Examination of the Effect of Ann and Nlpca Technique on Prediction Performance in Patients With Breast Tumors

Abstract

Breast cancer is among the most prevalent cancer kinds worldwide. The aim of this study is to examine the effect of combining Artificial Neural Networks and Nonlinear Principal Component Analysis techniques on prediction performance in patients with breast tumors. In the application, a network containing 5 layers, including the input, the coding, the bottleneck, the decoding and the output, was used for the 30 variable data set of 569 breast tumor patients. The training algorithm of choice was the Conjugate Gradient Descent (CGD) algorithm. In this study, artificial neural networks (ANN) and nonlinear principal component analysis were coupled. NLPCA was first applied to dimension reduction in artificial neural networks. Using both the original data set and the decreased size, artificial neural networks were used in the second stage to develop prediction models. By contrasting the performance of these two prediction models with one another, the outcomes were understood. 96.37% of the variation was explained by the two fundamental components that were found using NLPCA. The prediction models developed for the original data set and the dimension-reduced data set have R2 values of 91% and 87%, respectively. The advantages of the NLPCA and ANN combination for breast tumor patients are demonstrated by this study. It is believed that utilizing principal components as inputs can cut down on complexity and extraneous information.