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ORIGINAL RESEARCH article
Front. Cardiovasc. Med.
Sec. General Cardiovascular Medicine
Volume 11 - 2024 |
doi: 10.3389/fcvm.2024.1454642
Application of Machine Learning Algorithms in Predicting Carotid Artery Plaques Using Routine Health Assessments
Provisionally accepted- Hainan Medical University, Haikou, China
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, MLP, NBM, RF, SVM, XGBoost and MLP, 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, 6381 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 (LightGBM) had the highest AUC value (85.4%). Moreover, age, systolic blood pressure (SBP), gender, low-density lipoprotein cholesterol (LDL-C), and total cholesterol (CHOL) were the top five predictors of carotid plaque formation. Conclusions: This study demonstrated the feasibility of predicting carotid plaque risk using ML algorithms. ML offers effective tools for improving public health monitoring and risk assessment, with the potential to improve primary care and community health by identifying high-risk individuals and enabling proactive healthcare measures and resource optimization.
Keywords: carotid plaque, Cardiovascular Diseases, risk prediction, machine learning, healthcare
Received: 25 Jun 2024; Accepted: 28 Aug 2024.
Copyright: © 2024 Wei, Tao, Geng, Ning, Li and Bi. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
* Correspondence:
Junlong Tao, Hainan Medical University, Haikou, China
Yifan Geng, Hainan Medical University, Haikou, China
Yi Ning, Hainan Medical University, Haikou, China
Weixia Li, Hainan Medical University, Haikou, China
Bo Bi, Hainan Medical University, Haikou, China
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