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 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.

Breast Cancer Diagnosis with Machine Learning Techniques

“…Rahman et.al. [21] Wisconsin dataset was used in their study and they studied the dataset with 60%-40%, 70%-30%, and 80%-20% training-test rates. The highest success was achieved in the application where they divided 70%-30% with an accuracy of 98.5%.…”

“…[20] using SVM, NB, RF, and KNN methods and tested that performance on the WBCD data set, achieved 93.865%, 94.74%, 96.49%, 91.23%, and 98.24% accuracy rates. Rahman et al [21] created an ANN classification model for the classification of breast cancer. They determined the number of neurons in a single hidden layer of the ANN as 15 using the Taguchi method.…”

Enhancement of an Optimized Key for Database Sanitization to Ensure the Security and Privacy of an Autism Dataset

“…Although some research works [42][43][44][45][46] employed symmetry-adapted cutting-edge technologies for diagnosing different human disabilities and illnesses, maintaining accuracy and privacy without delays, there is also an imperative urge for data security and privacy.…”

Determining the number of hidden layer and hidden neuron of neural network for wind speed prediction

“…Artificial neural networks, as a part of artificial intelligence methods have been widely used in many fields for prediction purposes( Bakhashwain & Sagheer, 2021 ; Rahman et al, 2021 ; Zhao & Liu, 2021 ), including wind speed prediction. One of the crucial factor for designing a neural network is its structure or topology, namely determining the hidden layers number and the hidden neurons number for corresponding hidden layer because it is closely related to the topological performance ( Aggarwal, 2018 ; Koutsoukas et al, 2017 ; Nitta, 2017 ), but until now topology determination is still a complex and difficult problem ( Lee et al, 2018 ; Naitzat, Zhitnikov & Lim, 2020 ; Rahman et al, 2021 ). Topology is one of the important hyperparameters in neural networks.…”

“…Determining the topology that does not match the needs caused overfitting or underfitting in neural networks. Several researchers have conducted research to determine the neural network topology in various ways: methods based solely on the number of input and output attributes ( Sartori & Antsaklis, 1991 ; Tamura & Tateishi, 1997 ), trial and error ( Blanchard & Samanta, 2020 ; Madhiarasan, 2020 ; Madhiarasan & Deepa, 2016 ; Madhiarasan & Deepa, 2017 ; Şen & Özcan, 2021 ) , and the rule of thumb ( Bakhashwain & Sagheer, 2021 ; Carballal et al, 2021 ; Rahman et al, 2021 ).…”