Predicting hepatitis C infection with machine learning algorithms: a prospective study

Globally, chronic hepatitis C virus (HCV) infection affects millions ofpeople and leads to a high number of deaths annually. In 2019, the World Health Organization (WHO) recorded around 290,000 deaths related to HCV, a virus transmitted mainly through blood that causes liver damage. The virus has infected morethan 169 million people worldwide. This study aims to compare the performance ofmachine learning (ML) models for HCV detection. ML models such as logistic regression (LR), random forest (RF), decision tree (DT), and catBoost classifier (CATBC) were used. To carry out this task, a dataset of 615 patient records, and 14 variables were used. This research process was carried out in multiple phases, encompassing model understanding, data analysis and cleaning, ML model training, and subsequent model evaluation. The results revealed that the gradient boosting(GB) model stood out by achieving the best performance and highest accuracy, achieving a rate of 94% in HCV detection, this demonstrates outstanding performancecompared to the other models such as LR, RF, k-nearest neighbor (KNN), DT, and CATBC, which obtained accuracy rates of 89%, 93%, 85%, 93%, 93%, and 92%, respectively.