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

Author

Listed:
  • 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)

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.

Suggested Citation

  • Mudita Uppal & Deepali Gupta & Sapna Juneja & Adel Sulaiman & Khairan Rajab & Adel Rajab & M. A. Elmagzoub & Asadullah Shaikh, 2022. "Cloud-Based Fault Prediction for Real-Time Monitoring of Sensor Data in Hospital Environment Using Machine Learning," Sustainability, MDPI, vol. 14(18), pages 1-19, September.
  • Handle: RePEc:gam:jsusta:v:14:y:2022:i:18:p:11667-:d:917187
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    References listed on IDEAS

    as
    1. Kanwalpreet Kour & Deepali Gupta & Kamali Gupta & Sapna Juneja & Manjit Kaur & Amal H. Alharbi & Heung-No Lee, 2022. "Controlling Agronomic Variables of Saffron Crop Using IoT for Sustainable Agriculture," Sustainability, MDPI, vol. 14(9), pages 1-16, May.
    2. Chetna Monga & Deepali Gupta & Devendra Prasad & Sapna Juneja & Ghulam Muhammad & Zulfiqar Ali, 2022. "Sustainable Network by Enhancing Attribute-Based Selection Mechanism Using Lagrange Interpolation," Sustainability, MDPI, vol. 14(10), pages 1-15, May.
    3. Prettner, Klaus, 2019. "A Note On The Implications Of Automation For Economic Growth And The Labor Share," Macroeconomic Dynamics, Cambridge University Press, vol. 23(3), pages 1294-1301, April.
    4. Daron Acemoglu & Pascual Restrepo, 2017. "Secular Stagnation? The Effect of Aging on Economic Growth in the Age of Automation," American Economic Review, American Economic Association, vol. 107(5), pages 174-179, May.
    5. Hamid Mukhtar & Saeed Rubaiee & Moez Krichen & Roobaea Alroobaea, 2021. "An IoT Framework for Screening of COVID-19 Using Real-Time Data from Wearable Sensors," IJERPH, MDPI, vol. 18(8), pages 1-17, April.
    6. Kanwalpreet Kour & Deepali Gupta & Kamali Gupta & Gaurav Dhiman & Sapna Juneja & Wattana Viriyasitavat & Hamidreza Mohafez & Mohammad Aminul Islam, 2022. "Smart-Hydroponic-Based Framework for Saffron Cultivation: A Precision Smart Agriculture Perspective," Sustainability, MDPI, vol. 14(3), pages 1-19, January.
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    1. Mudita Uppal & Deepali Gupta & Amena Mahmoud & M. A. Elmagzoub & Adel Sulaiman & Mana Saleh Al Reshan & Asadullah Shaikh & Sapna Juneja, 2023. "Fault Prediction Recommender Model for IoT Enabled Sensors Based Workplace," Sustainability, MDPI, vol. 15(2), pages 1-21, January.

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