Classification and Comparison of Hepatitis-C using Data Mining Technique

The major focus in this paper is to get the factors that shows the significance in predicting the risks of virus of hepatitis-C. 2 datasets were used for this purpose the first one is gathered from UCI Repository and the second one is taken from Zahid Medical Centre with the help of Dr. Abdul Fateh. There are nineteen features and a class feature with classification in binary. The first data set that is gathered from UCI repository contains 155 records with missing values in most of them in order to reduce this technique of normalization is applied. Now for qualitative approaches for data reduction as well as quantitative the binary logistic regression is used. The first result gathered from the Zahid Medical Centre gave us 58% accuracy result using these techniques. And second result using these procedures produced about 90% accurate classification. Our approach gives good classification rate only by using total 37% fields.