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.
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