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IoT-Based In-Hospital-In-Home Heart Disease Remote Monitoring System with Machine Learning Features for Decision Making

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Connected e-Health

Part of the book series: Studies in Computational Intelligence ((SCI,volume 1021))

Abstract

Advancement of new technologies, such as the Internet of Things (IoT), improves healthcare quality by personalizing it, lowering costs, reducing medical mistakes, improving patient safety, and saving time for crucial circumstances. Medical decision-making is transformed by IoT-based technologies, which provide services such as transferring medial data/biomedical signals, patient tracking and remote monitoring, secure access to medical data, and rapid emergency response. Traditional healthcare management is prone to biases and mistakes, which can have an impact on the quality of care delivered to patients. The scenario is generally caused by a clinical judgement made by doctors based on their intuition and expertise. Furthermore, the traditional human decision-making process might result in inaccurate descriptions. Therefore, this chapter focuses on the development of an IoT-based heart disease remote monitoring system called Remoteheart. The system utilizes IoT devices to measure patient biomedical data such as blood pressure, Oxygen, ECG, PPG, and Heart rate. The data is transferred to hospital information management system from time to time and analyzed using machine learning techniques. A decision tree is created and presented to healthcare professionals for better decision making about patient situation and treatment. RemoteHeart is successfully developed and evaluated by 24 users. This chapter can assist healthcare practitioners in implementing suitable IoT-based healthcare remote monitoring systems to improve medical information system decision-making.

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Acknowledgements

The authors are thankful to School of Computer Sciences, Universiti Sains Malaysia (USM) for unlimited supports. In addition, she is grateful to Division of Research and Innovation (RCMO), USM for providing financial support from Short Term Grant (304/PKOMP/6315435).

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Authors and Affiliations

  1. School of Computer Sciences, Universiti Sains Malaysia, 11800, Minden, Penang, Malaysia

    Pantea Keikhosrokiani & Nor Saralyna Azwa Binti Kamaruddin

Authors
  1. Pantea Keikhosrokiani
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  2. Nor Saralyna Azwa Binti Kamaruddin
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Corresponding author

Correspondence to Pantea Keikhosrokiani .

Editor information

Editors and Affiliations

  1. School of Computer Engineering, Kalinga Institute of Industrial Technology, Bhubaneswar, Odisha, India

    Sushruta Mishra

  2. Grupo de Investigación BISITE, Departamento de Informática y Automática, Facultad de Ciencias, University of Salamanca, Instituto de Investigación Biomédica de Salamanca, Salamanca, Spain

    Alfonso González-Briones

  3. KIET Group of Institutions, Ghaziabad, Delhi-NCR, India

    Akash Kumar Bhoi

  4. School of Computer Engineering, Kalinga Institute of Industrial Technology, Bhubaneswar, Odisha, India

    Pradeep Kumar Mallick

  5. Grupo de Investigación BISITE, Departamento de Informática y Automática, Facultad de Ciencias, University of Salamanca, Instituto de Investigación Biomédica de Salamanca, Salamanca, Spain

    Juan M. Corchado

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Keikhosrokiani, P., Kamaruddin, N.S.A.B. (2022). IoT-Based In-Hospital-In-Home Heart Disease Remote Monitoring System with Machine Learning Features for Decision Making. In: Mishra, S., González-Briones, A., Bhoi, A.K., Mallick, P.K., Corchado, J.M. (eds) Connected e-Health. Studies in Computational Intelligence, vol 1021. Springer, Cham. https://doi.org/10.1007/978-3-030-97929-4_16

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