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

Table 2 Characteristics of the model used in the studies

Ref.	Models evaluated	Internal validation strategy	External validation	Training dataset	Test/validation dataset	Performance metrics
Kayvanpour et al[8], 2021	LR, kNN, LDA, NB, RF, CT, SVM, XGB, and ANN	The subjects were divided into training and test sets in the ratio of 9:1, respectively. This was repeated 10 times to enable ten-fold cross-validation	None	90% of subjects; 121 samples per split	10% of subjects; 13 samples per split	Accuracy, sensitivity, specificity, and ROC-AUC
Ren et al[13], 2024	Regularized LR using either SCAD or LASSO	Leave-one-out cross-validation	None for the ML model; selected miRNAs were biologically evaluated in matched clinical samples using an ion-exchange membrane sensor platform	24 subjects; 800-miRNA screening library (100%)	None; leave-one-out cross-validation was used because of small sample size	ROC curves and AUC (used to evaluate the selected miRNA combinations)
Samadishadlou et al[14], 2023	A 2-layer architecture utilizing SVM (with linear, polynomial, and RBF kernels), LR, RF, kNN, GB, XGB, and DT models (layer 1 isolated healthy vs not-healthy; layer 2 separated MI vs CAD)	The data was split in a 7:3 ratio into the training and test sets, respectively. A ten-fold cross-validation followed this	None	70% of all the samples	30% of all the samples	AUC-ROC, accuracy, sensitivity, specificity, and confusion matrix
Samadishadlou et al[15], 2024	SVM, GB, XGB and hard voting ensemble model	Done in 2 phases. In miRNA selection: The LASSO method was cross-validated using the dataset 10-fold to select the best miRNA to be used in model development. In model selection, the training dataset was split in a 7:3 ratio into training and validation datasets. The models were then cross-validated 5-fold on the datasets. The best-performing models were then tested on the independent dataset	Performed using an independent dataset (GSE29532)	GSE61741 (62 MI samples and 94 healthy samples)	GSE29532 (8 MI samples and 6 healthy samples)	Accuracy, AUC-ROC, sensitivity, and specificity
Reel et al[16], 2025	J48, NB, IBk, RF, LB, LMT, SL, and SMO	The data was randomly split into training and testing sets in an 8:2 ratio for model development and validation	None	80% of all the samples	20% of all the samples	Balanced accuracy is the primary metric. Other metrics include sensitivity, specificity, AUC-ROC, F1 score and Kappa score
Sajid et al[17], 2024	LR, SVM, nonlinear kNN, tree-based (DT, RF), GB, XGBM, CBoost, ABoost, and ensemble voting	The data subset was first split in an 8:2 ratio into a CV subset and a hold-out subset for final evaluation. The CV subset was then divided into 10 folds. Nine folds were used for training and one-fold for testing, and the process was repeated 10 times. The best models were then tested on the hold-out dataset	None	80% of the 113 subjects (cohort: 58 CAD cases, 55 healthy controls)	20% of the 113 subjects (hold-out subset)	Accuracy, sensitivity, specificity, AUC-ROC, performance evaluation measure, F-statistic, and P values
Yerukala Sathipati et al[18], 2025	kNN, XGB, SVM, and RF	The data was split in a 8:2 ratio into training and validation datasets, respectively	Performed using an independent GEO dataset (GSE222739)	80% of the dataset (n = 12)	20% of the dataset (n = 3)	AUC-ROC, accuracy, specificity, and sensitivity
Jusic et al[19], 2023	RF, SVM, MLP, XGB, kNN, Logit	Hyperparameter tuning used two repeated 10-fold CV. The final model was also evaluated using leave-one-out cross-validation	None	147 subjects (89 from the validation cohort + 58 from the sequenced discovery cohort)	23 subjects (randomly extracted as 20% of the 112-subject validation cohort)	AUC-ROC, balanced accuracy, F1 score, precision, sensitivity, and specificity
Errington et al[20], 2021	RF, Rpart, LASSO, XGB, and Ensemble	The data was split into training and validation data sets. The models were then CV 10-fold in the training dataset	The models were externally validated using publicly available datasets	Two-thirds of the samples	One-third of the samples (validation set)	Sensitivity, specificity, AUC, correct classification rate (accuracy), positive predictive value, and negative predictive value

ML: Machine learning; CV: Cross validation; SVM: Support vector machines; CT: Classification tree; NB: Naïve bayes; LR: Regularized logistic regression; SCAD: Smoothly clipped absolute deviation; LASSO: Least absolute shrinkage and selection operator; RF: Random forests; XGB: XGBoost; GB: Gradient boosting; kNN: K-nearest neighbors; DT: Decision tree models; LB: Logitboost; SL: Simple logistic; LMT: Logistic model tree; SMO: Sequential minimal optimization; CBoost: CatBoost; ABoost: AdaBoost; ANN: Artificial neural networks; Rpart: Regression partition tree; LDA: Linear discriminant analysis; miRNA: MicroRNA; ROC: Receiver operating characteristic; AUC: Area under the curve; RBF: Radial basis function; CAD: Coronary artery disease; MI: Myocardial infarction; XGBM: Extreme gradient boosting machine; MLP: Multilayer perceptron.