Prediction of Liver Cirrhosis and Analysis

Liver cirrhosis, caused by diseases like hepatitis and chronic alcoholism, results in scarring that impairs liver function. Each injury forms scar tissue, leading to a nodular, uneven liver surface. We propose an AI-driven prediction model which makes use of Ensemble Techniques to detect liver cirrhosis early. The Ensemble includes Gradient Boost, XGBoost and Random Forest models. Our system allows patients to upload medical reports or enter data manually through a user-friendly platform. The model analyses this data, predicts cirrhosis risk, and provides visualizations for monitoring. This approach aims to improve early detection and timely intervention, reducing healthcare costs and enhancing patient outcomes by preventing disease progression.