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Using Two Methods of Decision Tree and Regression in Identifying the Most Effective Factors in the Occurrence of Hepatitis C Diseases, Hepatitis C with Fibrosis and Hepatitis C with Cirrhosis and Checking the Results and Accuracy of These Two Methods

Arefe Bagheri1*, and Mina Kalini2

One of the common disorders of the liver is the hepatitis C virus, which after a period causes serious damage to the liver and the failure of this important organ. This disease can improve or progress to liver fibrosis or cirrhosis. In this regard, this research focuses on the identification of effective factors and the extent of these factors in the occurrence of hepatitis C disease and two other types along with liver fibrosis or cirrhosis so that doctors and patients can pay more attention to these factors and avoid the occurrence of the disease. In this research, using the information related to blood donors, the implementation operation is carried out to identify the effective factors in the occurrence of disease in these persons. This research uses the two techniques of decision tree and regression to carry out the implementation work, and finally the efficiency of each is examined and compared. At the end of this research and after examining the results of modeling methods, the three factors ALB, AST and CHE are known as the most effective factors in diagnosing the occurrence of hepatitis C and its advanced types. The results of this research show that the accuracy of diagnosis in this research with the decision tree method equals 94.57% and with the regression technique has an accuracy of 91.28%.

Hepatitis C; Decision tree; Regression; Data mining; Liver diseases