Iranian Association of Medical InformaticsFrontiers in Health Informatics2676-710410120210326Machine Learning Models for Diagnostic Classiﬁcation of Hepatitis C Tests27427410.30699/fhi.v10i1.274ENOladosuOyebisiOladimejiUniversity of Ibadan. firstname.lastname@example.orgAbimbolaOladimejiOladimejiOlayanju202101222021032320210303Background - Hepatitis C is a chronic infection caused by hepatitis c virus - a blood borne virus. Therefore, the infection occurs through exposure to small quantities of blood. It has been estimated by World Health Organization (WHO) to have affected 71 million people worldwide. This infection costs individual, groups and government a lot because no vaccine has been gotten yet for the treatment. This disease is likely to continue to affect more people because its long asymptotic phase which makes its early detection not feasible. Method - In this study, we have presented machine learning models to automatically classify the diagnosis test of hepatitis and also ranked the test features in order to know how they contribute to the classification which help in decision making process by the health care industry. Results - The models were evaluated based on metrics such as F-measure, Precision-Recall curve and Receiver Operating Characteristic Area Under Curve. Conclusion - This discovery has the potential to impact on clinical practice, when health workers aim at classifying diagnosis result of disease at its early stage.
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