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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: © 2020 |Pages: 27
ISBN13: 9781799810902|ISBN10: 1799810909|EISBN13: 9781799810919
DOI: 10.4018/978-1-7998-1090-2.ch003
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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." Incorporating the Internet of Things in Healthcare Applications and Wearable Devices, edited by P. B. Pankajavalli and G. S. Karthick, IGI Global, 2020, pp. 40-66. https://doi.org/10.4018/978-1-7998-1090-2.ch003

APA

Karthick, G. S. & Pankajavalli, P. B. (2020). Architecting IoT based Healthcare Systems Using Machine Learning Algorithms: Cloud-Oriented Healthcare Model, Streaming Data Analytics Architecture, and Case Study. In P. Pankajavalli & G. Karthick (Eds.), Incorporating the Internet of Things in Healthcare Applications and Wearable Devices (pp. 40-66). IGI Global. https://doi.org/10.4018/978-1-7998-1090-2.ch003

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 Incorporating the Internet of Things in Healthcare Applications and Wearable Devices, edited by P. B. Pankajavalli and G. S. Karthick, 40-66. Hershey, PA: IGI Global, 2020. https://doi.org/10.4018/978-1-7998-1090-2.ch003

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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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