Hepatitis C virus prediction in machine learning

The infection of the hepatitis C virus is a considerable medical field challenge globally that can require the development of effective as well as accurate diagnostic approaches. Traditional diagnostic techniques, while widely used, often have limits when it comes to accuracy, accessibility, and cost-effectiveness. This study proposes a predictive model utilizing machine learning to early diagnose the liver HCV, utilizing the Extra Trees Classifier in conjunction with the Synthetic Minority Over-Sampling Technique to address the challenge of class imbalance within the dataset. Three freely accessible datasets, HCV-EGY, ILPD, and HCV, have been used in both training and evaluation, thereby ensuring robustness and generalisability across diverse population groups. The model of this study achieves an accuracy of 98% of both the HCV and HCV-EGY datasets, while the ILPD achieved 95%. exceeding the performance of traditional diagnostic methods and demonstrating the effectiveness of machine learning in improving early HCV detection. An analysis of feature importance was performed to determine the key biomarkers that significantly influence the classification process. The interpretability component is essential, offering insights into the biological markers linked to HCV infection, which may assist in refining diagnostic criteria and treatment strategies. This study highlights the potential of non-invasive, data-driven diagnostic methods in clinical settings through the application of advanced machine learning techniques. The results indicate that machine learning models can function as dependable, efficient, and interpretable instruments to aid healthcare professionals in the early diagnosis of HCV. This research enhances the existing evidence for AI-driven methodologies in medical diagnostics, facilitating the development of more accurate and accessible disease detection frameworks.