Integration of 5G and Block-Chain Technologies in Smart Telemedicine Using IoT

Abstract

The Internet of Health Thing (IoHT) has various applications in healthcare. Modern IoHTintegrates health-related things like sensors and remotely observed medical devices for the assessment and managment of a patient's record to provide smarter and efficient health diagnostics to the patient. In this paper, we proposed an IoT with a cloud-based clinical decision support system for prediction and observation of disease with its severity level with the integration of 5G services and block-chain technologies. A block-chain is a system for storing and sharing information that is secure because of its transparency. Block-chain has many applications in healthcare and can improve mobile health applications, monitoring devices, sharing and storing of the electronic media records, clinical trial data, and insurance information storage. The proposed framework will collect the data of patients through medical devices that will be attached to the patient, and these data will be stored in a cloud server with relevant medical records. Deployment of Block-chain and 5G technology allows for sending patient data securely at a fast transmission rate with efficient response time. Furthermore, a Neural Network (NN) classifier is used for the prediction of diseases and their severity level. The proposed model is validated by employing different classifiers. The performance of different classifiers is measured by comparing the values to select the classifier that is the best for the dataset. The NN classifier attains an accuracy of 98.98. Furthermore, the NN is trained for the dataset so that it can predict the result of the dataset class that is not labeled. The trained Neural Network predicts and intelligently shows the results with more accuracy than other classifiers.

Figure 1

Store and forward method of telemedicine.

Figure 2

The trend of Telemedicine Services.

Figure 3

System architecture.

Figure 4

Sensor-based data from humans.

Figure 5

Processing of sensor data.

Figure 6

Arduino UNO microcontroller.

Figure 7

Pulse rate sensor.

Figure 8

: Blood-pressure sensor.

Figure 9

Body temperature sensor.

Figure 10

MAX 30100 oximeter sensor.

Figure 11

Block-chain and 5G technologies in healthcare.

Figure 12

Components of the CDSS

Figure 13

Architecture of the CDSS

Figure 14

Neural network architecture.

Figure 15

Plot of the rectifier near x, y = 0.

Figure 16

Import the training dataset.

Figure 17

Comparison of classification results in terms of FPR and FNR.

Figure 18

Comparison of the classifier result in terms of various measures.

Figure 19

Comparative sensitivity and specificity of the NN with other classifiers.

Figure 20

Comparative accuracy of the NN with other Classifiers.