A decision support healthcare system based on IoT and neural network technique

Abstract

Purpose

The purpose of this paper is to develop e-health and patient monitoring systems remotely to overcome the difficulty of patients going to hospitals especially in times of epidemics such as virus disease (COVID-19). Artificial intelligence (AI) technology will be combined Internet of Things (IoT) in this research to overcome these challenges. The research aims to select the most appropriate, best-hidden layers numbers and the activation function types for the neural network (NN). Then, define the patient data sent through protocols of the IoT. NN checks the patient’s medical sensors data to make the appropriate decision. Then it sends this diagnosis to the doctor. Using the proposed solution, the patients can diagnose and expect the disease automatically and help physicians to discover and analyze the disease remotely without the need for patients to go to the hospital.

Design/methodology/approach

AI technology will be combined with the IoT in this research. The research aims to select the most appropriate’ best-hidden layers numbers’ and the activation function types for the NN.

Findings

Decision support health-care system based on IoT and deep learning techniques was proposed. The authors checked out the ability to integrate the deep learning technique in the automatic diagnosis and IoT abilities for speeding message communication over the internet has been investigated in the proposed system. The authors have chosen the appropriate structure of the NN (best-hidden layers numbers and the activation function types) to build the e-health system is performed in this work. Also, depended on the data from expert physicians to learn the NN in the e-health system. In the verification mode, the overall evaluation of the proposed diagnosis health-care system gives reliability under different patient’s conditions. From evaluation and simulation results, it is clear that the double hidden layer of feed-forward NN and its neurons contain Tanh function preferable than other NN.

Originality/value

AI technology will be combined IoT in this research to overcome challenges. The research aims to select the most appropriate, best-hidden layers numbers and the activation function types for the NN.

Citation

Fahmy, K.A., Yahya, A. and Zorkany, M. (2022), "A decision support healthcare system based on IoT and neural network technique", Journal of Engineering, Design and Technology, Vol. 20 No. 3, pp. 727-748. https://doi.org/10.1108/JEDT-08-2020-0317