Hepatitis Disease Prediction Using Machine Learning Models

This research uses the Decision Tree and Naïve Bayes algorithms to analyze patient data to create a machine learning model that predicts hepatitis illness. The model classifies illness risk by evaluating characteristics such as liver function, enzyme levels, and other health indicators. These algorithms produce probabilistic results that are helpful in early diagnosis using a labeled dataset, enabling medical professionals an invaluable resource for determining hepatitis risk. This prediction model's simplicity and high accuracy make it a promising candidate for incorporation into health management systems, which could help with prompt diagnosis and enhance hepatology patient outcomes.