Cloud-Based Fault Prediction for Real-Time Monitoring of Sensor Data in Hospital Environment Using Machine Learning

Mudita Uppal, Deepali Gupta, Sapna Juneja (), Adel Sulaiman, Khairan Rajab, Adel Rajab (), M. A. Elmagzoub and Asadullah Shaikh
Additional contact information
Mudita Uppal: Chitkara University Institute of Engineering and Technology, Chitkara University, Punjab 140401, India
Deepali Gupta: Chitkara University Institute of Engineering and Technology, Chitkara University, Punjab 140401, India
Sapna Juneja: KIET Group of Institutions, Delhi NCR, Ghaziabad 201206, India
Adel Sulaiman: College of Computer Science and Information Systems, Najran University, Najran 61441, Saudi Arabia
Khairan Rajab: College of Computer Science and Information Systems, Najran University, Najran 61441, Saudi Arabia
Adel Rajab: College of Computer Science and Information Systems, Najran University, Najran 61441, Saudi Arabia
M. A. Elmagzoub: College of Computer Science and Information Systems, Najran University, Najran 61441, Saudi Arabia
Asadullah Shaikh: College of Computer Science and Information Systems, Najran University, Najran 61441, Saudi Arabia

Sustainability, 2022, vol. 14, issue 18, 1-19

Abstract: The amount of data captured is expanding day by day which leads to the need for a monitoring system that helps in decision making. Current technologies such as cloud, machine learning (ML) and Internet of Things (IoT) provide a better solution for monitoring automation systems efficiently. In this paper, a prediction model that monitors real-time data of sensor nodes in a clinical environment using a machine learning algorithm is proposed. An IoT-based smart hospital environment has been developed that controls and monitors appliances over the Internet using different sensors such as current sensors, a temperature and humidity sensor, air quality sensor, ultrasonic sensor and flame sensor. The IoT-generated sensor data have three important characteristics, namely, real-time, structured and enormous amount. The main purpose of this research is to predict early faults in an IoT environment in order to ensure the integrity, accuracy, reliability and fidelity of IoT-enabled devices. The proposed fault prediction model was evaluated via decision tree, K-nearest neighbor, Gaussian naive Bayes and random forest techniques, but random forest showed the best accuracy over others on the provided dataset. The results proved that the ML techniques applied over IoT-based sensors are well efficient to monitor this hospital automation process, and random forest was considered the best with the highest accuracy of 94.25%. The proposed model could be helpful for the user to make a decision regarding the recommended solution and control unanticipated losses generated due to faults during the automation process.

Keywords: cloud; hospital environment; IoT-based sensor; machine learning; monitoring system; fault prediction; recommendations; random forest (search for similar items in EconPapers)
JEL-codes: O13 Q Q0 Q2 Q3 Q5 Q56 (search for similar items in EconPapers)
Date: 2022
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (1)

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