Hepatitis C virus data analysis and prediction using machine learning

Abstract

Medical decision support systems have been on the rise with technological advances and they have been the subject of many studies. Developing an effective medical decision support system requires a high amount of accuracy, precision, and sensitivity as well as time efficiency that is inversely proportional to the complexity of the model. Hepatitis C virus (HCV) infection is one of the most important causes of chronic liver disease worldwide. In this study, data discovery has been made by applying data science processes, and the HCV has been estimated with machine learning methods. By analyzing and visualizing the values in the data set, features that may be important for HCV was determined, and HCV estimation was made using various machine learning methods, pre-processing and feature extraction. According to the features obtained from this study, the estimation of HCV can be made automatically and can be a decision support system that helps the researchers and clinicians. In this study, HCV was obtained with 99.31% accuracy by adding new features and eliminating imbalances between classes. The model in this study can be used as an alternative method in the prediction of Hepatitis C-related diseases.