Background: The main challenge of companies is to fulfill services and meet customer expectations. Quality Function Deployment (QFD) helps to extract service features from customer requirements. Objective: Due to the use of the Internet of Things in telemedicine, this r
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Background: The main challenge of companies is to fulfill services and meet customer expectations. Quality Function Deployment (QFD) helps to extract service features from customer requirements. Objective: Due to the use of the Internet of Things in telemedicine, this research has two objectives, the first purpose is to identify the parameters affecting the integration of IoT and telemedicine systems with the view of improving quality performance and the second purpose is to investigate the possibility of using artificial neural networks to predict the behavior of the QFD matrix. Methods: For the first purpose, value-oriented engineering analysis method and customer-oriented Function Analysis System Technique (FAST) diagram were used, and after obtaining the parameters, including customer call, service characteristics and operation requirements, for the second purpose, Fuzzy Back-Propagation Network was used. Findings: After obtaining identify the parameters affecting the integration of IoT and telemedicine systems with the view of improving quality performance, a total of 50 QFD matrices were collected and their data were introduced in a Fuzzy Back-Propagation Network. 80% of this amount has been used for network training, 15% for testing and 5% for validation of predicted results. The mean square error (MSE) for the validation section is MSE = 0.015584. Conclusion: the results showed that the Fuzzy Back-Propagation Network method is suitable for predicting the expansion of QFD in the integration of IoT and telemedicine systems.
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