RT Journal Article
T1 Predicting abnormal respiratory patterns in older adults using supervised machine learning on Internet of medical things respiratory frequency data
A1 Santana Mancilla, Pedro C.
A1 Castrejón Mejía, Oscar E.
A1 Fajardo Flores, Silvia B.
A1 Anido Rifón, Luis Eulogio
K1 3399 Otras Especialidades Tecnológicas
AB 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.
PB Information
SN 20782489
YR 2023
FD 2023-11-21
LK http://hdl.handle.net/11093/6448
UL http://hdl.handle.net/11093/6448
LA eng
NO Information, 14(12): 625 (2023)
DS Investigo
RD 13-dic-2024