Application of machine learning algorithms in predicting carotid artery plaques using routine health assessments

作者：Yuting Wei, Junlong Tao, Yifan Geng, Yi Ning, Weixia Li, Bo Bi · 发表于：Frontiers in Cardiovascular Medicine · 年份：2024 · DOI：10.3389/fcvm.2024.1454642 · 被引用次数：5 · 研究领域：Cardiovascular Health and Disease Prevention、Artificial Intelligence in Healthcare、Cerebrovascular and Carotid Artery Diseases

Background: Cardiovascular diseases (CVD) constitute a grave global health challenge, engendering significant socio-economic repercussions. Carotid artery plaques (CAP) are critical determinants of CVD risk, and proactive screening can substantially mitigate the frequency of cardiovascular incidents. However, the unequal distribution of medical resources precludes many patients from accessing carotid ultrasound diagnostics. Machine learning (ML) offers an effective screening alternative, delivering accurate predictions without the need for advanced diagnostic equipment. This study aimed to construct ML models that utilize routine health assessments and blood biomarkers to forecast the onset of CAP. Methods: In this study, seven ML models, including LightGBM, LR, multi-layer perceptron (MLP), NBM, RF, SVM, and XGBoost, were used to construct the prediction model, and their performance in predicting the risk of CAP was compared. Data on health checkups and biochemical indicators were collected from 19,751 participants at the Beijing MJ Health Screening Center for model training and validation. Of these, 6,381 were diagnosed with CAP using carotid ultrasonography. In this study, 21 indicators were selected. The performance of the models was evaluated using the accuracy, sensitivity, specificity, positive predictive value (PPV), negative predictive value (NPV), F1 score, and area under the curve (AUC) value. Results: Among the seven ML models, the light gradient boosting machine ...