Leveraging Machine Learning Techniques for Accurate Hepatitis prediction

Hepatitis is a liver disease caused by various factors, including viral infections, alcohol consumption, and autoimmune responses. Timely detection and treatment of hepatitis are critical for preventing liver damage and improving patient outcomes. In this research paper, we explore the development of an optimized machine learning (ML) model for predicting the presence and progression of hepatitis. We evaluate various algorithms, feature selection techniques, and optimization strategies to enhance the accuracy and efficiency of prediction. Our approach incorporates a data-driven analysis using publicly available datasets, providing a robust framework for hepatitis prediction. The paper discusses the comparative performance of models like Decision Trees, Random Forest, Support Vector Machines (SVM), and Neural Networks, alongside feature engineering techniques. We propose an optimized hybrid model that outperforms traditional models, offering significant improvements in both prediction accuracy and computational efficiency. This research aims to contribute to the advancement of early detection systems for hepatitis, which could serve as valuable tools in clinical decision-making.