LivXAI-Net: An explainable AI framework for liver disease diagnosis with IoT-based real-time monitoring support.

BACKGROUND & OBJECTIVE: Liver disease remains a significant global health burden, often progressing silently until advanced stages such as cirrhosis or hepatic failure. Early detection is essential but remains hindered by the limitations of conventional diagnostics. This study presents LivXAI-Net, an explainable artificial intelligence (XAI) framework integrated with Internet of Things (IoT) biosensors, designed and evaluated in a simulated real-time setting using a historical dataset. METHODS: LivXAI-Net simulates continuous data acquisition from wearable biosensors - including sweat, platelet, and prothrombin sensors - and processes this data using machine learning (ML) models trained on the Mayo Clinic primary biliary cirrhosis (PBC) dataset (n = 424, 1974-1984). Random Forest (RF) and XGBoost(XGB) classifiers were deployed with SHAP and Permutation Feature Importance (PFI) to enhance interpretability. A mobile application, Hepatic Health Tracker, delivers real-time risk predictions, supported by a secure data pipeline using TLS 1.3 and AES-256 encryption. RESULTS: RF and XGB achieved accuracies of 84% and 82% respectively under 20-fold cross-validation. Key biomarkers - albumin, cholesterol, and triglycerides - were consistently identified by SHAP as influential in classification. The system achieved a total latency of 0.85 s in a simulated 5G environment, supporting near-instantaneous alert delivery via the mobile interface. CONCLUSION: LivXAI-Net combines interpretable ML with real-time biosensor data to enable proactive liver disease management. While currently validated using historical data in a simulated environment, future work will involve deployment with live sensor input and clinical trials to validate utility and generalizability in real-world settings.