Explainable Multi-Class Classification Based on Integrative Feature Selection for Breast Cancer Subtyping

Souham Meshoul (), Ali Batouche (), Hadil Shaiba and Shiekhah AlBinali
Additional contact information
Souham Meshoul: Department of Information Technology, College of Computer and Information Sciences, Princess Nourah Bint Abdulrahman University, P.O. Box 84428, Riyadh 11671, Saudi Arabia
Ali Batouche: Department of Computer and Information Sciences, Northumbria University, London E1 7HT, UK
Hadil Shaiba: Department of Computer Science, College of Computer and Information Sciences, Princess Nourah Bint Abdulrahman University, P.O. Box 84428, Riyadh 11671, Saudi Arabia
Shiekhah AlBinali: College of Computer and Information Sciences, Princess Nourah Bint Abdulrahman University, P.O. Box 84428, Riyadh 11671, Saudi Arabia

Mathematics, 2022, vol. 10, issue 22, 1-27

Abstract: Breast cancer subtype classification is a multi-class classification problem that can be handled using computational methods. Three main challenges need to be addressed. Consider first the high dimensionality of the available datasets relative to the extremely small number of instances. Second, the integration of different levels of data makes the dimensionality problem even more challenging. The third challenging issue is the ability to explain the predictions provided by a machine learning model. Recently, several deep learning models have been proposed for feature extraction and classification. However, due to the small size of the datasets, they were unable to achieve satisfactory results, particularly in multi-class classification. Aside from that, explaining the impact of features on classification has not been addressed in previous works. To cope with these problems, we propose a multi-stage feature selection (FS) framework with two data integration schemes. Using multi-omics data, four machine learning models, namely support vector machines, random forest, extra trees, and XGBoost, were investigated at each level. The SHAP framework was used to explain how specific features influenced classification. Experimental results demonstrated that ensemble models with early integration and two stage feature selection improved results compared to baseline experiments and to state-of-the art methods. Furthermore, more explanations regarding the implications of the main relevant features in the predictions are provided, which could serve as a baseline for future biological investigations.

Keywords: feature selection; multi-class classification; explainable AI; breast cancer sub-typing; multi-omics data; SHAP plots (search for similar items in EconPapers)
JEL-codes: C (search for similar items in EconPapers)
Date: 2022
References: View complete reference list from CitEc
Citations:

Downloads: (external link)
https://www.mdpi.com/2227-7390/10/22/4271/pdf (application/pdf)
https://www.mdpi.com/2227-7390/10/22/4271/ (text/html)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:gam:jmathe:v:10:y:2022:i:22:p:4271-:d:973315

Access Statistics for this article

Mathematics is currently edited by Ms. Emma He

More articles in Mathematics from MDPI
Bibliographic data for series maintained by MDPI Indexing Manager ().