Construction and Validation of Integrated Traditional Chinese and Western Medicine Risk Prediction Model for Carotid Artery Plaques in 3 009 Individuals with High-risk of Stroke
10.13422/j.cnki.syfjx.20250912
- VernacularTitle:3 009例中风病高危人群颈动脉斑块中西医结合风险预测模型构建及验证
- Author:
Shuqi QIN
1
;
Xiangyu GUO
2
;
Weihao YANG
1
;
Yang CHEN
1
;
Ying YU
1
;
Limin HE
1
;
Jialin WANG
2
Author Information
1. The Second Clinical Medical College of Beijing University of Chinese Medicine， Beijing 100078，China
2. Dongfang Hospital of Beijing University of Chinese Medicine， Beijing 100078，China
- Publication Type:Journal Article
- Keywords:
carotid artery plaque;
prediction model;
traditional Chinese medicine syndrome element;
stroke
- From:
Chinese Journal of Experimental Traditional Medical Formulae
2026;32(20):242-250
- CountryChina
- Language:Chinese
-
Abstract:
ObjectiveTo construct a risk prediction model for carotid artery plaques in high-risk populations of stroke based on five machine learning methods. MethodsThe clinical information of the high-risk population of stroke was collected. Factor analysis and statistical analysis of syndrome elements were conducted on their traditional Chinese medicine （TCM） symptoms and tongue and pulse manifestations. On the basis of the results of factor analysis and variable screening， five machine learning methods-classification and regression tree （CART） decision tree， support vector machine （SVM）， back propagation（BP） neural network， logistic regression， and random forest-were used to construct the risk prediction model for carotid artery plaques. ResultsThe most common TCM syndrome elements in the high-risk population of stroke was Qi deficiency. The scores of Qi deficiency， Yin deficiency， and Yang deficiency in the population with carotid artery plaques were higher than those without carotid artery plaques （P<0.05）. The CART decision tree， SVM， logistic regression， BP neural network， and random forest models showed the areas under the receiver operating characteristic （ROC） curves of 0.71， 0.75， 0.76， 0.76， and 0.75， the accuracy rates of 68.94%， 69.27%， 69.44%， 70.10%， and 69.60%， the precision rates of 68.56%， 68.53%， 68.75%， 69.74%， and 69.42%， the recall rates of 68.91%， 67.93%， 67.92%， 68.10%， and 67.31%， and the F1 values of 0.69， 0.68， 0.68， 0.68， and 0.68， respectively. ConclusionAmong the high-risk population of stroke， the most frequently distributed TCM syndrome element is Qi deficiency， with the rest mainly being fire heat， Yin deficiency， Yang deficiency， phlegm dampness， blood stasis， and Qi stagnation. Deficiency syndrome may be a major factor leading to carotid artery plaques in the high-risk population of stroke. The BP neural network model demonstrates better performance in predicting the risk of carotid artery plaques in the high-risk population of stroke. People with carotid artery plaques are more likely to present with symptoms such as a heavy head， dizziness， headache， and thready pulse. The primary community benefits more widely when the BP neural network model is adopted to predict the risk of carotid artery plaques in the high-risk population of stroke over 40 years old.
