Converging Digital Governance and Health Analytics: Predictive Models for Smarter Policy in Virtual Spaces

Abstract

The convergence of immersive technologies and artificial intelligence has catalyzed the evolution of digital health ecosystems. This paper explores how predictive health analytics, powered by machine learning, can be effectively interwoven into metaverse governance structures to enable smarter, more anticipatory public health policy-making. The study is grounded in the urgent need for real-time, personalized health surveillance and data-driven policy responses in the face of rising chronic diseases and dynamic population health patterns. We propose and evaluate a multilayered framework that leverages spatial data governance within the metaverse, wearable health technologies, and predictive machine learning models to inform policy decisions in real time. The approach integrates gradient boosting machines (GBM), recurrent neural networks (RNN), and ensemble classifiers trained on multimodal datasets, wearable sensor streams, electronic health records, and virtual health interactions within metaverse environments. Evaluation metrics include F1-score, AUC-ROC, and policy relevance indices that measure the timeliness and accuracy of recommended interventions. The models demonstrated high precision in forecasting early-onset conditions such as esophageal cancer and predicting hospital readmissions, with an AUC averaging 0.91 across all tasks. Furthermore, real-time policy simulations in metaverse-integrated dashboards enabled stakeholders to visualize the population-level impact of different intervention scenarios, including mortality reduction and hospital burden alleviation. The results underscore the potential of integrating predictive health analytics with immersive governance architectures for real-time decision intelligence. The findings advocate for a paradigm shift in public health policy, from reactive to predictive, facilitated by AI-driven digital ecosystems. This paper offers both a technical and conceptual foundation for future research and institutional adoption.