A Hybrid Gene Selection Strategy Based on Fisher and Ant Colony Optimization Algorithm for Breast Cancer Classification

Authors

  • Mohammed Hamim ENSAM-Casablanca, University Hassan II, Casablanca, Morocco
  • Ismail El Moudden EVMS-Sentara Healthcare Analytics and Delivery Science Institute, Eastern Virginia Medical School, Norfolk, Va
  • Mohan D Pant School of Health Professions, Eastern Virginia Medical School, Norfolk, Va
  • Hicham Moutachaouik ENSAM-Casablanca, University Hassan II, Casablanca
  • Mustapha Hain ENSAM-Casablanca, University Hassan II, Casablanca

DOI:

https://doi.org/10.3991/ijoe.v17i02.19889

Keywords:

Breast cancer. Gene Selection. Ant Colony Optimization. Microarray technology

Abstract


Breast cancer poses the greatest threat to human life and especially to women's life. Despite the progress made in data mining technology in recent years, the ability to predict and diagnose such fatal diseases based on gene expression data still reveals a limited prediction performance, which may not be surprising since most of the genes in expression data are believed to be irrelevant or redundant. The dimensionality reduction process may be considered as a crucial step to analyze gene expression data, as it can reduce the high dimensionality of the breast cancer datasets, which may result into a better prediction performance of such diseases. The paper suggests a new hybrid approach-based gene selection that combines the filter method and the Ant Colony Optimization algorithm to find the smallest subset of informative genes (genes markers) among 24,481 genes. The proposed approach combines four machine learning algorithms - C5.0 Decision Tree, Support Vector Machines, K-Nearest Neighbors algorithm, and Random Forest Classifier - to classify each of the selected samples (patients) into two classes which have cancer or not.  Compared with existing methods in the literature, experimental results indicate that our proposed gene selection approach achieved globally higher classification accuracies with a relatively smaller number of genes.

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Published

2021-02-12

How to Cite

Hamim, M., El Moudden, I., D Pant, M., Moutachaouik, H., & Hain, M. (2021). A Hybrid Gene Selection Strategy Based on Fisher and Ant Colony Optimization Algorithm for Breast Cancer Classification. International Journal of Online and Biomedical Engineering (iJOE), 17(02), pp. 148–163. https://doi.org/10.3991/ijoe.v17i02.19889

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Papers