Predicting coronary artery lesions in patients with Kawasaki disease in China using a machine-learning algorithm: a retrospective cohort study.
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.
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.
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.
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.
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.
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.
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.
Authors
Li Li, Zhou Zhou, Fan Fan, Zhao Zhao, Xu Xu, Li Li, Ma Ma, Sun Sun, Wu Wu, Wang Wang, Wang Wang
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