This document discusses using data mining algorithms and feature selection techniques to improve classifier accuracy for chronic kidney disease prediction. It analyzes the J48 and Naive Bayes classifiers on different combinations of attributes from a chronic kidney disease dataset, ranked by information gain. The classifiers were tested on attribute combinations from highest to lowest ranked attributes. J48 achieved the highest accuracy of 96.75% using highly ranked attributes, demonstrating the benefit of feature selection for improving classifier performance.