Multi-Source Health Risk Intelligence: A Machine Learning Framework for Disease Pattern Prediction Integrating Insurance Policy Data and Environmental Factors

Disease prediction and early detection have become increasingly critical in modern healthcare systems, particularly as environmental and demographic factors continue to shape public health outcomes. Traditional approaches to health risk assessment often rely on isolated data sources, limiting their effectiveness in capturing the complex interplay of factors that influence disease patterns. A novel approach is presented for disease pattern prediction and exploration through the integration of health insurance policy data with multiple environmental, demographic, and geospatial factors. This comprehensive study examines the complex relationships between disease occurrence patterns and regional characteristics, with particular emphasis on understanding how environmental conditions, population distributions, and healthcare outcomes vary across diverse geographical settings, from metropolitan centers to rural areas. The research addresses a critical gap in current healthcare analytics by combining traditionally separate data streams into a unified analytical framework for enhanced risk assessment and pattern recognition. This paper presents a framework underpinned by unsupervised learning methods that investigates the complex web of dependencies between population demographics, environment conditions, and disease incidence rates. We model regional health risk patterns that leverage diverse data sources—health insurance claims, and policy data, population density, environmental conditions (including both air quality and industrial proximity), and healthcare facility distribution—in an integrated comprehensive model. The approach consists of a three-pillar analysis: In the first, we quantify regional disease profiles and link them to prevalent diseases, people’s tendencies, and the environment. Second, our analysis considers the geographic environmental demand variations in industrial and urban geographies. Lastly, we construct a predictive model outlining population health patterns and environmental risk factors. There are also meaningful correlations between disease patterns, environmental and population information, and regional differences in healthcare needs and utilization, nuanced geographical patterns. Environmental determinants correlate closely with certain diseases, and population density and proximity to industry significantly affect the utilization of health care resources. This results in early onset of disease pattern detection, allocation of healthcare resources on their optimized path, and model development of risk-specific to areas that highlights value to health insurance risk projections and public health planning. The proposed framework captures environmental dynamics and recognizes population-at-risk as the common denominator, and extends beyond the health surveillance framework. The results of the study offer critical guidance for insurance risk factors, healthcare resource allocation, and specific public health initiatives in regions with a high burden of environmental health hazards and corresponding pressures on healthcare systems.