Diffferentiating Viral and Non-viral Hepatocellular Carcinoma using Machine Learning

Abstract: The Pathogenesis and treatment outcomes of hepatocellular carcinoma(HCC), a major cause of cancer-related death globally, are influenced by a variety of etiological factors. Using a large dataset comprising clinical, demographic, and molecular characteristics, this study explores the possibility of using machine learning methods to distinguish between viral and non-viral HCC. To evaluate the data and find the important predictors of HCC etiology, we used a variety of machine learning models, such as stacking classifiers, Logistic Regression, Decision tree, random forests, and neural networks. Our findings show that machine learning techniques can classify HCC subtypes with high accuracy, and that certain features like viral load, liver function tests, and histological features emerge as important discriminators. The result highlight how incorporating machine learning into clinical practice can be beneficial. Histological features emerging as important with different etiological factors impacting its pathogenesis and treatment outcomes, hepatocellular carcinoma(HCC) is a prominent case of cancerrelated mortality that occurs globally. This study uses a large dataset that contains clinical, demographic, and molecular variables to examine how major cause of cancer-related death globally, hepatocellular carcinoma(HCC) has a variety of etiological elements that affect both its pathology. This research uses a large dataset that contains clinical, demographic, and molecular characteristics to examine how well machine learning algorithms can distinguish between viral and non-viral HCC. Support vector machines, Random forests and neural networks are just a few of the machine learning models , we used to examine the data and find the important indicators of etiology of HCC. The results highlight how incorporating machine learning into clinical practice can improve the accuracy of diagnoses and guide specialized treatment plans for patients with HCC.