Predicting Diabetes through Data Analytics Enhancing Early Detection and Intervention

Umamaheswara Reddy Kudumula

Abstract: Diabetes is a prevalent chronic illness in the United States and worldwide, leading to significant health complications and economic burdens. Approximately 537 million adults globally are affected by diabetes, with numbers projected to rise dramatically. In the United States alone, around 37.3 million people live with diabetes, with annual healthcare costs exceeding $412.9 billion. Early identification and intervention are crucial to addressing [1] the impacts of diabetes. This study explores the application of predictive analytics in forecasting diabetes risk, providing an evidence-based foundation for proactive healthcare. By leveraging data-driven insights, healthcare providers can identify high-risk individuals before symptoms appear, allowing for tailored preventive measures that improve patient outcomes and reduce costs. This paper also details how predictive models can offer quantified accuracy metrics that indicate the likelihood of diabetes prevention through targeted interventions. Additionally, examine the broader impact of predictive analytics across healthcare, particularly in managing other chronic conditions, enhancing population health, and informing public health strategies.

Keywords: Predictive Analytics, Diabetes Risk Forecasting, Chronic Disease Management, Early Intervention, Healthcare Transformation