基于聚类分析的川崎病亚组冠状动脉病变预测模型表格数据

数据集获取（Figshare）

• 源站标题：Table 4_A universal model for predicting coronary artery lesions in subgroups of kawasaki disease in China: based on cluster analysis.docx
• DOI：10.3389/fcvm.2025.1532768.s004
• 发布日期：2025-03-12
• 文件数：1 个
• 文件总体积：19.01 KB
• 源站页面：https://frontiersin.figshare.com/articles/dataset/Table_4_A_universal_model_for_predicting_coronary_artery_lesions_in_subgroups_of_kawasaki_disease_in_China_based_on_cluster_analysis_docx/28580723

命令行下载

curl -L -o "Table 4_A universal model for predicting coronary artery lesions in subgroups of kawasaki disease in China: based on cluster analysis.docx" "https://ndownloader.figshare.com/files/52936817"

文件清单

• Table 4_A universal model for predicting coronary artery lesions in subgroups of kawasaki disease in China: based on cluster analysis.docx（19.01 KB）

文件总体积 19.01 KB（源站 API 实测）。

数据加载示例（图像类）

from PIL import Image
import glob, os

files = (glob.glob(os.path.join(path, "**", "*.png"), recursive=True)
       + glob.glob(os.path.join(path, "**", "*.jpg"), recursive=True)
       + glob.glob(os.path.join(path, "**", "*.tif"), recursive=True))
print("图像文件数:", len(files))
img = Image.open(files[0]); print("尺寸/模式:", img.size, img.mode)

# torchvision Dataset 方式:
# from torchvision import datasets
# ds = datasets.ImageFolder(path)  # 要求 子目录=类别

目录组织与标注格式以源站说明和下载后实际文件为准。

数据说明

ObjectiveCoronary artery lesions (CAL) represent the most severe complication of Kawasaki disease (KD). Currently, there is no standardized method for predicting CAL in KD, and the predictive effectiveness varies among different KD patients. Therefore, our study aims to establish distinct predictive models for CAL complications based on the characteristics of different clusters.
MethodsWe employed principal component clustering analysis to categorize 1,795 KD patients into different clustered subgroups. We summarized the characteristics of each cluster and compared the occurrence of CAL components within each cluster. Additionally, we utilized LASSO analysis to further screen for factors associated with CAL. We then constructed CAL predictive models for each subgroup using the selected factors and conducted preliminary validation and assessment.
ResultsThrough PCA analysis, we identified three clusters in KD. We developed predictive models for each of the three clusters. The AUCs of the three predictive models were 0.789 (95% CI: 0.732–0.845), 0.894 (95% CI: 0.856–0.932), and 0.773 (95% CI: 0.727–0.819), respectively, all demonstrating good predictive performance.
ConclusionOur study identified the existence of three clusters among KD patients. We developed KD-related CAL predictive models with good predictive performance for each cluster with distinct characteristics. This provides reference for individualized precision treatment of KD patients and aids in the health management of coronary arteries in KD.

许可

CC BY 4.0

引用

学术使用请引用 DOI 10.3389/fcvm.2025.1532768.s004（source: https://frontiersin.figshare.com/articles/dataset/Table_4_A_universal_model_for_predicting_coronary_artery_lesions_in_subgroups_of_kawasaki_disease_in_China_based_on_cluster_analysis_docx/28580723）。