An Empirical Comparison of Supervised Learning Algorithms in Disease Detection

In this paper empirical comparison is carried out with various supervised algorithms. We studied the performance criterion of the machine learning tools such as Naïve Bayes, Support vector machines, Radial basis neural networks, Decision trees J48 and simple CART in detecting diseases. We used both binary and multi class data sets namely WBC, WDBC, Pima Indians Diabetes database and Breast tissue from UCI machine learning depositary. The experiments are conducted in WEKA. The aim of this research is to find out the best classifier with respect to disease detection.