  Areas where Artificial Intelligence (AI) & related fields are finding their
applications are increasing day by day, moving from core areas of computer
science they are finding their applications in various other domains.In recent
times Machine Learning i.e. a sub-domain of AI has been widely used in order to
assist medical experts and doctors in the prediction, diagnosis and prognosis
of various diseases and other medical disorders. In this manuscript the authors
applied various machine learning algorithms to a problem in the domain of
medical diagnosis and analyzed their efficiency in predicting the results. The
problem selected for the study is the diagnosis of the Chronic Kidney
Disease.The dataset used for the study consists of 400 instances and 24
attributes. The authors evaluated 12 classification techniques by applying them
to the Chronic Kidney Disease data. In order to calculate efficiency, results
of the prediction by candidate methods were compared with the actual medical
results of the subject.The various metrics used for performance evaluation are
predictive accuracy, precision, sensitivity and specificity. The results
indicate that decision-tree performed best with nearly the accuracy of 98.6%,
sensitivity of 0.9720, precision of 1 and specificity of 1.
