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

Abstract

© Springer Nature Switzerland AG 2020. 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.