Machine learning integration in microRNA-based markers for cardiovascular diseases: A systematic review

Table 3 Key diagnostic performance metrics of machine learning models integrating microRNAs for cardiovascular disease diagnosis

Ref.	Best model(s)	AUC-ROC (range or best)	Accuracy (best reported)	Sensitivity (best)	Specificity (best)	Notes on interpretation
Kayvanpour et al[8], 2021	ANN was the best-performing model (SVM, kNN, LDA, and RF also performed highly)	0.87-0.99	0.87-0.96	0.87-0.95	0.87-1.00	Good internal discriminative performance, but no external validation; risk of optimistic bias
Ren et al[13], 2024	Regularized LR (LASSO/SCAD)	0.5 to approximately 1.0	NR	NR	NR	Focus on miRNA identification; no full diagnostic model metrics
Samadishadlou et al[14], 2023	Two-layer architecture utilizing SVM (RBF)	0.96 (layer 2) to 1.0 (layer 1)	0.96 (overall two-layer architecture)	0.97 (layer 2) to 1.0 (layer 1)	0.86 (layer 2) to 1.0 (layer 1)	Good internal performance (two-layer approach isolated healthy samples perfectly), but no external validation cohort utilized
Samadishadlou et al[15], 2024	HVE (aggregating SVM, GB, and XGB)	0.83 (HVE on test set)	0.86	1.00	0.67	Very small test set (14 samples total: 8 MI, 6 healthy) limits reliability; platform differences between training and test sets impacted individual model performance
Reel et al[16], 2025	LMT/LogitBoost (along with SL and SMO)	0.80-0.90	0.71-0.89 (balanced accuracy)	0.43-0.95	0.83-1.00	Moderate-large sample; balanced accuracy used
Sajid et al[17], 2024	AdaBoost (for miRNA biomarkers) and GB (for atherosclerosis inflammatory biomarkers)	0.88-0.95 (CV)/0.76-0.93 (hold-out)	0.87-0.90 (CV)/0.78-0.96 (hold-out)	0.88-0.92 (CV)/0.71-0.86 (hold-out)	0.96-1.00 (CV)/0.81-1.00 (hold-out)	Moderate sample; strong internal metrics but no external validation
Yerukala Sathipati et al[18], 2025	RF/XGB	0.76-0.83	0.73-0.80	0.75-0.87	0.71	Very small sample, high risk of overfitting, though external validation was performed
Jusic et al[19], 2023	SVM	0.90	0.87	0.83	0.91	Moderate sample; internal only
Errington et al[20], 2021	RF, XGB and Ensemble model	0.82-0.85	0.81-0.83	0.86-0.91	0.64-0.71	Study with external validation, more reliable estimates

HVE: Hard voting ensemble; CV: Cross validation; SVM: Support vector machines; ROC: Receiver operating characteristic; AUC: Area under the curve; ANN: Artificial neural networks; kNN: K-nearest neighbors; RF: Random forests; LR: Regularized logistic regression; LASSO: Least absolute shrinkage and selection operator; SCAD: Smoothly clipped absolute deviation; miRNA: MicroRNA; RBF: Radial basis function; XGB: XGBoost; GB: Gradient boosting; LMT: Logistic model tree; SL: Simple logistic; SMO: Sequential minimal optimization; MI: Myocardial infarction; LDA: Linear discriminant analysis; NR: Not reported.