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

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 Brea

SUBMITTER: Rahman MA 

PROVIDER: S-EPMC7924699 | biostudies-literature | 2021

REPOSITORIES: biostudies-literature