  Diagnosis of liver infection at preliminary stage is important for better
treatment. In todays scenario devices like sensors are used for detection of
infections. Accurate classification techniques are required for automatic
identification of disease samples. In this context, this study utilizes data
mining approaches for classification of liver patients from healthy
individuals. Four algorithms (Naive Bayes, Bagging, Random forest and SVM) were
implemented for classification using R platform. Further to improve the
accuracy of classification a hybrid NeuroSVM model was developed using SVM and
feed-forward artificial neural network (ANN). The hybrid model was tested for
its performance using statistical parameters like root mean square error (RMSE)
and mean absolute percentage error (MAPE). The model resulted in a prediction
accuracy of 98.83%. The results suggested that development of hybrid model
improved the accuracy of prediction. To serve the medicinal community for
prediction of liver disease among patients, a graphical user interface (GUI)
has been developed using R. The GUI is deployed as a package in local
repository of R platform for users to perform prediction.
