Enhancing Diabetes Detection through a Hybrid AI Model Utilizing Wearable Technology

Diabetes is a persistent condition that affects a significant number of individuals worldwide and can lead to severe complications if left untreated. Timely identification of diabetes can greatly reduce the risk of complications and improve overall outcomes. In recent years, the emergence of wearable technology has shown promise in disease detection and monitoring. Smartwatches equipped with IoT sensors allow for continuous and painless monitoring of vital signs, making them well-suited for diabetes screening. This study introduces a novel system that utilizes patient data to develop a hybrid AI model for detecting diabetes. The system combines vital sign measurements obtained from a smartwatch and an IoT sensor to gather accurate and continuous information about the wearer's health status. By integrating deep learning and traditional AI algorithms, the hybrid model achieves exceptional accuracy in identifying diabetes. The system collects various vital sign data, including heart rate, blood pressure, and skin conductance, which are closely associated with diabetes. After preprocessing the collected data, it is used to train the hybrid model. The deep learning algorithm extracts high-level features from the raw data, while the traditional AI algorithm classifies the data into diabetic or non-diabetic categories. This hybrid model is specifically designed to improve the precision of diabetes detection by harnessing the strengths of both deep learning and traditional AI techniques. By combining these approaches, the system aims to enhance the accuracy and effectiveness of diabetes diagnosis, ultimately benefiting individuals at risk of this condition.\