Diabetes Management Using Machine Learning and Communication Protocol Technology

Ad hoc protocols like AODV, DSDV, and OLSR have been studied in healthcare, but their application to CGM networks with real-time integrated Machine Learning (ML) presents unique challenges due to distinct traffic patterns and node configurations. This paper explores how CGM, ML, and communication protocols jointly enable continuous monitoring and timely intervention, where transmission reliability directly affects ML performance in anomaly detection. Using NS3, we evaluate AODV, DSDV, and OLSR based on throughput, delay, and packet delivery ratio (PDR) under varying network sizes and data rates. Results show AODV offers minimal delay and packet loss, enhancing ML accuracy, while DSDV and OLSR perform better in dense networks. We also identify challenges related to scalability and data security, highlighting directions for future optimization of CGM-ML systems through hybrid and energyefficient protocols.