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

Aug 3, 2026

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Wearable Internet of Medical Things (IoMT) enables non-invasive respiratory monitoring and supports earlier detection of severe disease. This paper applies supervised machine learning to predict respiratory abnormalities by analyzing respiratory frequency data collected from non-invasive wearable devices. The study targets older adults to identify respiratory-related health risks and develops, evaluates, and compares three machine learning models. The combination of wearable IoMT and predictive ML is positioned to enable proactive, personalized healthcare and improve quality of life.

LanguageEnglish

CategoryHealthcare

FormatPDF

Pages18

Reading Time45 min

AccessFree to download

Introduction

Older adults and healthcare challenges

Respiratory infections and clinical needs

Internet of Medical Things and machine learning

Methods

Data and respiratory frequency analysis

Supervised machine learning models

Results and Discussion

Model comparison and predictive performance

Implications for proactive healthcare

Conclusions

information Article Predicting Abnormal Respiratory Patterns in Older Adults Using Supervised Machine Learning on Internet of Medical Things Respiratory Frequency Data Pedro C. Santana-Mancilla 1, Oscar E. Castrejân-Mej½a 1, Silvia B. Fajardo-Flores 1 and Luis E. Anido-Rifân 2, * Citation: Santana-Mancilla, P.C.; Castrejón-Mejía, O.E.; Fajardo-Flores, S.B.; Anido-Rifón, L.E. Predicting Abnormal Respiratory Patterns in Older Adults Using Supervised Machine Learning on Internet of Medical Things Respiratory Frequency Data. Information 2023, 14, 625. [https://doi.org/10.3390/](https://doi.org/10.3390/)[ ](https://doi.org/10.3390/)info14120625 Academic Editors: Vasco N. G. J. Soares, Jo¢o M. L. P. Caldeira, Bruno Bogaz Zarpel¢o and Jaime Gal¡n-Jim²nez Received: 10 October 2023 Revised: 16 November 2023 Accepted: 17 November 2023 Published: 21 November 2023 Copyright: © 2023 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license ([https://](https://)[ ](https://)[creativecommons.org/licenses/by/](creativecommons.org/licenses/by/)[ ](creativecommons.org/licenses/by/)[4.0/](4.0/)) . 1 School of Telematics, Universidad de Colima, Colima 28040, Mexico; [psantana@ucol.mx](psantana@ucol.mx) (P.C.S.-M.); [ocastrejon@ucol.mx](ocastrejon@ucol.mx) (O.E.C.-M.); [medusa@ucol.mx](medusa@ucol.mx) (S.B.F.-F.) 2 atlanTTic Research Center, School of Telecommunications Engineering, University of Vigo, 36310 Vigo, Spain * Correspondence: lanido@det.uvigo.es Abstract: 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. Keywords: internet of medical things; respiratory monitoring; abnormal respiratory patterns; predictive machine learning; older adults; wearable technology 1. Introduction In modern times, technology has become an integral part of our daily routine, with numerous initiatives aimed at improving our lives through tech-based solutions. This project explicitly targets individuals aged 60 and above who are vulnerable regarding health. Around 26.3% of this population lacks health insurance coverage and faces dif􀀂culties regarding companionship and care due to changing family dynamics [1] . The growing global population of senior citizens is a critical matter that demands our attention. In 1950, there were 200 million people aged 60 and above, but this number exceeded 350 million by 1975 . According to recent projections, this 􀀂gure is expected to double by 2025 [2] . Older adults who have been inactive and con􀀂ned to bed for extended periods may develop cardiovascular, respiratory, and musculoskeletal problems, exacerbating their illnesses [3] . Furthermore, this age group tends to recover more slowly, with 33% experiencing dif􀀂culties performing one or more daily activities among those aged 60 to 79, increasing to 50% for those over 80 [3] . Respiratory infections affecting the lower tract are a severe health risk for older individuals worldwide. Two types of lower respiratory tract infections (LRTIs) that are of concern are chronic obstructive pulmonary disease (CO

FAQs

What is the main goal of the study?

To predict respiratory abnormalities in older adults using supervised machine learning based on respiratory frequency data collected from non-invasive wearable IoMT devices.

How does the paper use Internet of Medical Things in respiratory monitoring?

It describes IoMT connecting medical devices and sensors to collect real-time physiological data, which supports accurate diagnosis and early detection when combined with machine learning.

What models does the paper develop and how are they handled?

It develops three supervised machine learning models and presents their development, assessment, and comparison to evaluate their ability to predict respiratory-related health issues.

Aug 3, 2026

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