International Journal for Innovative Engineering and Management Research

Liver disease is one of the leading causes of disease-related mortality worldwide, with survival rates heavily dependent on early detection and timely treatment.This project presents a Flaskbased web application integrated with a machine learning model designed for early liver disease detection.The system employs a Random Forest Classifier trained on diverse patient health parameters, including liver function test results, demographic details, and clinical indicators, to assess an individual's risk of developing liver disease.The machine learning model, stored as Liver2.pkl,leverages robust decision-tree-based ensemble learning techniques to achieve high classification accuracy and predictive performance.Users can input their health parameters through a user-friendly web interface, which then provides a real-time risk assessment.Additionally, the application supports interactive features such as secure Data set uploads for retraining the model, visualization of training metrics, and comprehensive result dashboards.The integration of advanced machine learning techniques within a simple and accessible web application ensures broader adoption among medical practitioners and researchers.This project contributes to the ongoing efforts to enhance liver disease screening methods and highlights the potential of AI-driven healthcare solutions for early disease detection and prevention.