Evaluating machine and deep learning techniques in predicting blood sugar levels within the E-health domain

Abstract

This paper focuses on exploring and comparing different machine learning algorithms in the context of diabetes management. The aim is to understand their characteristics, mathematical foundations, and practical implications specifically for predicting blood glucose levels. The study provides an overview of the algorithms, with a particular emphasis on deep learning techniques such as Long Short-Term Memory Networks. Efficiency is a crucial factor in practical machine learning applications, especially in the context of diabetes management. Therefore, the paper investigates the trade-off between accuracy, resource utilisation, time consumption, and computational power requirements, aiming to identify the optimal balance. By analysing these algorithms, the research uncovers their distinct behaviours and highlights their dissimilarities, even when their analytical underpinnings may appear similar.

KEYWORDS:

• E-health
• regression
• deep learning
• LSTM

Acknowledgments

The work described in this paper has been supported by the Project VALERE “SSCeGov - Semantic, Secure and Law Compliant e-Government Processes”.

Disclosure statement

No potential conflict of interest was reported by the author(s).

Additional information

Funding

This work was supported by Universitá degli Studi della Campania Luigi Vanvitelli.