Artificial neural network with Taguchi method for robust classification model to improve classification accuracy of breast cancer

Md Akizur Rahman; Ravie Chandren Muniyandi; Dheeb Albashish; Md Mokhlesur Rahman; Opeyemi Lateef Usman

doi:10.7717/peerj-cs.344

Artificial neural network with Taguchi method for robust classification model to improve classification accuracy of breast cancer

PeerJ Comput Sci. 2021 Jan 25:7:e344. doi: 10.7717/peerj-cs.344. eCollection 2021.

Authors

Md Akizur Rahman¹, Ravie Chandren Muniyandi¹, Dheeb Albashish², Md Mokhlesur Rahman¹, Opeyemi Lateef Usman¹

Affiliations

¹ Research Center for Cyber Security, Faculty of Information Science and Technology, Universiti Kebangsaan Malaysia, 43600 Bangi, Selangor, Malaysia.
² Computer Science Department, Prince Abdullah Bin Ghazi Faculty of Information and Communication Technology, Al-Balqa Applied University, Salt, Jordan.

Abstract

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.

Keywords: Artificial neural network; Breast cancer classification; Hidden layer; Taguchi method.

Grants and funding

This research was supported by a grant from the Universiti Kebangsaan Malaysia (UKM), UKM Grant Code: GGP-2019-023). The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.