Breast cancer diagnosis based on genomic data and extreme learning machine

Abstract

According to cancer.org news in 2018, the most common cancer diagnosed in women is breast, lung, and colorectal cancers. About 30% of all new cancer diagnoses in women refer to breast cancer. Therefore, predicting breast cancer in its early stages stays a controversial challenge. In this study, we focus on DNA methylation gene expression profiles of patients (series GSE32393 NCBI dataset). For dimension reduction, Non-negative matrix factorization (NMF) is employed and combined with a new method called column-splitting. The reason of superiority of NMF algorithm over other popular ones is its usage for both supervised and unsupervised feature matrix transformation into lower dimensional feature matrix. Afterward the main algorithms that are used for classification are extreme learning machine and support vector machine algorithms. The achieved prediction performances are comparable to deep learning models. The best-attained performance has zero error rate on NCBI 137 samples. While it has shown that, the best deep model on the mentioned data has 2.7 error rate.