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Architecting IoT based Healthcare Systems Using Machine Learning Algorithms: Cloud-Oriented Healthcare Model, Streaming Data Analytics Architecture, and Case Study

Architecting IoT based Healthcare Systems Using Machine Learning Algorithms: Cloud-Oriented Healthcare Model, Streaming Data Analytics Architecture, and Case Study

G. S. Karthick, P. B. Pankajavalli
Copyright: © 2022 |Pages: 26
ISBN13: 9781668462911|ISBN10: 1668462915|EISBN13: 9781668462928
DOI: 10.4018/978-1-6684-6291-1.ch012
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MLA

Karthick, G. S., and P. B. Pankajavalli. "Architecting IoT based Healthcare Systems Using Machine Learning Algorithms: Cloud-Oriented Healthcare Model, Streaming Data Analytics Architecture, and Case Study." Research Anthology on Machine Learning Techniques, Methods, and Applications, edited by Information Resources Management Association, IGI Global, 2022, pp. 198-223. https://doi.org/10.4018/978-1-6684-6291-1.ch012

APA

Karthick, G. S. & Pankajavalli, P. B. (2022). Architecting IoT based Healthcare Systems Using Machine Learning Algorithms: Cloud-Oriented Healthcare Model, Streaming Data Analytics Architecture, and Case Study. In I. Management Association (Ed.), Research Anthology on Machine Learning Techniques, Methods, and Applications (pp. 198-223). IGI Global. https://doi.org/10.4018/978-1-6684-6291-1.ch012

Chicago

Karthick, G. S., and P. B. Pankajavalli. "Architecting IoT based Healthcare Systems Using Machine Learning Algorithms: Cloud-Oriented Healthcare Model, Streaming Data Analytics Architecture, and Case Study." In Research Anthology on Machine Learning Techniques, Methods, and Applications, edited by Information Resources Management Association, 198-223. Hershey, PA: IGI Global, 2022. https://doi.org/10.4018/978-1-6684-6291-1.ch012

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Abstract

The rapid innovations in technologies endorsed the emergence of sensory equipment's connection to the Internet for acquiring data from the environment. The increased number of devices generates the enormous amount of sensor data from diversified applications of Internet of things (IoT). The generation of data may be a fast or real-time data stream which depends on the nature of applications. Applying analytics and intelligent processing over the data streams discovers the useful information and predicts the insights. Decision-making is a prominent process which makes the IoT paradigm qualified. This chapter provides an overview of architecting IoT-based healthcare systems with different machine learning algorithms. This chapter elaborates the smart data characteristics and design considerations for efficient adoption of machine learning algorithms into IoT applications. In addition, various existing and hybrid classification algorithms are applied to sensory data for identifying falls from other daily activities.

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