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Clustering-Based Scaling for Healthcare Data

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Handbook on Artificial Intelligence-Empowered Applied Software Engineering

Part of the book series: Artificial Intelligence-Enhanced Software and Systems Engineering ((AISSE,volume 3))

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Abstract

This chapter describes a fuzzy cluster-scaled regression analysis and applications by using mobile health dataset. In the area of healthcare, attention is being paid to automatic measurement using sensors. However, such data are usually complex and large, and it is difficult to extract their latent features, such as capturing the difference in the kinds of physical human movement and the difference of subjects. The fuzzy cluster-scaled regression analysis is capable of obtaining the latent features by using the obtained clusters by clustering data and utilizes the clusters as scales for measuring differences over the kinds of physical human movement and the subjects. Numerical results are described to show better performances of the fuzzy cluster-scaled regression analysis.

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Authors and Affiliations

  1. Faculty of Engineering, Information and Systems, University of Tsukuba Tennodai, 1-1-1, Tsukuba, 305-8573, Ibaraki, Japan

    Mika Sato-Ilic

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  1. Mika Sato-Ilic
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Correspondence to Mika Sato-Ilic .

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Editors and Affiliations

  1. Department of Informatics, University of Piraeus, Piraeus, Greece

    Maria Virvou

  2. Department of Informatics, University of Piraeus, Piraeus, Greece

    George A. Tsihrintzis

  3. College of Engineering and Computer Science, Wright State University, Joshi Research, Dayton, OH, USA

    Nikolaos G. Bourbakis

  4. KES International, Shoreham-by-Sea, UK

    Lakhmi C. Jain

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Sato-Ilic, M. (2022). Clustering-Based Scaling for Healthcare Data. In: Virvou, M., Tsihrintzis, G.A., Bourbakis, N.G., Jain, L.C. (eds) Handbook on Artificial Intelligence-Empowered Applied Software Engineering. Artificial Intelligence-Enhanced Software and Systems Engineering, vol 3. Springer, Cham. https://doi.org/10.1007/978-3-031-07650-3_9

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