Prediction of kidney disease stages using data mining algorithms

Early detection and characterization are considered to be critical factors in the management and control of chronic kidney disease. Herein, use of efficient data mining techniques is shown to reveal and extract hidden information from clinical and laboratory patient data, which can be helpful to assist physicians in maximizing accuracy for identification of disease severity stage. The results of applying Probabilistic Neural Networks (PNN), Multilayer Perceptron (MLP), Support Vector Machine (SVM) and Radial Basis Function (RBF) algorithms have been compared, and our findings show that the PNN algorithm provides better classification and prediction performance for determining severity stage in chronic kidney disease.