Affiliations: [a] Research Groups in Intelligent Machines, University of Sfax, National School of Engineers, Sfax, Tunisia | [b] Computer Sciences and Mathematics Department, Faculty of Sciences and Techniques of SidiBouzid, University of Kairouan, Tunisia | [c] Computer Sciences and Communication Department, Faculty of Sciences of Sfax, University of Sfax, Tunisia
Correspondence:
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Corresponding author: Hamdi Ellouzi, %****␣mgs-16-mgs200329_temp.tex␣Line␣25␣**** Research Groups in Intelligent Machines, University of Sfax, National School of Engineers (ENIS), BP 1173, Sfax, 3038, Tunisia. E-mail: [email protected].
Abstract: The use of machine learning approach in the field of health informatics improves the quality and effectiveness of decision-making process. Its particular integration into the Remote Health Monitoring Systems (RHMS) allows: collecting, analyzing and learning from real-time data, automatically gaining knowledge and making predictions on the patients state. The RHMS represent an effective solution to control and monitor a growing number of dependent or elderly patients. They showed impressive results in healthcare applications. These systems are built around the sensors installed on the body of the patient and others embedded in their environments. In this context, we are confronted with the challenge of temporal aspect of data. To meet this challenge, we propose that RHMS combine machine learning for generating intelligent valuable information, and visual analytics for gaining insight the collected real-time data. The success of these applications is based on the quality of their design and development. For this reason, we propose to design a RHMS using multi-agent technology. The developed prototype was evaluated to verify our proposal feasibility.
Keywords: Remote health monitoring system, machine learning, visualization, data mining, multi-agent system
