Interpretable predictive value of including HDL-2b and HDL-3 in an explainable boosting machine model for multiclass classification of coronary artery stenosis severity in acute myocardial infarction patients.

Abstract

Aims: The aim of this study was to use explainable boosting machine (EBM) to evaluate the predictive value of HDL-2b and HDL-3 levels in comparison with traditional lipid parameters in three-class classification of coronary artery stenosis severity in acute myocardial infarction (AMI) patients.

Methods and results: In this cross-sectional study, 1200 AMI patients were evaluated. HDL subtypes were quantified via microfluidic chip detection, and stenosis severity was assessed via the Gensini scoring system. The Gensini scores were divided into three groups: low group (<36.5), moderate group (36.5-72), and high group (>72). Explainable boosting machine, an interpretable machine learning technique, was employed to assess the predictive value of HDL-2b and HDL-3 compared with traditional lipid markers. Explainable boosting machine was used as the main model in this study, whereas logistic regression, XGBoost, and Random Forest were selected as reference models for predictive performance. Model performance was evaluated using receiver operating characteristic curves. The HDL-3 (%) values were divided into three risk categories: low (>43), moderate (30-43), and high (<30). The incorporation of HDL-2b and HDL-3 levels into lipid profiling significantly increased the group importance scores. The macro-average area under the curve values for the four models were as follows: 0.56 for the logistic model, 0.54 for the EBM model, 0.50 for the Random Forest model, and 0.49 for the XGBoost model.

Conclusion: HDL-3 provides superior predictive value for evaluating coronary artery stenosis severity in AMI patients compared to HDL-2b and other conventional lipid markers.