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Automatic Classification of National Health Service Feedback

Author

Listed:
  • Christopher Haynes

    (School of Engineering, Computing and Mathematics, University of Plymouth, Plymouth PL4 8AA, UK)

  • Marco A. Palomino

    (School of Engineering, Computing and Mathematics, University of Plymouth, Plymouth PL4 8AA, UK)

  • Liz Stuart

    (School of Engineering, Computing and Mathematics, University of Plymouth, Plymouth PL4 8AA, UK)

  • David Viira

    (Faculty of Health, University Hospitals Plymouth, Derriford Rd., Plymouth PL6 8DH, UK)

  • Frances Hannon

    (Faculty of Health, University Hospitals Plymouth, Derriford Rd., Plymouth PL6 8DH, UK)

  • Gemma Crossingham

    (Faculty of Health, University Hospitals Plymouth, Derriford Rd., Plymouth PL6 8DH, UK)

  • Kate Tantam

    (Faculty of Health, University Hospitals Plymouth, Derriford Rd., Plymouth PL6 8DH, UK)

Abstract

Text datasets come in an abundance of shapes, sizes and styles. However, determining what factors limit classification accuracy remains a difficult task which is still the subject of intensive research. Using a challenging UK National Health Service (NHS) dataset, which contains many characteristics known to increase the complexity of classification, we propose an innovative classification pipeline. This pipeline switches between different text pre-processing, scoring and classification techniques during execution. Using this flexible pipeline, a high level of accuracy has been achieved in the classification of a range of datasets, attaining a micro-averaged F1 score of 93.30% on the Reuters-21578 “ApteMod” corpus. An evaluation of this flexible pipeline was carried out using a variety of complex datasets compared against an unsupervised clustering approach. The paper describes how classification accuracy is impacted by an unbalanced category distribution, the rare use of generic terms and the subjective nature of manual human classification.

Suggested Citation

  • Christopher Haynes & Marco A. Palomino & Liz Stuart & David Viira & Frances Hannon & Gemma Crossingham & Kate Tantam, 2022. "Automatic Classification of National Health Service Feedback," Mathematics, MDPI, vol. 10(6), pages 1-23, March.
    • Handle: RePEc:gam:jmathe:v:10:y:2022:i:6:p:983-:d:774482
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    References listed on IDEAS

    as
    1. Matthias Schonlau & Nick Guenther & Ilia Sucholutsky, 2017. "Text mining with n-gram variables," Stata Journal, StataCorp LP, vol. 17(4), pages 866-881, December.
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    Cited by:

    1. Florentina Hristea & Cornelia Caragea, 2022. "Preface to the Special Issue “Natural Language Processing (NLP) and Machine Learning (ML)—Theory and Applications”," Mathematics, MDPI, vol. 10(14), pages 1-5, July.
    2. Bogdan Oancea, 2023. "Automatic Product Classification Using Supervised Machine Learning Algorithms in Price Statistics," Mathematics, MDPI, vol. 11(7), pages 1-32, March.
    3. Sergio Bolívar & Alicia Nieto-Reyes & Heather L. Rogers, 2022. "Supervised Classification of Healthcare Text Data Based on Context-Defined Categories," Mathematics, MDPI, vol. 10(12), pages 1-31, June.
    4. Sergio Bolívar & Alicia Nieto-Reyes & Heather L. Rogers, 2023. "Statistical Depth for Text Data: An Application to the Classification of Healthcare Data," Mathematics, MDPI, vol. 11(1), pages 1-20, January.

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