Utilizing machine learning algorithms for the prediction of carotid artery plaques in a Chinese population

Shuwei Weng; Jin Chen; Chen Ding; Die Hu; Wenwu Liu; Yanyi Yang; Daoquan Peng

doi:10.3389/fphys.2023.1295371

Utilizing machine learning algorithms for the prediction of carotid artery plaques in a Chinese population

Front Physiol. 2023 Nov 6:14:1295371. doi: 10.3389/fphys.2023.1295371. eCollection 2023.

Authors

Shuwei Weng^#^{1

2}, Jin Chen^#^{1

2}, Chen Ding³, Die Hu^{1

2}, Wenwu Liu^{1

2}, Yanyi Yang⁴, Daoquan Peng^{1

2}

Affiliations

¹ Department of Cardiovascular Medicine, The Second Xiangya Hospital, Central South University, Changsha, Hunan, China.
² Research Institute of Blood Lipid and Atherosclerosis, Changsha, Hunan, China.
³ Department of Cardiology, Suzhou Dushu Lake Hospital, Dushu Lake Hospital Affiliated to Soochow University, Medical Center of Soochow University, Suzhou, Jiangsu, China.
⁴ Health Management Center, The Second Xiangya Hospital, Central South University, Changsha, Hunan, China.

^# Contributed equally.

Abstract

Background: Ischemic stroke is a significant global health issue, imposing substantial social and economic burdens. Carotid artery plaques (CAP) serve as an important risk factor for stroke, and early screening can effectively reduce stroke incidence. However, China lacks nationwide data on carotid artery plaques. Machine learning (ML) can offer an economically efficient screening method. This study aimed to develop ML models using routine health examinations and blood markers to predict the occurrence of carotid artery plaques. Methods: This study included data from 5,211 participants aged 18-70, encompassing health check-ups and biochemical indicators. Among them, 1,164 participants were diagnosed with carotid artery plaques through carotid ultrasound. We constructed six ML models by employing feature selection with elastic net regression, selecting 13 indicators. Model performance was evaluated using accuracy, sensitivity, specificity, Positive Predictive Value (PPV), Negative Predictive Value (NPV), F1 score, kappa value, and Area Under the Curve (AUC) value. Feature importance was assessed by calculating the root mean square error (RMSE) loss after permutations for each variable in every model. Results: Among all six ML models, LightGBM achieved the highest accuracy at 91.8%. Feature importance analysis revealed that age, Low-Density Lipoprotein Cholesterol (LDL-c), and systolic blood pressure were important predictive factors in the models. Conclusion: LightGBM can effectively predict the occurrence of carotid artery plaques using demographic information, physical examination data and biochemistry data.

Keywords: LightGBM; carotid artery plaque; machine learning; primary prevention; screening.

Grants and funding

The author(s) declare financial support was received for the research, authorship, and/or publication of this article. This study was supported by National Natural Science Foundation of China (81870336).