Classification Analysis on Medical Data

Classification is the most frequent technique for mining data in various domains. The accuracy measures are used to predict the accuracy, sensitivity, specificity and ROC values. This research focuses on finding the accuracy of machine learning techniques using medical data sets, which adopts the supervised learning techniques for analyzing the medical datasets. The study determines the classification accuracy of four medical datasets such as hepatitis dataset, heart disease dataset, and breast cancer data set and diabetes data set. The classifiers are employed to find out the correctly classified instances, incorrectly classified instances and Error rate. This Investigation helps to determine the accuracy of the classification analysis and it leads to find the disease affected stages. Also assists the medical practitioners in terms of treating the patients based on the stages of disease.