Explainable machine learning-driven identification of heart failure biomarkers: a multi-model feature selection approach with SHAP-based interpretability.

Heart failure (HF) remains a major clinical challenge due to its complex pathophysiology and the limitations of existing biomarkers. In this study, we developed a robust machine learning (ML) framework to identify novel transcriptomic signatures of HF. Three GEO RNA-seq datasets (GSE141910, GSE198945, GSE263297) were integrated and harmonized, followed by a "split-first" strategy for training (70%) and testing (30%). We employed a triphasic feature selection process-integrating LASSO, Random Forest (RF), and SVM-RFE-to identify candidate genes. A 10-model ensemble system was evaluated using Leave-One-Study-Out Cross-Validation (LOSO-CV) and interpreted via SHAP values. Findings were validated using an independent external cohort (GSE135055) and experimental RT-qPCR in a local clinical cohort. Three potential biomarkers-FNDC1, LPCAT3, and TIMP2-were prioritized. FNDC1 and TIMP2 were significantly upregulated, while LPCAT3 was suppressed in HF tissues (p < 0.001), patterns consistently confirmed by qPCR. The ML models demonstrated high diagnostic stability, with peak LOSO-CV AUCs reaching 0.973 and maintaining robustness in external validation (AUC up to 0.876). SHAP analysis identified FNDC1 as the most influential predictor. Functional enrichment linked these signatures to extracellular matrix remodeling and lipid metabolism. These findings suggest that FNDC1, LPCAT3, and TIMP2 may serve as potential biomarkers associated with the pathological mechanisms of HF.