3 July 2001 Investigating different similarity measures for a case-based reasoning classifier to predict breast cancer
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Abstract
This paper investigates the effects of using different similarity measures for a case-based reasoning (CBR) classifier to predict breast cancer. The CBR classifier used a mammographer's BI-RADSTM description of a lesion to predict breast biopsy outcome. The classifier compared the case to be examined to a reference collection of cases and identified those that were similar. The decision variable was formed as the ratio of similar cases that were malignant to all similar cases. A reference collection of 1027 biopsy-proven cases from Duke University Medical Center was used as input. Both Euclidean and Hamming distance measures were compared using all possible combinations of nine BI-RADSTM features and age. Performance was evaluated using jackknife sampling and ROC analysis. For all combinations of features, it was found that Euclidean distance measure produced greater ROC areas and partial ROC areas than Hamming. The differences were significant at an alpha level of 0.05. The greatest ROC area of 0.82 +/- 0.01 was generated using six of the features and Euclidean distance measure. The results of both distance measures yielded greater ROC areas than previously reported values and were similar to results generated with an Artificial Neural Network using 10 features.
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Anna O. Bilska-Wolak, Carey E. Floyd, "Investigating different similarity measures for a case-based reasoning classifier to predict breast cancer", Proc. SPIE 4322, Medical Imaging 2001: Image Processing, (3 July 2001); doi: 10.1117/12.431077; https://doi.org/10.1117/12.431077
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