Rahman Md Akizur, Muniyandi Ravie Chandren, Albashish Dheeb, Rahman Md Mokhlesur, Usman Opeyemi Lateef
Research Center for Cyber Security, Faculty of Information Science and Technology, Universiti Kebangsaan Malaysia, 43600 Bangi, Selangor, Malaysia.
Computer Science Department, Prince Abdullah Bin Ghazi Faculty of Information and Communication Technology, Al-Balqa Applied University, Salt, Jordan.
PeerJ Comput Sci. 2021 Jan 25;7:e344. doi: 10.7717/peerj-cs.344. eCollection 2021.
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.
人工神经网络（ANN）在实际的分类问题中表现出色。在本文中，构建了一个使用ANN的稳健分类模型，以提高乳腺癌分类的准确性。田口方法用于确定ANN单个隐藏层中合适的神经元数量。选择合适数量的神经元有助于通过影响ANN的分类性能来解决过拟合问题。据此，构建了一个用于乳腺癌分类的稳健分类模型。基于田口方法的结果，本研究中为隐藏层选择的合适神经元数量为15，用于所提出的ANN模型的训练。所开发的模型以威斯康星诊断乳腺癌数据集（通常称为UCI数据集）为基准。最后，将所提出的模型与其他七个现有的分类模型进行比较，结果证实本研究中的模型在乳腺癌分类方面具有最佳准确性，达到98.8%。这证实了所提出的模型显著提高了性能。
