Figure 4 | Scientific Reports

Figure 4

From: Machine learning insights into thrombo-ischemic risks and bleeding events through platelet lysophospholipids and acylcarnitine species

Figure 4

Machine learning of cardiovascular risk factors including the platelet lipidome in patients with CAD enhances the diagnostic accuracy of CV risk prediction. Comparison machine-learning algorithms on platelet lipidomics data showing mean absolute error (MAE) of predicting adverse ischemic (A) and major bleeding (B) events in patients with CAD. Least absolute shrinkage and selection operator (LASSO) showed a superior MAE among regression models and was implemented for further analyses. (C) Receiver operator characteristic (ROC) plot of the final LASSO model including platelet lipid subspecies (lysophosphatidylethanolamines (LPE) and acylcarnitines (CAR)) to predict adverse ischemic events. (D) ROC plot of the final LASSO model including platelet LPE/CAR to predict major bleeding events.

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