Volume 21, Issue 4 (December 2025)
IJEEE 2025, 21(4): 44-60 |
Back to browse issues page
Abstract: (2871 Views)
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.
Keywords: Hepatitis C virus, Synthetic Minority Over-sampling Technique, exploratory data analysis, SHapley Additive exPlanations, machine learning, classification algorithms, OPTUNA.
Type of Study: Research Paper |
Subject:
Biomedical Signal & Image Processing
Received: 2024/08/20 | Revised: 2025/12/29 | Accepted: 2025/04/03
Received: 2024/08/20 | Revised: 2025/12/29 | Accepted: 2025/04/03
| Rights and permissions | |
 |
This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License. |