Advancing Liver Disease Prediction with Multi-Modal Graph Neural Networks and Federated Meta-Learning

Accurate prediction of liver diseases such as fatty liver, cirrhosis, and liver cancer remains a critical challenge in modern healthcare due to increasing prevalence and limitations in existing diagnostic methods. Current approaches often rely on single data modalities, lack interpretability, and compromise data privacy through centralized models, limiting their scalability and applicability in real-world clinical settings, especially in sparse and noisy data environments. This research aims to address these challenges through a multi-objective framework encompassing advanced machine-learning techniques. The first objective focuses on developing a clinical imaging-genetic data fusion model using self-supervised graph neural networks to manage sparse datasets, achieving accuracy gains of 92–94% and improving AUC-ROC scores by 5–8% over baseline models. The second objective introduces the Federated Meta-Learning Framework for Liver Disease Prediction (FML-LDP), enabling privacy-preserving, collaborative model training across institutions. This framework achieves precision levels of 88–91%, reduces computational overhead by 15–20%, and ensures adaptability to diverse patient scenarios. The third objective addresses the need for interpretability through the Explainable Deep Learning Framework with Reinforcement Learning Optimization (EDL-RL), which dynamically selects optimal features using reinforcement learning. This framework enhances interpretability by 25–30%, integrates Shapley-value-based feature explanations, and maintains high predictive accuracy (90–93%) to improve clinical trust.By integrating multi-modal data learning, privacy-preserving collaboration, and interpretable AI, this work provides a robust and scalable solution for liver disease prediction, setting a new benchmark for clinical decision support systems.