A Robust Machine Learning Model for Predicting Hepatitis Types in a Multi-Class Setting

Abstract

This research introduces a comprehensive machine learning-driven framework tailored to accurately identify and categorize diverse forms of Hepatitis while also delivering predictive insights into patient prognosis. Hepatitis, a significant global health challenge, demands early and precise diagnostic tools to improve treatment outcomes and reduce the burden on healthcare systems. Recognizing this need, the study leverages advanced machine learning algorithms, including Support Vector Machines (SVM), K-Nearest Neighbors (KNN), and Artificial Neural Networks (ANN). Each of these algorithms was selected for its proven strengths in addressing classification tasks and handling complex medical datasets. The framework evaluates these techniques rigorously, assessing their performance through critical metrics such as accuracy, mean square error, recall, precision, and F1-score. This detailed analysis ensures that the most suitable model is identified for the task. The aim is to provide a tool that not only meets but exceeds the current standards for Hepatitis diagnosis, enabling timely and effective clinical decision-making. The algorithms' comparative analysis serves to pinpoint each approach's strengths and limitations, laying the groundwork for an optimized and robust diagnostic solution. One of the distinguishing features of this system is its ability to address the gaps present in existing models, particularly in the accurate classification of Hepatitis types in a multi-class setting. Many traditional approaches either fail to capture the nuances of various Hepatitis subtypes or are constrained by their reliance on small datasets and binary classifications. This research aspires to achieve a new benchmark in diagnostic precision by overcoming these limitations.