What question did this study set out to answer?

The study aims to develop a machine learning model to predict coronary heart disease risk using blood biomarkers in patients with carotid stenosis.

March 27, 2026Open Access

Interpretable machine learning analysis of routine blood biomarkers and derived indicators for predicting coronary heart disease in patients with carotid stenosis

WLWenzhuang LiTianjin Medical University KGKaiming GaoTianjin Medical University HZHaihong ZhangTianjin Medical University

Puntos clave

The study aims to develop a machine learning model to predict coronary heart disease risk using blood biomarkers in patients with carotid stenosis.
Retrospective analysis of clinical data from 723 patients with diagnosed carotid artery stenosis.
Utilized six feature selection methods and 11 machine learning algorithms for model development.
Applied SHAP analysis to interpret model predictions and identify key features.
The random forest algorithm achieved the best performance with an AUC of 0.800.
Key features identified include residual cholesterol, fasting plasma glucose, and monocyte-to-lymphocyte ratio.
SHAP values indicated significant differences across varying degrees of carotid artery stenosis.

Resumen

Coronary heart disease (CHD) and carotid stenosis share similar pathogenic mechanisms such as chronic inflammation, metabolic dysregulation, oxidative stress, and immune imbalance. These conditions are often comorbid. This study aimed to develop and validate an effective machine learning model that uses routine blood biomarkers to predict CHD risk in patients with carotid stenosis. Clinical data from 723 patients diagnosed with carotid artery stenosis between January 2019 and December 2024 were retrospectively collected, including demographic characteristics, hematological and biochemical laboratory parameters, and their derived composite indices. We used six feature selection methods and 11 machine learning (ML) algorithms to construct predictive models, and we systematically compared their performance. To interpret the established predictive model, we applied SHapley Additive exPlanations (SHAP) analysis to visualize and elucidate the risk prediction framework. We identified the intersection of six feature selection methods, screened a total of nine potential predictors related to CHD, and used them to construct a prediction model. Cross-validation results demonstrated that the predictive model based on the random forest (RF) algorithm achieved the best performance among all evaluated algorithms (AUC = 0.800; sensitivity = 0.729; specificity = 0.792; F1 score = 0.733). The SHAP plots for RF indicate that estimating residual cholesterol (RC), fasting plasma glucose (FPG), and monocyte-to-lymphocyte ratio (MLR) are the three most important features for predicting CHD. Significant differences were observed in SHAP values for key biomarkers (including RC, MLR) across varying degrees of carotid artery stenosis. The RF model achieved the best predictive performance in predicting CHD in patients with carotid stenosis. Elevated metabolic and immunoinflammatory markers significantly enhanced the predictive power of the model. Prospective multicenter validation is warranted to confirm its generalizability across diverse populations.

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