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@article{178528,
        author = {Shruthi M K and Venkata Charan Kumar Reddy T and Preetham G and Mohamed Akif Ur Rahman and Sujan Gowda G M and Ramesh B E},
        title = {Machine Learning-Based Diagnostic Paradigm in Viral and Non-Viral Hepatocellular Carcinoma},
        journal = {International Journal of Innovative Research in Technology},
        year = {2025},
        volume = {11},
        number = {12},
        pages = {5223-5227},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=178528},
        abstract = {Hepatocellular carcinoma (HCC) is one of the most prevalent and deadly forms of liver cancer, often resulting from chronic viral infections such as HBV and HCV. Differentiating between viral and non-viral HCC is critical for proper treatment planning, yet traditional diagnostic methods often fall short due to their invasive nature and limited accuracy. This project introduces a machine learning-based diagnostic system that classifies HCC into viral and non-viral categories using publicly available datasets. We employed multiple classifiers—Decision Tree, Random Forest, Logistic Regression, and a Stacking Classifier—to enhance diagnostic accuracy. The system is designed to reduce human error, support faster and more accurate diagnosis, and ultimately improve patient outcomes. Evaluation metrics such as accuracy, precision, recall, and F1-score are used to determine the best-performing model, with the stacking classifier demonstrating superior predictive performance.},
        keywords = {Hepatocellular Carcinoma, Viral HCC, Non-Viral HCC, Machine Learning, Stacking Classifier, Diagnostic Accuracy, Liver Cancer},
        month = {May},
        }