<![CDATA[Background of Study: Hepatitis is a significant viral infection causing liver inflammation, potentially leading to hepatocyte death and impaired liver function. Types B (HBV) and C (HCV) can cause chronic hepatitis, cirrhosis, and cancer. Globally, around 257 million people are infected with HBV and 71 million with HCV. Early detection of chronic Hepatitis B is crucial for effective management.Aims and Scope of Paper: This study aims to predict hepatitis progression in patients from their medical histories. It seeks to enhance prediction accuracy by addressing challenges like noise and inefficiency caused by similar aspect values and distributions within datasets.Methods: Machine learning, a branch of AI, is employed for chronic disease prediction. The study primarily utilizes the K-Nearest Neighbour (KNN) algorithm to predict and eliminate redundant data and noise. Other models evaluated include Logistic Regression, Random Forest, and Convolutional Neural Networks (CNN), with SMOTE used for dataset balancing.Result: KNN achieved 0.970 accuracy, Logistic Regression 0.966, and Random Forest 0.95. The CNN model demonstrated exceptional performance, reaching 1.0 accuracy with perfect precision, recall, and F1-score for Hepatitis A and B.Conclusion: While KNN performed well among traditional methods, deep learning models like CNN show superior accuracy and generalizability, offering a robust framework for hepatitis prediction.]]>
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