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      An efficient cat hunting optimization-biased ReLU neural network for healthcare monitoring system

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          Effective Heart Disease Prediction using Hybrid Machine Learning Techniques

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            Is Open Access

            Evolution of Wearable Devices with Real-Time Disease Monitoring for Personalized Healthcare

            Wearable devices are becoming widespread in a wide range of applications, from healthcare to biomedical monitoring systems, which enable continuous measurement of critical biomarkers for medical diagnostics, physiological health monitoring and evaluation. Especially as the elderly population grows globally, various chronic and acute diseases become increasingly important, and the medical industry is changing dramatically due to the need for point-of-care (POC) diagnosis and real-time monitoring of long-term health conditions. Wearable devices have evolved gradually in the form of accessories, integrated clothing, body attachments and body inserts. Over the past few decades, the tremendous development of electronics, biocompatible materials and nanomaterials has resulted in the development of implantable devices that enable the diagnosis and prognosis through small sensors and biomedical devices, and greatly improve the quality and efficacy of medical services. This article summarizes the wearable devices that have been developed to date, and provides a review of their clinical applications. We will also discuss the technical barriers and challenges in the development of wearable devices, and discuss future prospects on wearable biosensors for prevention, personalized medicine and real-time health monitoring.
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              The importance of interpretability and visualization in machine learning for applications in medicine and health care

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                Author and article information

                Journal
                Wireless Networks
                Wireless Netw
                Springer Science and Business Media LLC
                1022-0038
                1572-8196
                November 2023
                June 13 2023
                November 2023
                : 29
                : 8
                : 3349-3365
                Article
                10.1007/s11276-023-03373-x
                acf4a241-dacb-4b00-b8ca-05b2efed393d
                © 2023

                https://www.springernature.com/gp/researchers/text-and-data-mining

                https://www.springernature.com/gp/researchers/text-and-data-mining

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