enhancing hepatitis c diagnosis: the impact of smote, optuna, and shap on detection methods

Hepatitis c virus (hcv) detection is a critical aspect of early intervention and effective management of the disease. this paper presents a comprehensive study focused on enhancing the detection accuracy of hcv through the integration of advanced techniques - smote, optuna, and shap - alongside extensive exploratory data analysis (eda). the study addresses class imbalance using synthetic minority over-sampling technique (smote), optimizes model performance with optuna for hyperparameter tuning, and provides model interpretability using shap (shapley additive explanations). eda is leveraged to gain valuable insights into the dataset's characteristics, ensuring robust data preprocessing and feature engineering. the results show 97% improved hcv detection performance, highlighting the efficacy of the proposed methodology in medical diagnostics and aiding healthcare professionals in making informed clinical decisions.