School of Computer Science, National College of Business Administration and Economics, Lahore 54000, Pakistan.
Lahore Institute of Science and Technology, Lahore 54792, Pakistan.
Comput Intell Neurosci. 2021 Nov 25;2021:2487759. doi: 10.1155/2021/2487759. eCollection 2021.
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.
物联网医疗（IoMT）使数字设备能够通过云平台收集、推断和广播健康数据。物联网医疗的飞速发展得益于许多因素，包括可穿戴设备的广泛普及和日益增长，以及基于传感器的技术成本的不断降低。随着全球老年人口的增长以及对负担得起的医疗保健服务、解决方案和发展的整体预期寿命的要求，相关医疗保健的成本将会上升。IoMT 可能会在医疗科学领域带来革命，改善老年人的医疗保健质量，同时与机器学习（ML）算法纠缠在一起。通过对 IoMT 数据集进行测试，观察到智能医疗保健（SHC）模型对老年人进行监测的有效性。为了进行评估，计算了精度、召回率、f 分数、准确性和 ROC 值。作者还将 SHC 模型的结果与不同的传统流行的 ML 技术进行比较，例如支持向量机（SVM）、K 最近邻（KNN）和决策树（DT），以分析结果的有效性。
