Fuzzy-assisted machine learning framework for the fog-computing system in remote healthcare monitoring

Health care monitoring systems have mainly depended on the internet of things (IoT) devices to collect, manage and analyze data from patients. Patients' health can be constantly monitored and controlled with the remote health monitoring systems. From these points, a Fuzzy-assisted machine learning framework (F-AMLF) with fog computing increases the effectiveness of the health care monitoring system. This paper presents an F-AMLF to recognize how device resource cost reduction is achieved while maintaining efficiency limitations. Patients can submit their demands for health care by a fuzzy assisted fog computing system. These systems use fuzzy logic to calculate how much computing capacity is needed to maintain fog and cloud projections. The F-AMLF shows the highest accuracy ratio of 93.6%, monitoring ratio of 92.5%, prediction ratio of 95.3%, data management ratio of 91.4%, and the lowest latency ratio of 19.7%, energy consumption ratio of 20.1%, and the cost-effective ratio of 21.5% compared to the existing methods.

Automated summary

This paper presents an F-AMLF that combines fuzzy-assisted machine learning framework with fog computing to improve the effectiveness of health care monitoring systems. By using fuzzy logic to calculate the required computing capacity, device resource cost reduction is achieved while maintaining efficiency, enabling accurate monitoring and prediction of health data.