<![CDATA[Hepatitis C is a liver disease that can progress to chronic conditions such as cirrhosis and liver cancer. Early detection is essential and can be supported through machine learning approaches. This study analyzes the effect of feature selection and hyperparameter tuning on the performance of the XGBoost model in classifying hepatitis C infection. The dataset, obtained from Kaggle, contains laboratory test attributes. The preprocessing stage involved handling missing values, encoding categorical variables, removing outlier classes, and normalizing data using StandardScaler. After stratified splitting, the training set was balanced using the SMOTE technique. Feature selection was carried out using the ANOVA F-score method, and hyperparameter tuning was performed using GridSearchCV. Three model scenarios were compared: baseline, with feature selection, and with combined feature selection and hyperparameter tuning. The evaluation results showed that the third model achieved the best performance with 96% accuracy, 79% precision, 81% recall, and a 78% F1-score, despite a slight decrease in the ROC AUC value. This approach has proven effective in improving model performance and is relevant for supporting more accurate hepatitis C diagnosis systems.]]>
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