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Trends in Using IoT with Machine Learning in Health Prediction System

Author

Listed:
  • Amani Aldahiri

    (Department of Cybersecurity, College of Computing Science and Engineering, University of Jeddah, Jeddah 21589, Saudi Arabia)

  • Bashair Alrashed

    (Department of Cybersecurity, College of Computing Science and Engineering, University of Jeddah, Jeddah 21589, Saudi Arabia)

  • Walayat Hussain

    (School of Information, Systems and Modelling, Faculty of Engineering and Information Technology, University of Technology Sydney, Sydney, NSW 2007, Australia)

Abstract

Machine learning (ML) is a powerful tool that delivers insights hidden in Internet of Things (IoT) data. These hybrid technologies work smartly to improve the decision-making process in different areas such as education, security, business, and the healthcare industry. ML empowers the IoT to demystify hidden patterns in bulk data for optimal prediction and recommendation systems. Healthcare has embraced IoT and ML so that automated machines make medical records, predict disease diagnoses, and, most importantly, conduct real-time monitoring of patients. Individual ML algorithms perform differently on different datasets. Due to the predictive results varying, this might impact the overall results. The variation in prediction results looms large in the clinical decision-making process. Therefore, it is essential to understand the different ML algorithms used to handle IoT data in the healthcare sector. This article highlights well-known ML algorithms for classification and prediction and demonstrates how they have been used in the healthcare sector. The aim of this paper is to present a comprehensive overview of existing ML approaches and their application in IoT medical data. In a thorough analysis, we observe that different ML prediction algorithms have various shortcomings. Depending on the type of IoT dataset, we need to choose an optimal method to predict critical healthcare data. The paper also provides some examples of IoT and machine learning to predict future healthcare system trends.

Suggested Citation

  • Amani Aldahiri & Bashair Alrashed & Walayat Hussain, 2021. "Trends in Using IoT with Machine Learning in Health Prediction System," Forecasting, MDPI, vol. 3(1), pages 1-26, March.
    • Handle: RePEc:gam:jforec:v:3:y:2021:i:1:p:12-206:d:512114
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    References listed on IDEAS

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    1. D.P. Tambuskar & B.E. Narkhede & Siba Sankar Mahapatra, 2018. "A flexible clustering approach for virtual cell formation considering real-life production factors using Kohonen self-organising map," International Journal of Industrial and Systems Engineering, Inderscience Enterprises Ltd, vol. 28(2), pages 193-215.
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    Cited by:

    1. Carolina Del-Valle-Soto & Leonardo J. Valdivia & Juan Carlos López-Pimentel & Paolo Visconti, 2023. "Comparison of Collaborative and Cooperative Schemes in Sensor Networks for Non-Invasive Monitoring of People at Home," IJERPH, MDPI, vol. 20(7), pages 1-22, March.
    2. Walayat Hussain & Asma Musabah Alkalbani & Honghao Gao, 2021. "Forecasting with Machine Learning Techniques," Forecasting, MDPI, vol. 3(4), pages 1-2, November.
    3. Hanan Butt & Muhammad Raheel Raza & Muhammad Javed Ramzan & Muhammad Junaid Ali & Muhammad Haris, 2021. "Attention-Based CNN-RNN Arabic Text Recognition from Natural Scene Images," Forecasting, MDPI, vol. 3(3), pages 1-21, July.
    4. Xingxing Zong & Lian Wang & Qingyuan Xie & Mariusz Lipowski, 2022. "The Influence of Psychological Distance on the Challenging Moral Decision Support of Sports Majors in Internet of Things and Machine Learning," Sustainability, MDPI, vol. 14(19), pages 1-14, September.

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