Hepatitis Disease Diagnosis Using Multiple Imputation and Neural Network with Rough Set Feature Reduction

Intelligent automated decision support systems are found to be useful for early detection of hepatitis for augmenting survivability. We present here an intelligent system for hepatitis disease diagnosis using UCI data set for experiment. We use multiple imputation technique for managing missing values in the UCI data set. One of the potential tools in this context is neural network for classification. For better diagnostic classification accuracy, various feature selection techniques are deployed as prerequisite. These features are considered to be more informative to the doctors for taking final decision. This work attempts rough set-based feature selection (RS) technique. For classification, we use incremental back propagation learning network (IBPLN), and Levenberg-Marquardt (LM) classification tested on UCI data base. We compare classification results in terms of classification accuracy, specificity, sensitivity and receiver-operating characteristics curve area(AUC).