Prospects of machine and deep learning in analysis of vital signs for the improvement of healthcare services

Mohamed Alloghani; Thar Baker; Dhiya Al-Jumeily; Abir Hussain; Jamila Mustafina; Ahmed J. Aljaaf

doi:10.1007/978-3-030-28553-1_6

Prospects of machine and deep learning in analysis of vital signs for the improvement of healthcare services

Mohamed Alloghani, Thar Baker, Dhiya Al-Jumeily, Abir Hussain, Jamila Mustafina, Ahmed J. Aljaaf

School of Arch, Tech and Eng

Research output: Chapter in Book/Conference proceeding with ISSN or ISBN › Chapter › peer-review

Abstract

The advent of eHealth and the need for real-time patient monitoring and assessment has prompted interest in understanding people behavior for improving care services. In this paper, the application of machine learning algorithms in clustering and predicting vital signs was pursued. In the context of big data and the debate surrounding vital signs data is fast becoming more relevant and applicable in predictive medicine. This paper assesses the applicability of k-Means and x-Means in clustering signals and used deep learning, Naïve Bayes, Random Forests, Decision Trees, and Generalized Linear Models to predict human dynamic motion-based vital signal patterns.

Original language	English
Title of host publication	Studies in Computational Intelligence
Publisher	Springer-Verlag
Pages	113-136
Number of pages	24
DOIs	https://doi.org/10.1007/978-3-030-28553-1_6
Publication status	Published - 4 Sept 2019

Publication series

Name	Studies in Computational Intelligence
Volume	855
ISSN (Print)	1860-949X
ISSN (Electronic)	1860-9503

Bibliographical note

Funding Information:
We are grateful to the UCI team for granting access to the data used in the study. We acknowledge and appreciate the Oresti Banos, Rafael Garcia, and Alejandro Saez of the Department of Computer Architecture and Computer Technology, University of Granada for collecting and sharing the data with UCI.

Publisher Copyright:
© Springer Nature Switzerland AG 2020.

Keywords

Cognitive hypervisor
Deep learning
Machine learning
Vital signals

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10.1007/978-3-030-28553-1_6

Cite this

Alloghani, M., Baker, T., Al-Jumeily, D., Hussain, A., Mustafina, J., & Aljaaf, A. J. (2019). Prospects of machine and deep learning in analysis of vital signs for the improvement of healthcare services. In Studies in Computational Intelligence (pp. 113-136). (Studies in Computational Intelligence; Vol. 855). Springer-Verlag. https://doi.org/10.1007/978-3-030-28553-1_6

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Prospects of machine and deep learning in analysis of vital signs for the improvement of healthcare services. / Alloghani, Mohamed; Baker, Thar; Al-Jumeily, Dhiya et al.
Studies in Computational Intelligence. Springer-Verlag, 2019. p. 113-136 (Studies in Computational Intelligence; Vol. 855).

Research output: Chapter in Book/Conference proceeding with ISSN or ISBN › Chapter › peer-review

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Prospects of machine and deep learning in analysis of vital signs for the improvement of healthcare services

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Bibliographical note

Keywords

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