An IoMT‐Enabled Smart Healthcare Model to Monitor Elderly People Using Machine Learning Technique

Abstract

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.

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“…The performance analysis of the proposed model is carried and the outcomes attained are compared with existing methods ( Latif et al., 2022 ), ( Khan et al., 2021 ), ( Yang et al., 2017 ) and ( Sharma et al., 2021b ) to validate the efficiency of proposed methodology. The outcomes attained from the analysis is projected below in the table and graphical representation.…”

A secure healthcare 5.0 system based on blockchain technology entangled with federated learning technique

“…The data collected in this manner can be examined, pooled, and mined to perform effective disease prediction [26]. Khan et al [27] suggested novel healthcare facilities for senior citizens focused on the patients' actual needs and problems. To better satisfy the basic demands of elderly healthcare, the researchers applied machine learning approaches.…”

Machine Learning Methods for Cancer Classification Using Gene Expression Data: A Review

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