Early Prediction of Liver Disease

Chronic liver disease (CLD) poses a substantial global health challenge, leading to considerable morbidity and mortality. The timely identification of CLD is imperative to enhance patient outcomes and ensure effective disease management. This research introduces an innovative machine learning framework designed to predict liver disease at an early stage, utilizing a wide range of clinical data resources. By integrating demographic information, laboratory test results, imaging findings, and patient medical history, our model aims to accurately forecast the onset and progression of CLD. Advanced classification algorithms, including Random Forest and Gradient Boosting, are employed for feature selection and model development. Performance evaluation is conducted on a comprehensive dataset comprising longitudinal patient records. The results demonstrate promising accuracy, sensitivity, and specificity, highlighting the potential of machine learning in enhancing CLD risk assessment and enabling timely interventions.