Framework for Diagnosing Hepatitis Disease using Classification Algorithms.

Hepatitis is a liver disease which affects major population in all age group. Diagnosing the disease is the challenging task for many public health physicians. In this study, we propose the performance and usage of classification techniques like Neural Network, Naive Bayes and Support Vector Machine to predict the accuracy in diagnosing hepatitis disease. Hepatitis dataset is taken from the Indian Liver Patient Dataset to train the proposed model. The performance of the proposed classification techniques is compared and evaluated based on the sensitivity and specificity to obtain the accuracy of the model. Based on the evaluation, Neural Network obtained improved accuracy rate compared to the other techniques thereby, minimizing the time duration in diagnosing of the hepatitis disease with reduced possible errors.