Designing a framework for diagnosing hepatitis disease using data mining techniques

Diagnosing liver disease is the challenging task for many public health physicians. In this study, we propose the framework to diagnose the hepatitis disease. For this study the adaptive rule based induction were formulated and the adaptive rule implemented in combined Robust BoxCox Transformation (RBCT) and Neural Network (NN) methods. The performance of proposed model is compared and results are evaluated based on the classification accuracy. Based on the evaluation parameters RBCT-NN obtained improved accuracy rate of 98.07% compared to other techniques thereby, minimizing the difficulty in predicting the hepatitis disease with reduced possible errors.