Liver Disease Detection System Using Random Forest Algorithm

Abstract

Liver diseases are a major global health concern, often progressing silently until advanced stages that require critical intervention. However, traditional diagnostic procedures can be costly, invasive, and time-consuming, limiting early detection and effective management. The Liver Disease Prediction System addresses this challenge by offering an AI-powered mobile application that predicts the risk of liver disease based on clinical data. Utilizing the Random Forest algorithm, the system analyzes user-inputted medical data to deliver fast and accurate predictions. It further enhances patient care through automated report generation, personalized health recommendations, and integrated telemedicine consultations. The application is developed using modern technologies such as Flutter, Firebase, and Python, ensuring secure data handling and cross-platform accessibility. Comprehensive testing, including black-box and white-box methods, confirms system reliability and usability. Feedback from medical professionals was incorporated to refine functionality and user experience. The system enables early liver disease screening, improves patient-provider interaction, and promotes proactive liver health management.