An iomt-enabled smart healthcare model to monitor elderly people using machine learning technique
The Internet of Medical Things (IoMT) enables digital devices to gather, infer, and broadcast health data via the cloud platform. The phenomenal growth of the IoMT is fueled by many factors, including the widespread and growing availability of wearables and the ever-decreasing cost of sensor-based technology. The cost of related healthcare will rise as the global population of elderly people grows in parallel with an overall life expectancy that demands affordable healthcare services, solutions, and developments. IoMT may bring revolution in the medical sciences in terms of the quality of healthcare of elderly people while entangled with machine learning (ML) algorithms. The effectiveness of the smart healthcare (SHC) model to monitor elderly people was observed by performing tests on IoMT datasets. For evaluation, the precision, recall, fscore, accuracy, and ROC values are computed. The authors also compare the results of the SHC model with different conventional popular ML techniques, e.g., support vector machine (SVM), K-nearest neighbor (KNN), and decision tree (DT), to analyze the effectiveness of the result. © 2021 Muhammad Farrukh Khan et al.
This article is not available at CUD collection. The version of scholarly record of this article is published in Computational Intelligence and Neuroscience (2021), available online at: https://doi.org/10.1155/2021/2487759
Decision trees, Health care, Nearest neighbor search, Support vector machines, Wearable technology, Cloud platforms, Elderly people, Global population, Health care modeling, Health data, Healthcare services, Life expectancies, Machine learning techniques, Medical science, Quality of health care
Khan, M. F., Ghazal, T. M., Said, R. A., Fatima, A., Abbas, S., Khan, M. A., . . . Khan, M. A. (2021). An iomt-enabled smart healthcare model to monitor elderly people using machine learning technique. Computational Intelligence and Neuroscience, 2021 https://doi.org/10.1155/2021/2487759