Machine Learning-Driven Strategies for Laboratory Diagnostic Pathway Optimization

The Hepatitis C Virus (HCV) is a blood-borne infection that mostly affects the liver. If untreated, it can cause cirrhosis, chronic liver disease, or liver cancer. The risk of serious liver damage rises when early signs are absent, making prompt detection difficult. For treatment and patient management to be successful, an early and precise diagnosis is necessary. Based on the outcomes of laboratory tests, different machine-learning approaches were used in this work to categorize blood donors and Hepatitis C patients. The dataset includes 615 instances and 12 features. Mean/mode imputation was used for data preprocessing to handle missing values. The most important characteristics for categorization were identified using feature selection approaches. Hepatitis C infection was predicted using a variety of machine learning models, such as ensemble learning, random forest, decision trees, and support vector machines. To guarantee a thorough examination of model efficacy, the models’ performance was assessed using accuracy, precision, recall, F1-score, ROC curve, and confusion matrix. According to the results, Ensemble Learning had the lowest accuracy of 87.8%, while the Decision Tree classifier outperformed the other models with the best accuracy of 99.5%. The work illustrates the potential of ML-based predictive models in medical applications by highlighting the effects of several machine-learning techniques on HCV detection. By lowering the chance of complications and assisting medical professionals in making well-informed judgments, the findings help to improve early detection techniques.