Hepatitis Diagnosis: A Comprehensive Review of Machine Learning Classification Algorithms

Abstract

Hepatitis is a liver-related medical disorder caused by inflammation, often caused by hepatitis virus infection or an unknown source. There are five primary hepatitis viruses: A, B, C, D, and E.  Machine learning (ML) algorithms have emerged as a promising tool for hepatitis diagnosis, leveraging vast datasets and complex patterns. This review examines the application of ML classification algorithms in hepatitis diagnosis, focusing on challenges faced in traditional diagnostic approaches and the potential of ML techniques to address these. Various ML algorithms, including decision trees, support vector machines, neural networks, Naïve Bayes, random forest, K-nearest neighbor, and logistic regression and ensemble methods, are analyzed for their efficacy in hepatitis classification tasks. Key considerations such as data preprocessing, feature selection, and performance evaluation are also discussed. The review aims to provide clinicians, researchers, and healthcare stakeholders with a comprehensive understanding of ML algorithms' role in hepatitis diagnosis and improving patient outcomes.