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A Cluster-Based Machine Learning Model for Large Healthcare Data Analysis

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Big Data Innovations and Applications (Innovate-Data 2019)

Part of the book series: Communications in Computer and Information Science ((CCIS,volume 1054))

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Abstract

There is huge growth in the amount of patient survey data being generated in healthcare industries and hospitals. Curse of dimensionality is a barrier to extracting useful information from patient survey data which can help in the treatment and care of patients. It is paramount to have methods to find importance of features based on such huge volumes of stored information for the desired outputs. The health-related quality of life (HRQOL) is a powerful paradigm to help reaching such a desired output, measuring as patient satisfaction. In such scenarios, it is difficult to investigate the features, out of such high-dimensional data, that could best represent desired output and explain them so that such features can be used in the future at the point f care. In this paper we propose a Cluster-based Random Forest (CB-RF) method to particularly exploit the most important features for the desired output, which is Expanded Prostate Index Composite-26 (EPIC-26) domain scores. EPIC-26 is being used for assessing a range of HRQOL issues related to the diagnosis and treatment of prostate cancer. Different feature extraction methods are applied to extract features and the best method is the proposed CB-RF model which could find the most important features (10 or less) out of over 1500 features that can be used to accurately estimate patient with their EPIC-26 values with on average 85% coefficient of correlation between predicted and observed values of real dataset including 5093 patients.

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Author information

Authors and Affiliations

  1. Department of Electrical and Computer Engineering, University of Calgary, Calgary, AB, Canada

    Fatemeh Sharifi & Behrouz H. Far

  2. Faculty of Engineering, Lakehead University, Thunder Bay, ON, Canada

    Emad Mohammed

  3. Department of Surgery, University of Calgary, Calgary, AB, Canada

    Trafford Crump

Authors
  1. Fatemeh Sharifi
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  2. Emad Mohammed
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  3. Trafford Crump
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  4. Behrouz H. Far
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Corresponding author

Correspondence to Fatemeh Sharifi .

Editor information

Editors and Affiliations

  1. School of ECM, Oxford Brookes University, Oxford, UK

    Muhammad Younas

  2. Department of Informatics, University of Bradford , Bradford, UK

    Irfan Awan

  3. Universite Paris Descartes, Paris, France

    Salima Benbernou

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Sharifi, F., Mohammed, E., Crump, T., Far, B.H. (2019). A Cluster-Based Machine Learning Model for Large Healthcare Data Analysis. In: Younas, M., Awan, I., Benbernou, S. (eds) Big Data Innovations and Applications. Innovate-Data 2019. Communications in Computer and Information Science, vol 1054. Springer, Cham. https://doi.org/10.1007/978-3-030-27355-2_7

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  • DOI: https://doi.org/10.1007/978-3-030-27355-2_7

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-27354-5

  • Online ISBN: 978-3-030-27355-2

  • eBook Packages: Computer ScienceComputer Science (R0)

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