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Predicting coronary artery lesions in patients with Kawasaki disease in China using a machine-learning algorithm: a
Xuemei Li1, Zihan Zhou1, Jingyi Fan1
1Department of Pediatrics, Shengjing Hospital of China Medical University, Shenyang, Liaoning, China.
Insights
This study developed a machine-learning model to predict coronary artery lesions (CAL) in Kawasaki disease (KD) patients in China. The CatBoost model shows high accuracy, aiding personalized treatment strategies for better outcomes.
Area of Science:
- Cardiology
- Pediatrics
- Machine Learning in Healthcare
Background:
- Kawasaki disease (KD) is a critical pediatric illness.
- Coronary artery lesions (CAL) are a major complication of KD.
- Identifying risk factors for CAL is crucial for timely intervention.
Purpose of the Study:
- To analyze risk factors associated with CAL in KD patients.
- To develop and validate predictive models for CAL in KD.
- To leverage machine learning for improved KD patient management.
Main Methods:
- Retrospective cohort study of KD patients.
- Utilized 41 demographic, clinical, and laboratory parameters.
- Employed LASSO regression for variable selection and CatBoost for predictive modeling.
- 10-fold cross-validation and ROSE oversampling for model training and validation.
Main Results:
- The CatBoost model demonstrated high predictive performance with an AUC of 0.953.
- Achieved sensitivity of 0.908, specificity of 0.860, and accuracy of 0.883 on the training set.
- Internal and external validation confirmed the model's robustness and generalizability.
Conclusions:
- A novel machine-learning model effectively predicts CAL risk in KD patients.
- This tool can assist clinicians in developing personalized treatment plans.
- Improved prediction can lead to better patient outcomes and reduced CAL incidence.
Background:
This study aimed to analyze the risk factors of coronary artery lesions (CAL) in patients with Kawasaki disease (KD) and establish predictive models for CAL in patients with KD.
Methods:
This retrospective cohort study included KD patients admitted to Shengjing Hospital of China Medical University, collecting data on 41 demographic, clinical, and laboratory parameters. LASSO regression identified key predictive variables. The dataset was split into 70% training and 30% validation. Ten models were trained using 10-fold cross-validation, with the training set balanced through ROSE oversampling. Model performance was assessed using the area under the receiver operating characteristic curve (AUC), sensitivity, specificity, and accuracy.
Results:
The CatBoost algorithm achieved the best results: AUC, 0.953; sensitivity, 0.908; specificity, 0.860; and accuracy, 0.883. Internal validation results were as follows: AUC, 0.874; sensitivity, 0.721; specificity, 0.848; accuracy, 0.837. External validation results were as follows: AUC, 0.876.sensitivity, 0.894; specificity, 0.954.
Conclusions:
We present a machine-learning model that predicts the risk of CAL in patients with KD in China, aiding doctors in creating personalized treatment strategies to improve outcomes.
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