Selected feature selection methods for classifying patients with Hepatitis C

Abstract

Hepatitis C is a disease that is often asymptomatic, so that it is detected completely by chance. However, it is worth remembering that untreated hepatitis C can lead to a number of serious complications, such as post-inflammatory cirrhosis and hepatocellular carcinoma, among others. Therefore, diagnosis and proper treatment are very important here. Appropriate data mining techniques can be helpful here and can support the work of medical. This work analyzed the data of 73 patients who have been diagnosed with hepatitis C through serological and histopathological testing. During the initial phase of the study, the data underwent classification. Then feature selection was carried out, followed by the same classifiers again. Finally, the obtained classification rules are also presented.