Machine Learning Based Diagnostic Paradigm in Viral and Non-Viral Hepatocellular Carcinoma Using Resnet50 Algorithm

Gaddam Kalpana and C Yamini

International Journal of Scientific Research in Science and Technology, 2025, vol. 12, issue 3, 710-720

Abstract: This project investigates the application of machine learning techniques for diagnosing viral and non-viral hepatocellular carcinoma (HCC). Utilizing a comprehensive dataset of 204 entries and 50 features, including demographic, clinical, and laboratory parameters, the study evaluates the performance of several classification algorithms: Logistic Regression, Random Forest, Decision Tree, XGBoost, and AdaBoost. The models achieved accuracy rates of 90%, 80%, 68%, 88%, and 93%, respectively. The results indicate that machine learning approaches can significantly improve diagnostic accuracy for HCC, with AdaBoost demonstrating the highest accuracy. This research underscores the potential of advanced machine learning methods in enhancing the diagnostic precision and decision-making process in oncology.

Keywords: Machine Learning; Hepatocellular Carcinoma (HCC); Viral vs. Non-Viral Diagnosis; Classification Algorithms; Logistic Regression; Random Forest; Decision Tree; XGBoost; AdaBoost; Diagnostic Accuracy; Resnet50 Algorithm (search for similar items in EconPapers)
Date: 2025
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Persistent link: https://EconPapers.repec.org/RePEc:etm:ijsrst:v12:y2025:i3:id:885

DOI: 10.32628/IJSRST2512381

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