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Fig. 2 | BioMedical Engineering OnLine

Fig. 2

From: Predicting plaque vulnerability change using intravascular ultrasound + optical coherence tomography image-based fluid–structure interaction models and machine learning methods with patient follow-up data: a feasibility study

Fig. 2

ROC obtained by optimal combinational-factor predictors and machine learning method with best performance for prediction of three morphological indices. a ROC of combinational-factor predictor (LP, critical WSS, cap PWS, cap PWSn and cap WSS) using SVM for ΔLPI prediction. b ROC of combinational-factor predictor (MinCT, MeanCT, critical PWS, cap PWS and cap PWSn) using DA for ΔLPI prediction. c ROC of combinational-factor predictor (MinCT, plaque area and critical PWS) using DA for ΔMPVI prediction

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BioMedical Engineering OnLine

ISSN: 1475-925X

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