Advanced Predictive Models for Proactive Liver Disease Detection and Future Health Optimization

Chronic liver illnesses like cirrhosis and hepatitis threaten world health. Early prediction helps improve treatment outcomes and prevent liver failure or cancer. This research presents the concept of early diagnosis of TabNet liver disease. TabNet predicts liver disease development accurately and reliably, outperforming Random Forest and Logistic Regression models for optimisation. Real-time analysis gives healthcare practitioners relevant insights for early intervention and personalised treatment. Key features include real-time alerts and predictions from continuous patient data monitoring. The technology alerts immediately of any irregularities or hazards, ensuring timely intervention for chronic liver disorders or preventative treatment. The easy, adaptive user interface allows dynamic model confidence threshold change. The solution also allows batch data analysis for retrospective evaluation. This hybrid machine learning method compares model confidence scores to optimise and strengthen the answer. Its flexibility allows it to be used in patient monitoring and risk prediction while preserving accuracy and interpretability.