Abstract
Advancement in sensor technologies has resulted in rapid evolution of Internet of Things (IoT) applications for developing behavioral and physiological monitoring systems such as IoT-based student healthcare monitoring system. Nowadays, a growing number of students living alone scattered over wide geographical areas, and tracking their health function status is necessary. In this paper, an IoT-based student healthcare monitoring model is proposed to continuously check student vital signs and detect biological and behavioral changes via smart healthcare technologies. In this model, vital data are collected via IoT devices and data analysis is carried out through the machine learning methods for detecting the probable risks of student’s physiological and behavioral changes. The experimental results reveal that the proposed model meets the efficiency and proper accuracy for detecting the students’ condition. After evaluating the proposed model, the support vector machine has achieved the highest accuracy of 99.1% which is a promising result for our purpose. The results outperformed decision tree, random forest, and multilayer perceptron neural network algorithms as well.
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References
Cai Y et al (2018) Software defined status aware routing in content-centric networking. In: 2018 International conference on information networking (ICOIN). IEEE
Chavda P et al (2019) Early detection of cardiac disease using machine learning. Available at SSRN 3370813
Fried LP et al (2001) Frailty in older adults: evidence for a phenotype. J Gerontol Ser A Biol Sci Med Sci 56(3):M146–M157
Ghanbari-Adivi F, Mosleh M (2019) Text emotion detection in social networks using a novel ensemble classifier based on Parzen Tree Estimator (TPE). Neural Comput Appl 31(12):8971–8983
Hamim M et al (2019) IoT based remote health monitoring system for patients and elderly people. In: 2019 International conference on robotics, electrical and signal processing techniques (ICREST). IEEE
Henze M et al (2016) A comprehensive approach to privacy in the cloud-based Internet of Things. Future Generation Computer Systems 56:701–718
Hussain A et al (2015) Health and emergency-care platform for the elderly and disabled people in the Smart City. J Syst Softw 110:253–263
Jabeen F et al (2019) An IoT based efficient hybrid recommender system for cardiovascular disease. Peer-to-Peer Netw Appl 1–14
Kaur P, Kumar R, Kumar M (2019) A healthcare monitoring system using random forest and internet of things (IoT). Multimed Tools Appl 78:19905–19916. https://doi.org/10.1007/s11042-019-7327-8
Lakshmanaprabu S et al (2019) Online clinical decision support system using optimal deep neural networks. Appl Soft Comput 81:105487
Lee S-K et al (2014) Prediction model for health-related quality of life of elderly with chronic diseases using machine learning techniques. Healthc Inform Res 20(2):125–134
Lee I-C, Chiu Y-H, Lee I-N, Lee C-Y (2017) Health-function indicators for the prediction of elderly frailty. J Aging Res Clin Pract 6:88–93
Mainetti L, Patrono L, Rametta P (2016) Capturing behavioral changes of elderly people through unobtrusive sensing technologies. In: 2016 24th International conference on software, telecommunications and computer networks (SoftCOM). IEEE
Perez D, Memeti S, Pllana S (2018) A simulation study of a smart living IoT solution for remote elderly care. In: 2018 Third international conference on fog and mobile edge computing (FMEC). IEEE
Pramanik PKD et al (2019) Internet of things, smart sensors, and pervasive systems: enabling connected and pervasive healthcare. In: Dey N, Ashour AS, Fong SJ, Bhatt C (eds) Healthcare data analytics and management. Elsevier, Amsterdam, pp 1–58
Richard AAR, et al (2019) Health monitoring system for elderly and disabled people. In: 2019 International conference on robotics, electrical and signal processing techniques (ICREST). IEEE
Souri A et al (2019) A systematic review of IoT communication strategies for an efficient smart environment. Trans Emerg Telecommun Technol e3736. https://doi.org/10.1002/ett.3736
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Souri, A., Ghafour, M.Y., Ahmed, A.M. et al. A new machine learning-based healthcare monitoring model for student’s condition diagnosis in Internet of Things environment. Soft Comput 24, 17111–17121 (2020). https://doi.org/10.1007/s00500-020-05003-6
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DOI: https://doi.org/10.1007/s00500-020-05003-6