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Measuring Diagnostic Accuracy of Statistical Prediction Rules

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  • D. J. Hand

Abstract

Many different statistical methods have been developed for predicting the disease classes of patients. However, in order to have confidence in the results of such methods, their performance needs to be assessed. Different performance measures are reviewed and the circumstances in which they are relevant are described. Subtleties exist which must be taken into account to ensure that the measure chosen matches the objectives. Examples are given showing different interpretations of future diagnostic performance.

Suggested Citation

  • D. J. Hand, 2001. "Measuring Diagnostic Accuracy of Statistical Prediction Rules," Statistica Neerlandica, Netherlands Society for Statistics and Operations Research, vol. 55(1), pages 3-16, March.
    • Handle: RePEc:bla:stanee:v:55:y:2001:i:1:p:3-16
    DOI: 10.1111/1467-9574.00153
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    Cited by:

    1. Tune H Pers & Anders Albrechtsen & Claus Holst & Thorkild I A Sørensen & Thomas A Gerds, 2009. "The Validation and Assessment of Machine Learning: A Game of Prediction from High-Dimensional Data," PLOS ONE, Public Library of Science, vol. 4(8), pages 1-8, August.
    2. Kaiser, Ulrich & Kuhn, Johan M., 2020. "The value of publicly available, textual and non-textual information for startup performance prediction," Journal of Business Venturing Insights, Elsevier, vol. 14(C).
    3. Elena Ballante & Silvia Figini & Pierpaolo Uberti, 2022. "A new approach in model selection for ordinal target variables," Computational Statistics, Springer, vol. 37(1), pages 43-56, March.
    4. Kaiser, Ulrich & Kuhn, Johan Moritz, 2020. "Value of Publicly Available, Textual and Non-textuThe al Information for Startup Performance Prediction," IZA Discussion Papers 13029, Institute of Labor Economics (IZA).
    5. D J Hand & C Whitrow & N M Adams & P Juszczak & D Weston, 2008. "Performance criteria for plastic card fraud detection tools," Journal of the Operational Research Society, Palgrave Macmillan;The OR Society, vol. 59(7), pages 956-962, July.
    6. Jessica Gronsbell & Molei Liu & Lu Tian & Tianxi Cai, 2022. "Efficient evaluation of prediction rules in semi‐supervised settings under stratified sampling," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 84(4), pages 1353-1391, September.

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