GRAPH-THEORETIC APPROACHES TO PERSONALIZED HEALTH NETWORKS

The emergence of precision health has transformed the landscape of preventive care by emphasizing individualized risk assessment and targeted interventions. This paper explores the application of graph-theoretic models to construct and analyze personalized health networks, where nodes represent health determinants—such as genetic markers, behavioral traits, environmental exposures, and clinical metrics—and edges encode relationships, dependencies, or influence pathways among them. We propose a framework that leverages centrality measures, community detection algorithms, to identify critical health factors, predict disease onset, and optimize intervention strategies. This graph-theoretic perspective not only enhances the predictive power of precision health models but also supports scalable, data-driven strategies for population-level wellness planning. The findings underscore the potential of network science to bridge the gap between individualized care and public health optimization.