Detection of kidney disease using various intelligent classifiers

Scientists are interesting to develop and improve analytical tools for medical diagnosis. Machine learning technique is one of the tools that is used in medical analysis and diagnosis. This research considers the implementation of data mining classification tools on the kidney patient data sets. The aim of this paper is to predict kidney function failure through the implementation of data mining classifiers tools. The experiment is performed on different algorithms like Back Propagation Neural Network, Naïve Bayes, Decision Table, Decision trees, K nearest neighbor and One Rule classifier. The experimental results show that the Naïve Bayes algorithm provides better result than the other classification algorithms and produces 99.36 % accuracy and 0.977 sensitivity.