评估在电子健康领域预测血糖水平的机器和深度学习技术

IF 3.2 4区 计算机科学 Q2 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE
Beniamino Di Martino, Antonio Esposito, Gennaro Junior Pezzullo, Tien-Hsiung Weng
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引用次数: 0

摘要

本文的重点是探索和比较糖尿病管理背景下不同的机器学习算法。目的是了解它们的特征、数学基础和实际意义,特别是预测血糖水平。该研究提供了算法的概述,特别强调深度学习技术,如长短期记忆网络。在实际的机器学习应用中,效率是一个至关重要的因素,尤其是在糖尿病管理方面。因此,本文研究了准确性,资源利用率,时间消耗和计算能力需求之间的权衡,旨在确定最佳平衡。通过分析这些算法,研究揭示了它们的独特行为,并强调了它们的不同之处,即使它们的分析基础可能看起来相似。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Evaluating machine and deep learning techniques in predicting blood sugar levels within the E-health domain
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.
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来源期刊
Connection Science
Connection Science 工程技术-计算机:理论方法
CiteScore
6.50
自引率
39.60%
发文量
94
审稿时长
3 months
期刊介绍: Connection Science is an interdisciplinary journal dedicated to exploring the convergence of the analytic and synthetic sciences, including neuroscience, computational modelling, artificial intelligence, machine learning, deep learning, Database, Big Data, quantum computing, Blockchain, Zero-Knowledge, Internet of Things, Cybersecurity, and parallel and distributed computing. A strong focus is on the articles arising from connectionist, probabilistic, dynamical, or evolutionary approaches in aspects of Computer Science, applied applications, and systems-level computational subjects that seek to understand models in science and engineering.
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