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Background <p>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.</p> Methods <p>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.</p> Results <p>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.</p> Conclusions <p>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.</p>

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

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2026年3月25日

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  • Wenzhuang Li,
  • Kaiming Gao,
  • Haihong Zhang,
  • Weidong Xia,
  • Bilali Balajiang,
  • Hongguang Wang,
  • Xiaoguang Tong

摘要

Background

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.

Methods

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.

Results

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.

Conclusions

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.