features can increase the complexity of decision process. Prioritising these features with respect to medical sickness is a prime task for effective assessment of patients' health. In this study, a two-step computational method based on Kullback-Leibler divergence (KLD) method and least squares support vector machine (LSSVM) is presented as an integrated model and a LSSVM-based approach is projected as an individual model. KLD was employed to rank the features and radial basis kernel function-based LSSVM approach was deployed to classify primary biliary cirrhosis (PBC) stages. Performance of several machine-learning algorithms, individually as well as in integration with KLD, was evaluated on a real-life biomedical PBC dataset. Simulation results indicated that proposed LSSVM and KLD-LSSVM-based frameworks had shown robustness to the noisy data and had outperformed other individual and integrated methods, respectively. It is concluded that the proposed methodologies can be productively applied to real-life health examination datasets containing a variety of features and multiple decision classes.