A stacking ensemble model for predicting the occurrence of carotid atherosclerosis
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View abstract on PubMed
Summary
This summary is machine-generated.A new ensemble machine learning model accurately predicts carotid atherosclerosis (CAS) risk, identifying high-risk individuals. This approach highlights the importance of endocrine factors in CAS development for preventing cardiovascular events.
Area Of Science
- Cardiovascular Medicine
- Machine Learning
- Endocrinology
Background
- Carotid atherosclerosis (CAS) is a major risk factor for cardio-cerebrovascular events.
- Predicting CAS occurrence is crucial for preventative strategies.
Purpose Of The Study
- To enhance the prediction of CAS occurrence using stacking ensemble machine learning.
- To incorporate a wide range of predictors, including endocrine-related markers.
Main Methods
- Developed five individual prediction models (LR, RF, SVM, XGBoost, GBDT) for CAS.
- Integrated base models using a stacking ensemble algorithm to improve prediction and address overfitting.
- Utilized SHAP value method for in-depth analysis of variable importance, focusing on endocrine factors.
Main Results
- The ensemble model achieved superior performance over individual models, with AUCs of 0.893 (testing) and 0.861 (validation).
- Optimal accuracy, precision, recall, and F1 score were observed in the validation set.
- Carotid stenosis, age, and endocrine-related factors were identified as significant predictors.
Conclusions
- The ensemble model demonstrates enhanced accuracy and generalizability in predicting CAS risk.
- This approach underscores the critical role of endocrine dysfunctions in CAS development.
- The model serves as a promising tool for identifying high-risk individuals to prevent CAS and related diseases.
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