Abstract
The Internet-of-Things (IoT) is modifying the infrastructure of technologies through interactions among various modules and components. It has enabled the setting up of complex systems such as smart homes, smart traffic control systems and smart environments. After COVID-19 pandemic, it is becoming more and more difficult to maintain a healthy and secure environment on university grounds. This chapter presents an IoT-based smart health system implemented on a university campus. The smart health system allows people on campus to closely keep track of their health status. A web application has been developed to provide real-time information of their vitals through medical sensors connected to a microcontroller (Arduino) for data acquisition. For disease prediction, a disease prediction module uses the sensor data and a health form to predict three main diseases: cold flu, hypertension and diabetes. To perform prediction, three models namely the cold flu model, hypertension model and diabetes model have been trained on different machine learning algorithms where the most accurate models are deployed in the web application. The cold flu model is evaluated using five different non-linear classification algorithms namely, decision tree (99%), random forest (99.5%), naïve bayes (94.9%), K-Nearest-Neighbour (89.7%) and SVM (55.3%) while hypertension model having a linear distribution is evaluated using three linear classification algorithms namely, logistic regression (86.0%), linear SVM (99.3%) and stochastic gradient descent (49.6%). Besides, the diabetes model is evaluated using logistic regression (88.7%), linear SVM (93.3%), decision tree (98.0%) and KNN (93.3%). The user is alerted of his diagnosis by email. Moreover, the IoT- based smart health system consists of features such as online booking of appointments, health history and a medication section. Proper treatment can therefore be administered based on the users’ health details, diagnosis and medication, if any.
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References
Al Amiri, E., Abdullatif, M., Abdulle, A., Al Bitar, N., Afandi, E. Z., Parish, M., & Darwiche, G. (2015). The prevalence, risk factors, and screening measure for prediabetes and diabetes among Emirati overweight/obese children and adolescents. BMC public Health, 15(1), 1–9.
Ali, F., El-Sappagh, S., Islam, S. R., Kwak, D., Ali, A., Imran, M., & Kwak, K. S. (2020). A smart healthcare monitoring system for heart disease prediction based on ensemble deep learning and feature fusion. Information Fusion, 63, 208–222.
Ali, N., Mahmood, S., Manirujjaman, M., Perveen, R., Al Nahid, A., Ahmed, S., Khanum, F. A., & Rahman, M. (2018). Hypertension prevalence and influence of basal metabolic rate on blood pressure among adult students in Bangladesh. BMC Public Health, 18(1), 1–9.
AlKaabi, L. A., Ahmed, L. S., Al Attiyah, M. F., & Abdel-Rahman, M. E. (2020). Predicting hypertension using machine learning: Findings from Qatar biobank study. PLoS ONE, 15(10), e0240370.
Al-Makhadmeh, Z., & Tolba, A. (2019). Utilizing IoT wearable medical device for heart disease prediction using higher order Boltzmann model: A classification approach. Measurement, 147, 106815.
Antwi, J., Lavin, R., Sullivan, S., & Bellavia, M. (2020). Perception of and risk factors for type 2 diabetes among students attending an upstate New York college: A pilot study. Diabetology and Metabolic Syndrome, 12, 1–8.
Banka, S., Madan, I., & Saranya, S. S. (2018). Smart healthcare monitoring using IoT. International Journal of Applied Engineering Research, 13(15), 11984–11989.
Belgiu, M., & Drăguţ, L. (2016). Random forest in remote sensing: A review of applications and future directions. ISPRS Journal of Photogrammetry and Remote Sensing, 114, 24–31.
Bengio, Y., & Grandvaet, Y. (2004). No unbiased estimator of the variance of k-fold cross-validation. Journal of Machine Learning Research, 5, 1089–1105.
Bhatia, M., & Sood, S. K. (2017). A comprehensive health assessment framework to facilitate IoT-assisted smart workouts. A predictive healthcare perspective. Computers in Industry, 92, 50–66.
docs.anaconda.com. (n.d.). Frequently asked questions—Anaconda documentation. [online] Available at: https://docs.anaconda.com/anaconda/user-guide/faq/. [Accessed 30 July 2021].
Eccles, R. (2005). Understanding the symptoms of the common cold and influenza. The Lancet Infectious Diseases, 5(11), 718–725.
Eccles, R. (2009). Mechanisms of symptoms of common cold and flu. In Common cold (pp. 23–45). Birkhäuser Basel.
Elshoush, H. T., & Dinar, E. A. (2019). Using adaboost and stochastic gradient descent (sgd) algorithms with R and orange software for filtering e-mail spam. In 2019 11th computer science and electronic engineering (CEEC) (pp. 41–46). IEEE.
El Naqa, I., & Murphy, M. J. (2015). What is machine learning?. In Machine learning in radiation oncology (pp. 3–11). Springer, Cham.
Hossin, M., & Sulaiman, M. N. (2015). A review on evaluation metrics for data classification evaluations. International Journal of Data Mining and Knowledge Management Process, 5(2), 1.
