A Diabetes Monitoring System and Health-Medical Service Composition Model in Cloud Environment

Patients with diabetes who are continuously monitored have better lives. Utilizing a variety of technologies, including smart devices, deep learning, computer vision and automated learning can reduce the cost burden on the healthcare system. Health care can now be provided remotely and with personalization thanks to various communication technologies. The risk of acquiring diabetes and its complications, like cardiovascular disease, can be decreased by engaging in physical exercise, maintaining a healthy weight, and following a nutritious diet. Long-term health conditions and their aftereffects can be avoided or postponed with a healthy lifestyle; however few people adhere to all suggested self-care practices. For precise diabetes prediction and proactive care, this paper suggests integrating a machine learning-driven strategy into a cloud-based health service. Strong predictive models are constructed by the system by utilizing a variety of datasets, including lifestyle characteristics, medical records, and genetic predispositions. An analysis of these datasets is done by machine learning methods. In addition, real-time monitoring and customized advice for those at risk of diabetes are made possible by the incorporation of these predictive models into a cloud-based health care architecture.