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Breast cancer is the second dangerous cancer in the world. Breast cancer data often contains more redundant information. Redundant information makes the breast cancer auxiliary diagnosis less accurate and time consuming. Dimension reduction algorithm combined with machine learning can solve these problems well. This paper proposes the single parameter decision theoretic rough set (SPDTRS) combined with the probability neural network (PNN) model for breast cancer diagnosis. We find that when the parameter value of SPDTRS is 2.5 and the SPREAD value is 0.75, the number of 30 attributes of the original breast cancer data dropped to 12, the accuracy of the SPDTRS-PNN model training set is 99.25%, the accuracy of the test set is 97.04%, and the test time is 0.093 s. The experimental results show that the SPDTRS-PNN model can improve the ac-curacy of breast cancer recognition, reduce the time required for diagnosis.

Background:Breast cancer is the second dangerous cancer in the world. How to identify breast cancer quickly and accurately is of great help to the treatment of breast cancer. Breast cancer data often contains more redundant information. Redundant information makes the breast cancer auxiliary diagnosis less accurate and time-consuming. Dimension reduction algorithm combined with machine learning can solve these problems well. Methods:This paper proposes the single-parameter decision-theoretic rough set (SPDTRS) combined with the probability neural network (PNN) model for breast cancer diagnosis. We structure fifteen models by combining five dimensionality reduction algorithms with three classification algorithms. We compared the accuracy and test time of fifteen models under different parameters or dimensions. We find that when the parameter value of SPDTRS is 2.5, the classification effect of SPDTRS combined with PNN is better. At this point, the number of 30 attributes of the original breast cancer data dropped to 12. Then the SPDTRS-PNN model is further optimized. We compared the accuracy and test time of the model under different SPREAD values in PNN, and established a better SPDTRS-PNN model.Result:We find the parameter value of SPDTRS is 2.5 and the SPREAD value is 0.75, the accuracy of the SPDTRS-PNN model training set is 99.25%, the accuracy of the test set is 97.04%, and the test time is 0.093s.Conclusion:The experimental results show that the SPDTRS-PNN model can improve the accuracy of breast cancer recognition, reduce the time required for diagnosis, and achieve rapid and accurate breast cancer diagnosis.