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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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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