Abstract
We evaluate the performance of seven classifiers as effective potential decision support tools in the cytodiagnosis of breast cancer. To this end, we use a real-world database containing 692 fine needle aspiration of the breast lesion cases collected by a single observer. The results show, in average, good overall classification performance in terms of five different tests: accuracy of 93.62%, sensitivity of 89.37%, specificity of 96%, PV+ of 92% and PV- of 94.5%. With this comparison, we identify and discuss the advantages and disadvantages of each of these approaches. Finally, based on these results, we give some advice regarding the selection on the classifier depending on the user’s needs.
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Cruz-Ramírez, N., Acosta-Mesa, HG., Carrillo-Calvet, H., Barrientos-Martínez, RE. (2007). Comparison of the Performance of Seven Classifiers as Effective Decision Support Tools for the Cytodiagnosis of Breast Cancer: A Case Study. In: Melin, P., Castillo, O., Ramírez, E.G., Kacprzyk, J., Pedrycz, W. (eds) Analysis and Design of Intelligent Systems using Soft Computing Techniques. Advances in Soft Computing, vol 41. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-72432-2_9
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DOI: https://doi.org/10.1007/978-3-540-72432-2_9
Publisher Name: Springer, Berlin, Heidelberg
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