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Haibo He, and Garcia, E.A. (2009) Learning from Imbalanced Data. IEEE Transactions on Knowledge and Data Engineering, 21, 1263-1284.
https://doi.org/10.1109/tkde.2008.239

has been cited by the following article:

• TITLE: Machine Learning-Based Selection of Key miRNA Biomarkers for Breast Cancer Diagnostics

  AUTHORS: Abderrahim Chafik

  KEYWORDS: RNA, Breast Cancer, Machine Learning, Feature Selection, Random Forest

  JOURNAL NAME: Open Journal of Applied Sciences, Vol.15 No.3, March 17, 2025

  ABSTRACT: MicroRNAs (miRNAs) play a pivotal role in gene expression regulation and are closely linked to cancer development. In this study, we employ machine learning techniques to identify critical miRNA biomarkers for breast cancer diagnostics using a dataset of 941 patient samples with 1,882 miRNA features. By addressing class imbalance and applying robust feature selection, we developed an optimized Random Forest model that achieved a perfect classification accuracy of 1.0. Analyzing feature importance revealed 51 miRNAs as potential biomarkers, offering a valuable panel for precision diagnostics and personalized treatment strategies.