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Background Major depressive disorder (MDD) is notably underdiagnosed and undertreated due to its complex nature and subjective diagnostic methods. Biomarker identification would help provide a clearer understanding of MDD aetiology. Although machine learning (ML) has been implemented in previous studies to study the alteration of microRNA (miRNA) levels in MDD cases, clinical translation has not been feasible due to the lack of interpretability (ie too many miRNAs for consideration) and stability. Methods This study applied logistic regression (LR) model to the blood miRNA expression profile to differentiate patients with MDD (n= 60) from healthy controls (HCs, n= 60). Embedded (L1-regularised logistic regression) feature selector was utilised to extract clinically relevant miRNAs, and optimized for clinical application. Results Patients with MDD could be differentiated from HCs with the area under the …