Hepatitis C is a critical global health issue where early and accurate diagnosis is essential for preventing severe liver disease. Machine learning techniques provide a promising avenue for developing automated diagnostic systems based on standard clinical data. This study presents a systematic evaluation of ten different machine learning classifiers on the UCI Hepatitis C dataset to determine which disease detection model is highly effective. The evaluated models include, together with conventional classifiers such as SVM and LR, as well as ensemble techniques like RF, XGBoost, and AdaBoost. Data transformation and feature selection based on ANOVA F test were used to prepare the data for modeling. Our comparative analysis demonstrates that ensemble-boosting algorithms deliver superior performance. The AdaBoost classifier achieved 99.46% accuracy, making it the most effective model.