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Improved concept drift handling in surgery prediction and other applications

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Abstract

The article presents a new algorithm for handling concept drift: the Trigger-based Ensemble (TBE) is designed to handle concept drift in surgery prediction but it is shown to perform well for other classification problems as well. At the primary care, queries about the need for surgical treatment are referred to a surgeon specialist. At the secondary care, referrals are reviewed by a team of specialists. The possible outcomes of this review are that the referral: (i) is canceled, (ii) needs to be complemented, or (iii) is predicted to lead to surgery. In the third case, the referred patient is scheduled for an appointment with a surgeon specialist. This article focuses on the binary prediction of case three (surgery prediction). The guidelines for the referral and the review of the referral are changed due to, e.g., scientific developments and clinical practices. Existing decision support is based on the expert systems approach, which usually requires manual updates when changes in clinical practice occur. In order to automatically revise decision rules, the occurrence of concept drift (CD) must be detected and handled. The existing CD handling techniques are often specialized; it is challenging to develop a more generic technique that performs well regardless of CD type. Experiments are conducted to measure the impact of CD on prediction performance and to reduce CD impact. The experiments evaluate and compare TBE to three existing CD handling methods (AWE, Active Classifier, and Learn++) on one real-world dataset and one artificial dataset. TBA significantly outperforms the other algorithms on both datasets but is less accurate on noisy synthetic variations of the real-world dataset.

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Notes

  1. The UCI machine learning repository, http://archive.ics.uci.edu/ml/.

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Acknowledgments

The conducted research is funded by a grant from Blekinge county research council. The authors would like to thank Elisabeth Håkansson at Blekinge hospital for extracting and compiling patient records as well as the corresponding decisions concerning surgery to enable the generation of the real-world hip-replacement dataset.

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Authors and Affiliations

  1. Department of Computer Science and Engineering, Blekinge Institute of Technology, 371 79 , Karlskrona, Sweden

    Ayne A. Beyene, Tewelle Welemariam, Marie Persson & Niklas Lavesson

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  1. Ayne A. Beyene
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  2. Tewelle Welemariam
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  3. Marie Persson
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  4. Niklas Lavesson
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Correspondence to Niklas Lavesson.

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Beyene, A.A., Welemariam, T., Persson, M. et al. Improved concept drift handling in surgery prediction and other applications. Knowl Inf Syst 44, 177–196 (2015). https://doi.org/10.1007/s10115-014-0756-9

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  • DOI: https://doi.org/10.1007/s10115-014-0756-9

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