Proactive Healthcare Analytics: Early Detection of Diabetes with SDOH Insights and Machine Learning

This white paper presents a proactive healthcare analytics framework for early diabetes detection, combining Social Determinants of Health (SDOH) with machine learning. Traditional models only use clinical biomarkers, ignoring socioeconomic factors like income levels, food access and healthcare availability. By including SDOH data from CDC, County Health Rankings and USDA Food Access Atlas we improve predictive accuracy and get population level insights. Using optimized XGBoost our model has an R² of 0.88 and MAE of 0.63, beating baseline models. The study shows how healthcare analytics can move diabetes prevention from reactive to proactive and support personalized interventions and public health initiatives. We propose integration into healthcare systems via real-time APIs and predictive analytics dashboards. This research highlights the importance of SDOH aware models in addressing health disparities and informing data driven policy decisions.