Abstract
Data analytics in healthcare applications play an important role as it gives insights into various aspects. In the present digital world, many gadgets are used for health monitoring. The data from these devices need to be collected in a careful manner as the privacy of the data also needs to be ensured. Machine learning methods such as clustering, random forests can be used for the analysis of various healthcare records. The patterns and trends in data become much more useful to predict and analyze data specifically in the sphere of healthcare. The aim of this chapter is to provide the different data science-oriented tools and methods for healthcare analytics. A case study on the usage of different tools and techniques is also presented at the end of the chapter that provides the complete life-cycle of the analytics phase for healthcare applications.
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Hiriyannaiah, S. et al. (2021). Data Science Tools and Techniques for Healthcare Applications. In: Srinivasa, K.G., G. M., S., Sekhar, S.R.M. (eds) Artificial Intelligence for Information Management: A Healthcare Perspective. Studies in Big Data, vol 88. Springer, Singapore. https://doi.org/10.1007/978-981-16-0415-7_10
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DOI: https://doi.org/10.1007/978-981-16-0415-7_10
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