Meta-analysis of computational methods for breast cancer classification. (26th June 2020)

• Record Type: Journal Article
• Authors: Pham, Tri-Cong; Luong, Chi-Mai; Doucet, Antoine; Hoang, Van-Dung; Tran, Diem-Phuc; Le, Duc-Hau
• Is Part Of: International journal of intelligent information and database systems. Volume 13:Number 1(2020)
• Page Start: 89
• Page End: 111
• Publication Date: 2020-06-26
• Subjects: breast cancer -- gene expression -- multi-class classification -- feature selection -- gene selection -- microarray data -- subtype related gene; Database management -- Computer programs -- Periodicals; Information retrieval -- Computer programs -- Periodicals; Information storage and retrieval systems -- Computer programs -- Periodicals; Artificial intelligence -- Periodicals; Expert systems (Computer science) -- Periodicals; Intelligent agents (Computer software) -- Periodicals; 006.33
• Languages: English
• ISSNs: 1751-5858

Abstract

Millions of women are suffering from breast cancer pressing burden on their shoulders and the global economy. Meanwhile, general treatment methods are applied without considering personalised health and genetic features. Artificial intelligence appears to be a robust method for breast cancer sub-typing. Most of researches have been implemented on binary classification with limited number of data samples. Multi-classification is much more difficult especially on large number of samples. The study aims to use machine learning to find better ways to subtype breast cancer as well as find new disease causative genes which help facilitate more personalised treatment with limited side effect in the future. This study compares the accuracy of three classification methods in combination with eight feature selection methods on a dataset of 2, 682 samples. The study shows that the highest accuracy was 83.96% with the SVM-C005 classifier and percentile feature selection (800 genes). Additionally, our method can predict causative disease genes of breast cancer with four of them known to be associated with breast cancer and 29 promising ones with supporting evidence from the literature. This shows the effectiveness of our research.