Treatment Response Prediction in Hepatitis C Patients using Machine Learning Techniques

The proper prognosis of treatment response is crucial in any medical therapy to reduce the effects disease and medication as well. mortality rate due hepatitis c virus (HCV) high Pakistan well all over world. During disease, prediction against particular medicine difficult. This paper focuses on predicting a drug: “L-ornithine L-Aspartate (LOLA)” patients. We have used various machine learning techniques for response, including: “K Nearest Neighbor, kStar, Naive Bayes, Random Forest, Radial Basis Function, PART, Decision Tree, OneR, Support Vector Machine Multi-Layer Perceptron”. Performance measures analyze performance include, “Accuracy, Recall, Precision, F-Measure”.