Hepatitis Prediction Model based on Data Mining Algorithm and Optimal Feature Selection to Improve Predictive Accuracy

Abstract

Data mining techniques are widely used in classification and prediction in the field of bioinformatics. This even helps in identifying the relationships and patterns in the data which helps in construction of prediction model. Classification and prediction model supports medical diagnosis which helps in improving the quality of patients. Noisy features are identified and eliminated by chi-square attribute evaluation which may further improve the classification accuracy of support vector machine. Hepatitis patients are those who need continuous special medical treatment to reduce mortality rate. Machine learning technologies are used for classification and prediction for Hepatitis patients. General Terms Data mining algorithm such as Support Vector Machine which is the most widely used algorithm for classification and prediction. Chi-square attribute evaluation is used to assign weight to the attributes thereby improving the classification accuracy.