Intelligent Health Surveillance: Cognitive IoT Enhanced by Rapid Machine Learning

Abstract

Abstract: The quality of life for diabetic patients is significantly improved through continuous monitoring. Integrating various technologies such as the Internet of Things (IoT), embedded software, communication technologies, artificial intelligence, and smart devices helps reduce the financial burden on the healthcare system. Advances in communication technologies have facilitated personalized and remote healthcare. To meet the growing demand for advanced e-health applications, developing intelligent healthcare systems and increasing the number of applications connected to the network is essential. Consequently, to achieve critical needs like high bandwidth and energy efficiency, the 5G network must support smart healthcare applications. This research proposes an intelligent infrastructure for monitoring diabetes patients using machine learning methods. The architecture incorporates smart devices, sensors, and mobile phones to provide comprehensive coverage of the patient's body. Data collected from the patient is analyzed and classified using machine learning to produce a diagnosis. Several machine learning methods were tested to evaluate the proposed prediction system, and simulation results indicated that the Sequential Minimum Optimization (SMO) method provides higher classification accuracy, sensitivity, and precision than other techniques.