ArticlePeerJ. Computer science2021
Artificial neural network with Taguchi method for robust classification model to improve classification accuracy of breast cancer.
Article in PeerJ. Computer science, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers.
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8 citing papers in PubMed.
- Hybrid metaheuristic optimization for detecting and diagnosing noncommunicable diseases.Scientific reports · 2025Article
- Advanced Brain Tumor Classification in MR Images Using Transfer Learning and Pre-Trained Deep CNN Models.Cancers · 2025Article
- Enhancing breast cancer prediction through stacking ensemble and deep learning integration.PeerJ. Computer science · 2025Article
- Restoring private autism dataset from sanitized database using an optimized key produced from enhanced combined PSO-GWO framework.Scientific reports · 2024Article
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- Ensemble of adapted convolutional neural networks (CNN) methods for classifying colon histopathological images.PeerJ. Computer science · 2022Article
- Determining the number of hidden layer and hidden neuron of neural network for wind speed prediction.PeerJ. Computer science · 2021Article
- Breast lesion identification using feature fusion and multiresolution dual-tree complex wavelet transform.Digital healthArticle
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Authors and funding
5 authors.
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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.
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