Empowering Health and Well-being: IoT-Driven Vital Signs Monitoring in Educational Institutions and Elderly Homes Using Machine Learning

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dc.contributor.authorDuodu, Nana Yaw
dc.contributor.authorPatel, Warish D.
dc.contributor.authorKoyuncu, Hakan
dc.contributor.authorNartey, Felix
dc.contributor.authorTorgby, Wisdom
dc.date.accessioned2025-02-06T18:01:18Z
dc.date.available2025-02-06T18:01:18Z
dc.date.issued2024
dc.departmentAltınbaş Üniversitesien_US
dc.description.abstractIoT-based EHRs use machine learning technology to automate real-time patient-centered records more securely for authorized users. Background: In this era of pandemics, predictive healthcare systems are necessary for private and public healthcare delivery to predict early cancer, COVID-19, hypertension, and fever in Educational Institutions and Elderly Homes. IoT-Based EHRs bring healthcare delivery to the doorsteps of educational home facilities users, thereby reducing the time required to access healthcare and minimizing direct physical interaction between individuals seeking healthcare and their providers. Method: This research work proposed a real-time intelligent IoT-based EHR system that generates vital signs of students within the educational environment using contactless sensors (Raspberry Pi Noir Camera, rPPG camera) and contacted wearable sensors composed of enzymatic sensor, immunogens, and Nano sensors to detect cancer (Leukaemia). AFTER CAPTURING THE PHYSIOLOGICAL DATA, THE in-build EWS plots system determines the condition and further triggers the criticality (abnormality) in health status. Discussion: For effective health status prediction by the proposed plan, the vital sign dataset was used to train a model for the proposed method. Among the best-performing models, the random forest algorithm proved a better model, with an accuracy of 99.66% and an error rate of 0.34%. Conclusion: The Home HMS seeks to improve health prediction in institutional homes for users' overall well-being. © 2024 by the Nana Yaw Duodu, Warish D. Patel, Hakan Koyuncu, Felix Nartey, Wisdom Torgby.en_US
dc.description.sponsorshipATU Research and Innovation Funden_US
dc.identifier.doi10.37391/IJEER.12bdf07
dc.identifier.endpage49en_US
dc.identifier.issn2347-470X
dc.identifier.issueSpecial Issueen_US
dc.identifier.scopus2-s2.0-85196941447
dc.identifier.scopusqualityQ3en_US
dc.identifier.startpage42en_US
dc.identifier.urihttps://doi.org/10.37391/IJEER.12bdf07
dc.identifier.urihttps://hdl.handle.net/20.500.12939/5297
dc.identifier.volume12en_US
dc.indekslendigikaynakScopus
dc.language.isoenen_US
dc.publisherForex Publicationen_US
dc.relation.ispartofInternational Journal of Electrical and Electronics Researchen_US
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanıen_US
dc.rightsinfo:eu-repo/semantics/closedAccessen_US
dc.snmzKA_Scopus_20250206
dc.subjectEducational Institutions and Elderly Homesen_US
dc.subjectEHRen_US
dc.subjectEHRsen_US
dc.subjectHealth Monitoringen_US
dc.subjectInternet of thingsen_US
dc.subjectIoT-baseden_US
dc.subjectMachine Learningen_US
dc.titleEmpowering Health and Well-being: IoT-Driven Vital Signs Monitoring in Educational Institutions and Elderly Homes Using Machine Learningen_US
dc.typeArticleen_US

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