Hepatitis C is a global health concern, with new cases being reported worldwide every year. Accurate prediction of the disease's stage is crucial in providing timely and effective treatment to patients. To achieve this, various non-invasive biochemical serum markers and clinical data have been used to identify the stage of the disease. Machine learning techniques have emerged as a powerful tool to predict the stage of this chronic liver disease without resorting to invasive biopsy procedures. In this context, an intelligent diagnostic system for Hepatitis C stage prediction has been developed using machine learning algorithms such as Artificial Neural Network (ANN), K-nearest neighbor (KNN), Support Vector Machine (SVM), and Logistic Regression.

Hepatitis C; machine learning; Python ; Jupiter notebook