Intelligent diagnosis of Kawasaki disease from real-world data using interpretable machine learning models

作者：Yifan Duan, Ruiqi Wang, Zhilin Huang, Haoran Chen, Mingkun Tang, Jiayin Zhou, Zhengyong Hu, Wanfei Hu, Zhenli Chen, Qing Qian, Haolin Wang · 发表于：Hellenic Journal of Cardiology · 年份：2024 · DOI：10.1016/j.hjc.2024.08.003 · 被引用次数：9 · 研究领域：Digital Imaging for Blood Diseases、COVID-19 diagnosis using AI、Statistical Methods in Epidemiology

OBJECTIVE: This study aimed to leverage real-world electronic medical record data to develop interpretable machine learning models for diagnosis of Kawasaki disease while also exploring and prioritizing the significant risk factors. METHODS: A comprehensive study was conducted on 4087 pediatric patients at the Children's Hospital of Chongqing, China. The study collected demographic data, physical examination results, and laboratory findings. Statistical analyses were performed using IBM SPSS Statistics, Version 26.0. The optimal feature subset was used to develop intelligent diagnostic prediction models based on the Light Gradient Boosting Machine, Explainable Boosting Machine (EBM), Gradient Boosting Classifier (GBC), Fast Interpretable Greedy-Tree Sums, Decision Tree, AdaBoost Classifier, and Logistic Regression. Model performance was evaluated in three dimensions: discriminative ability via receiver operating characteristic curves, calibration accuracy using calibration curves, and interpretability through SHAP (SHapley Additive exPlanations) and LIME (Local Interpretable Model-Agnostic Explanations). RESULTS: In this study, Kawasaki disease was diagnosed in 2971 participants. Analysis was conducted on 31 indicators, including red blood cell distribution width and erythrocyte sedimentation rate. The EBM model demonstrated superior performance relative to other models, with an area under the curve of 0.97, second only to the GBC model. Furthermore, the EBM model exhibited t...