Predicting Abnormal Respiratory Patterns in Older Adults Using Supervised Machine Learning on Internet of Medical Things Respiratory Frequency Data

Autor:	Pedro C. Santana-Mancilla, Oscar E. Castrejón-Mejía, Silvia B. Fajardo-Flores, Luis E. Anido-Rifón
Jazyk:	angličtina
Rok vydání:	2023
Předmět:	internet of medical things respiratory monitoring abnormal respiratory patterns predictive machine learning older adults wearable technology Information technology T58.5-58.64
Zdroj:	Information, Vol 14, Iss 12, p 625 (2023)
Druh dokumentu:	article
ISSN:	2078-2489
DOI:	10.3390/info14120625
Popis:	Wearable Internet of Medical Things (IoMT) technology, designed for non-invasive respiratory monitoring, has demonstrated considerable promise in the early detection of severe diseases. This paper introduces the application of supervised machine learning techniques to predict respiratory abnormalities through frequency data analysis. The principal aim is to identify respiratory-related health risks in older adults using data collected from non-invasive wearable devices. This article presents the development, assessment, and comparison of three machine learning models, underscoring their potential for accurately predicting respiratory-related health issues in older adults. The convergence of wearable IoMT technology and machine learning holds immense potential for proactive and personalized healthcare among older adults, ultimately enhancing their quality of life.
Databáze:	Directory of Open Access Journals
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