Jan, M., Awan, A. A., Khalid, M. S., & Nisar, S. (2018). Ensemble approach for developing a smart heart disease prediction system using classification algorithms. Research Reports in Clinical Cardiology, 9, 33–45.
Jiang, L., Cai, Z., & Wang, D. (2010). Improving naive Bayes for classification. International Journal of Computers and Applications, 32(3), 328–332.
Khlaifat, A. M., Al-Hadid, L. A., Dabbour, R. S., & Shoqirat, N. (2020). Cross-sectional survey on the diabetes knowledge, risk perceptions and practices among university students in South Jordan. Journal of Diabetes and Metabolic Disorders, 1–10.
Kumar, P. M., Lokesh, S., Varatharajan, R., Babu, G. C., & Parthasarathy, P. (2018). Cloud and IoT based disease prediction and diagnosis system for healthcare using Fuzzy neural classifier. Future Generation Computer Systems, 86, 527–534.
Liu, Y., Zhou, Y., Wen, S., & Tang, C. (2014). A strategy on selecting performance metrics for classifier evaluation. International Journal of Mobile Computing and Multimedia Communications (IJMCMC), 6(4), 20–35.
Long, S. (2020). How to tell if you have a common cold or a full-blown case of the flu—SheKnows. [online] Sheknows.com. Available at: https://www.sheknows.com/health-and-wellness/articles/842117/the-common-cold-versus-the-seasonal-flu-whats-the-difference/. [Accessed 25 April 2021].
Moonian, O., Jodheea-Jutton, A., Khedo, K. K., Baichoo, S., Nagowah, S. D., Nagowah, L., Mungloo-Dilmohamud, Z., & Cheerkoot-Jalim, S. (2020). Recent advances in computational tools and resources for the self-management of type 2 diabetes. Informatics for Health and Social Care, 45(1), 77–95.
Nagowah, S. D., & Joaheer, R. (2018). A model for classifying people at risk of diabetes mellitus using social media analytics. In International conference on emerging trends in electrical, electronic and communications engineering (pp. 195–204). Springer, Cham.
Nawaz, M. S., Shoaib, B., & Ashraf, M. A. (2021). Intelligent cardiovascular disease prediction empowered with gradient descent optimization. Heliyon, 7(5), e06948.
Pandey, H., & Prabha, S. (2020). Smart health monitoring system using IOT and machine learning techniques. In 2020 sixth international conference on bio signals, images, and instrumentation (ICBSII) (pp. 1–4). IEEE.
Petkovic, D., Altman, R. B., Wong, M., & Vigil, A. (2018). Improving the explainability of Random forest classifier-user centered approach. In PSB (pp. 204–215).
Pisner, D. A., & Schnyer, D. M. (2020). Support vector machine. In Machine learning (pp. 101–121). Academic Press.
Rymarczyk, T., Kozłowski, E., Kłosowski, G., & Niderla, K. (2019). Logistic regression for machine learning in process tomography. Sensors, 19(15), 3400.
Senthamilarasi, C., Rani, J. J., Vidhya, B., & Aritha, H. (2018). A smart patient health monitoring system using IoT. International Journal of Pure and Applied Mathematics, 119(16), 59–70.
Siam, A. I., Abou Elazm, A., El-Bahnasawy, N. A., El Banby, G., Abd El-Samie, F. E., & Abd El-Samie, F. E. (2019). Smart health monitoring system based on IoT and cloud computing. Menoufia Journal of Electronics Engineering Research, 28, 37–42.
Song, Y. Y., & Ying, L. U. (2015). Decision tree methods: Applications for classification and prediction. Shanghai Archives of Psychiatry, 27(2), 130.
Sooklall, R., Tengnah, M. A. J., & Nagowah, S. D. (2018). A proposed framework for hypertension in Mauritius. Journal of Health Informatics in Africa, 5(1), 16–27.
Suthaharan, S. (2016). Support vector machine. In Machine learning models and algorithms for big data classification (pp. 207–235). Springer.
Tadesse, T., & Alemu, H. (2014). Hypertension and associated factors among university students in Gondar, Ethiopia: A cross-sectional study. BMC Public Health, 14(1), 1–5.
Tengnah, M. A. J., Sooklall, R., & Nagowah, S. D. (2019). A predictive model for hypertension diagnosis using machine learning techniques. In Telemedicine technologies (pp. 139–152). Academic Press.
Zhang, S., Li, X., Zong, M., Zhu, X., & Wang, R. (2017). Efficient kNN classification with different numbers of nearest neighbors. IEEE Transactions on Neural Networks and Learning Systems, 29(5), 1774–1785.
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Mohung, Z.N.S.H., Boodoo, B.U., Nagowah, S.D. (2022). Predictive Analytics for Smart Health Monitoring System in a University Campus. In: Hemanth, D.J. (eds) Machine Learning Techniques for Smart City Applications: Trends and Solutions. Advances in Science, Technology & Innovation. Springer, Cham. https://doi.org/10.1007/978-3-031-08859-9_15
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