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

Research output: Chapter in Book/Conference proceeding with ISSN or ISBNChapterpeer-review


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 languageEnglish
Title of host publicationStudies in Computational Intelligence
Number of pages24
Publication statusPublished - 4 Sept 2019

Publication series

NameStudies in Computational Intelligence
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.


  • Cognitive hypervisor
  • Deep learning
  • Machine learning
  • Vital signals


Dive into the research topics of 'Prospects of machine and deep learning in analysis of vital signs for the improvement of healthcare services'. Together they form a unique fingerprint.

Cite this