Abstract
A medical database with 257 patients thought to have acute appendicitis has been analyzed. Binary classifiers composed of very simple univariate if-then classification rules (1R rules) were synthesized, and are shown to perform well for determining the true disease status. Discriminatory performance was measured by the area under the receiver operating characteristic (ROC) curve. Although an 1R classifier seemingly performs slightly better than a team of experienced physicians when only readily available clinical variables are employed, an analysis of cross-validated simulations shows that this perceived improvement is not statistically significant (p < 0.613). However, further addition of biochemical test results to the model yields an 1R classifier that is significantly better than both the physicians (p < 0.03) and an 1R classifier based on clinical variables only (p < 0.0003).
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Keywords
- Receiver Operating Characteristic
- Receiver Operating Characteristic Curve
- Acute Appendicitis
- Clinical Attribute
- Resampling Scheme
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.
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© 1999 Springer-Verlag Berlin Heidelberg
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Øhrn, A., Komorowski, J. (1999). Diagnosing Acute Appendicitis with Very Simple Classification Rules. In: Żytkow, J.M., Rauch, J. (eds) Principles of Data Mining and Knowledge Discovery. PKDD 1999. Lecture Notes in Computer Science(), vol 1704. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-48247-5_59
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DOI: https://doi.org/10.1007/978-3-540-48247-5_59
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