Diabetes Analytics and Recommendation Engine (DARE)

Diabetes is a chronic disease affecting over 415 million people worldwide. Effectively managing glucose levels on a daily routine is crucial to maintaining a healthy and threat-free lifestyle. In this paper, we propose the Diabetes Analytic and Recommendation Engine (DARE) Architecture to harness personal technologies in assisting type two diabetic patients to manage their glucose levels through a rule-based system coupled with anomaly detection and threat forecasting in a context-driven environment. To this end, the proposed DARE Architecture takes a modular approach in applying machine learning techniques to predict glucose levels and provide context-driven recommendations effectively.