Artificial neural networks (ANN) perform well in real-world classification problems. In this paper, a robust classification model using ANN was constructed to enhance the accuracy of breast cancer classification. The Taguchi method was used to determine the suitable number of neurons in a single hidden layer of the ANN. The selection of a suitable number of neurons helps to solve the overfitting problem by affecting the classification performance of an ANN. With this, a robust classification model was then built for breast cancer classification. Based on the Taguchi method results, the suitable number of neurons selected for the hidden layer in this study is 15, which was used for the training of the proposed ANN model. The developed model was benchmarked upon the Wisconsin Diagnostic Breast Cancer Dataset, popularly known as the UCI dataset. Finally, the proposed model was compared with seven other existing classification models, and it was confirmed that the model in this study had the best accuracy at breast cancer classification, at 98.8%. This confirmed that the proposed model significantly improved performance.

中文翻译：

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Taguchi方法的人工神经网络用于鲁棒分类模型以提高乳腺癌分类的准确性

人工神经网络（ANN）在现实世界中的分类问题中表现出色。在本文中，构建了使用ANN的鲁棒分类模型，以提高乳腺癌分类的准确性。Taguchi方法用于确定ANN单个隐藏层中合适的神经元数量。适当数量的神经元的选择有助于通过影响ANN的分类性能来解决过拟合问题。以此，然后建立了用于乳腺癌分类的鲁棒分类模型。根据Taguchi方法的结果，本研究中为隐蔽层选择的神经元的合适数目为15，这被用于训练所提出的ANN模型。所开发的模型以威斯康星州诊断性乳腺癌数据集（通常被称为UCI数据集）为基准。最后，将提出的模型与其他七个现有分类模型进行了比较，证实该研究模型在乳腺癌分类中具有最高的准确性，为98.8％。这证实了所提出的模型显着改善了性能。