Machine Learning Diagnostic Model for Early Stage NSTEMI: Using hs-cTnI 1/2h Changes and Multiple Cardiovascular Biomarkers

Figures

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  Figure 1. Distribution of log-transformed biomarker values at admission. Histograms represent the log-transformed values of the cardiac biomarkers CK-MB, Beckman-hs-cTnI, Myoglobin (MYO), and NT-proBNP measured at admission. The blue density line overlays the histogram, illustrating the probability distribution of each biomarker. The x-axis represents the log-transformed values, while the y-axis indicates the density.

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  Figure 2. Correlation plots between log-transformed cardiac biomarker values at admission. Scatter plots display pairwise correlations between the log-transformed values of CK-MB, Beckman-hs-cTnI, Myoglobin(MYO), and NT-proBNP at admission. The red line represents a locally weighted scatterplot smoothing (LOESS) fit, while the R values and p-values indicate the strength and significance of the correlation. Each plot shows a distinct relationship between two biomarkers, highlighting both linear and non-linear patterns of association across the dataset.

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  Figure 3. ROC and Precision–Recall curves for 3-fold cross-validation across (a) Beckman_0h, (b) Hybiome_0h, (c) Beckman_1/2h, and (d) Hybiome_1/2h models. ROC Curves (left panels): The blue line represents the mean ROC curve across the three folds, while the shaded area around the mean curve denotes the standard deviation (SD). This highlights the model’s consistency and reliability in distinguishing between classes across different subsets of data. The Area Under the Curve (AUC) values are also provided for each fold, with the overall mean AUC and its SD indicated, demonstrating the model’s discriminative power. Precision–Recall Curves (Right Panels): Like the ROC curves, the mean Precision–Recall curve is illustrated with a blue line, and the shaded region represents the standard deviation (SD). The Average Precision (AP) values for each fold are provided, alongside the mean AP and its SD.

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  Figure 4. The bar plots display the mean absolute SHAP values of features calculated from models trained using clinical data to predict outcomes of UA and NSTEMI. The left plot represents the feature importance for the Beckman_0h model, while the right plot corresponds to the Beckman_1/2h model. Blue bars indicate the contribution to predicting UA, and red bars show the contribution to predicting NSTEMI.