A Two Stage Hybrid Ensemble Classifier Based Diagnostic Tool for Chronic Kidney Disease Diagnosis Using Optimally Selected Reduced Feature Set

This paper presents an idea of applying a two stage hybrid ensemble classifier for improving the prediction accuracy of Machine Learning based automated diagnosis of chronic kidney disease on the basis of values of an optimally selected subset of clinical and physiological parameters fed to it. Chronic kidney disease is a generalized term for various heterogeneous disorders affecting the structure and function of the kidney. It is a disease with high mortality rate. In this paper the authors have proposed a two stage hybrid ensemble technique with very high efficiency. In two stage hybrid ensemble classifier the potential of individual classification algorithms are combined together. In addition to this the authors optimally selected 8 parameters of prime importance from the set of 24 parameters of the dataset used for the study .The parameters (features) selected represent the intersection of the two sets; one containing medically essential parameters arranged in decreasing contribution to the diagnosis and other set containing parameters ranked in decreasing order of their contribution in the Machine Learning classification process. The results depict that the two stage hybrid ensemble is a very efficient method for classification of chronic kidney disease. The results of this ensemble classifier on the optimally selected reduced feature set (with 8 parameters) as well as the complete feature set (with 24 parameters) in terms of various performance metrics are predictive accuracy of (2-class) 100%, sensitivity of 1, precision of 1, specificity of 1 and F-value of 1. The GUI based diagnostic tool developed on the basis of the proposed ensemble can act as a tool for assisting doctors for cross-validating their findings of initial screening of chronic kidney disease using fewer clinical parameters thus helping them to attend to the needs of more patients in less time.

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