Key result
The sequential minimal optimization (SMO) algorithm demonstrated superior classification accuracy, sensitivity, and precision compared to other machine learning algorithms for diabetic patient monitoring.
Why the study?
Continuous monitoring of diabetic patients improves quality of life and reduces healthcare costs, creating a need to develop intelligent e-health architectures supported by 5G and machine learning.
An intelligent IoT and machine learning-based architecture utilizing the SMO algorithm provides high accuracy for remote monitoring and diagnosis of diabetic patients.
Answers are grounded in this paper and cite their sources.
Warrants prospective validation before clinical use; leaves open whether accuracy gains improve diabetic outcomes.
Continuous monitoring of diabetic patients improves their quality of life. The use of multiple technologies such as the Internet of Things (IoT), embedded systems, communication technologies, artificial intelligence, and smart devices can reduce the economic costs of the healthcare system. Different communication technologies have made it possible to provide personalized and remote health services. In order to respond to the needs of future intelligent e-health applications, we are called to develop intelligent healthcare systems and expand the number of applications connected to the network. Therefore, the 5G network should support intelligent healthcare applications, to meet some important requirements such as high bandwidth and high energy efficiency. This article presents an intelligent architecture for monitoring diabetic patients by using machine learning algorithms. The architecture elements included smart devices, sensors, and smartphones to collect measurements from the body. The intelligent system collected the data received from the patient, and performed data classification using machine learning in order to make a diagnosis. The proposed prediction system was evaluated by several machine learning algorithms, and the simulation results demonstrated that the sequential minimal optimization (SMO) algorithm gives superior classification accuracy, sensitivity, and precision compared to other algorithms.
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Rghioui et al. (2020) studied Diabetes. Sequential minimal optimization (SMO) algorithm vs. Other machine learning algorithms was evaluated on Classification accuracy, sensitivity, and precision. The sequential minimal optimization (SMO) algorithm demonstrated superior classification accuracy, sensitivity, and precision compared to other machine learning algorithms for diabetic patient monitoring.
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