Enhanced Liver Disease Prediction Using Ensemble Learning

In the healthcare field, accurate prediction of liver disease plays a critical role in diagnosis and treatment planning. This study aims to improve liver disease prediction using machine learning techniques. Through analyzing individual patient data, the objective is to enhance diagnostic methods. The models consider various factors like patient demographics and clinical features to ensure precise predictions of liver disease. Algorithms such as LightGBM, AdaBoost, CatBoost and XGboost are employed. To simplify the model for users, it is integrated into a web application using the Flask framework. This enables users to input clinical features and receive predictions about their liver disease status. Challenges remain in data integration, model interpretability and clinical validation, despite these advancements. Future research will concentrate on developing strong machine learning models validated across diverse patient populations, facilitating personalized therapy for liver disease through real-time data integration. Overall, machine learning-based methods coupled with a user-friendly web interface have the potential to significantly advance liver disease diagnosis and enhance patient outcomes in clinical settings.