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IoT in Healthcare: A Big Data Perspective

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Smart Healthcare Analytics in IoT Enabled Environment

Part of the book series: Intelligent Systems Reference Library ((ISRL,volume 178))

Abstract

With the advent of Internet of Things, the entire world seems to be connected. Everything is connected to anything. It is natural that health care could not remain untouched, and smart health care systems are coming in practice. Wearable or implanted sensors form body area networks transmitting data at an enormous rate. This further brings in the huge amount of data, often called Big Data which needs to be stored and analyzed. In the era of Artificial intelligence, it is imperative that researchers look towards machine learning tools to handle this vast amount of medical data. This chapter presents a framework for data analytics using Random Forest classification technique. A comparison is done after applying feature selection. It is seen that the training time gets reduced substantially even though the accuracy does not suffer. This is the most important requirement of Big data handling. The algorithms are implemented on Apache Spark.

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Author information

Authors and Affiliations

  1. Department of Computer Science and Engineering, BIT Mesra, Ranchi, India

    Ritesh Jha, Vandana Bhattacharjee & Abhijit Mustafi

Authors
  1. Ritesh Jha
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  2. Vandana Bhattacharjee
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  3. Abhijit Mustafi
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Corresponding author

Correspondence to Vandana Bhattacharjee .

Editor information

Editors and Affiliations

  1. School of Computer Engineering, KIIT Deemed to be University, Bhubaneswar, Odisha, India

    Prasant Kumar Pattnaik

  2. School of Computer Engineering, KIIT Deemed to be University, Bhubaneswar, Odisha, India

    Suneeta Mohanty

  3. School of Computer Engineering, KIIT Deemed to be University, Bhubaneswar, Odisha, India

    Satarupa Mohanty

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Jha, R., Bhattacharjee, V., Mustafi, A. (2020). IoT in Healthcare: A Big Data Perspective. In: Pattnaik, P., Mohanty, S., Mohanty, S. (eds) Smart Healthcare Analytics in IoT Enabled Environment. Intelligent Systems Reference Library, vol 178. Springer, Cham. https://doi.org/10.1007/978-3-030-37551-5_13

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