Deep Learning Diabetes Monitoring and Prevention

This chapter introduces the Personalized Health Assistant, an integrated system that combines Internet of Things devices, machine learning, and natural language processing to monitor diabetes and promote proactive, personalized healthcare. For diabetic patients, the system leverages wearable sensors data. For non-diabetic users, physiological parameters are inferred through lifestyle and stress indicators. To support predictive health monitoring, the system incorporates advanced machine learning models: Long Short-Term Memory, Multiple Back-Propagation, and a Convolutional Neural Network. Another component of the system is a clinically aware chatbot, built upon the Falcon-7B large language model and semantically validated using Bio_ClinicalBERT. The chatbot pipeline integrates FAISS-based context retrieval, clinical term detection, and semantic similarity scoring. The validation results underscore the system's reliability for predictive analytics, intelligent interaction, and real-time clinical support, making it a viable solution for scalable and secure chronic disease management.