A Proposed Ensemble Model with Feature Selection Technique for Classification of Chronic Kidney Disease

Abstract

Healthcare diagnosis system is very important and critical task in medical science for doctors and medical students. Chronic kidney disease is a very serious and dangerous problem which is directly related to the human life. In this research work, we have used data mining and feature selection technique to develop the robust and computationally efficient model for classifying chronic and non chronic kidney disease. An ensemble model is constructing through combination of two more similar types of trained model which helps to improve the performance. Feature selection is frequently used in machine learning area to raise a model with a few numbers of features which increase the performance of classification accuracy. The proposed feature selection techniques principle of Genetic Search (GS) and Greedy Stepwise Search (GSW). This proposed technique called GS-NB utilizes a pursuit methodology which is embedded in the Genetic Algorithm to select the features based on natural selection, the procedure that drives biological evolution. Then proposed technique called GSW-NB utilizes a search strategy that is included in the Greedy Stepwise to search the relevant feature based on problem solving heuristic for settling the locally ideal decision at each stage. The performance of suggested technique were estimated on Chronic Kidney Disease (CKD) classification problems and compared with proposed feature selection method. The classification techniques namely the Single Rule Classification (SRC), Conditional Inference Tree (CIT) and their ensemble model (SRC, CIT) have used for classification of CKD. The proposed ensemble model have used stacking learning technique which combines multiple classifiers, hence we can improve the performance of classifiers. The classifier performance is measured with observed accuracy, sensitivity and specificity. The experimental results demonstrated that the ensemble model (SRC, CIT) with GS-NB and GSW-NB can recognized CKD better than existing model. The proposed model can be beneficial and useful in medical science for identifying and diagnosis of chronic kidney disease.