利用机器学习算法预测中国人群颈动脉斑块的数据集

数据集获取（Figshare）

• 源站标题：Table1_Utilizing machine learning algorithms for the prediction of carotid artery plaques in a Chinese population.XLS
• DOI：10.3389/fphys.2023.1295371.s002
• 发布日期：2023-11-06
• 文件数：1 个
• 文件总体积：26.62 KB
• 源站页面：https://frontiersin.figshare.com/articles/dataset/Table1_Utilizing_machine_learning_algorithms_for_the_prediction_of_carotid_artery_plaques_in_a_Chinese_population_XLS/24503296

命令行下载

curl -L -o "Table1_Utilizing machine learning algorithms for the prediction of carotid artery plaques in a Chinese population.XLS" "https://ndownloader.figshare.com/files/43047271"

文件清单

• Table1_Utilizing machine learning algorithms for the prediction of carotid artery plaques in a Chinese population.XLS（26.62 KB）

文件总体积 26.62 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

Background: Ischemic stroke is a significant global health issue, imposing substantial social and economic burdens. Carotid artery plaques (CAP) serve as an important risk factor for stroke, and early screening can effectively reduce stroke incidence. However, China lacks nationwide data on carotid artery plaques. Machine learning (ML) can offer an economically efficient screening method. This study aimed to develop ML models using routine health examinations and blood markers to predict the occurrence of carotid artery plaques.
Methods: This study included data from 5,211 participants aged 18–70, encompassing health check-ups and biochemical indicators. Among them, 1,164 participants were diagnosed with carotid artery plaques through carotid ultrasound. We constructed six ML models by employing feature selection with elastic net regression, selecting 13 indicators. Model performance was evaluated using accuracy, sensitivity, specificity, Positive Predictive Value (PPV), Negative Predictive Value (NPV), F1 score, kappa value, and Area Under the Curve (AUC) value. Feature importance was assessed by calculating the root mean square error (RMSE) loss after permutations for each variable in every model.
Results: Among all six ML models, LightGBM achieved the highest accuracy at 91.8%. Feature importance analysis revealed that age, Low-Density Lipoprotein Cholesterol (LDL-c), and systolic blood pressure were important predictive factors in the models.
Conclusion: LightGBM can effectively predict the occurrence of carotid artery plaques using demographic information, physical examination data and biochemistry data.

许可

CC BY 4.0

引用

学术使用请引用 DOI 10.3389/fphys.2023.1295371.s002（source: https://frontiersin.figshare.com/articles/dataset/Table1_Utilizing_machine_learning_algorithms_for_the_prediction_of_carotid_artery_plaques_in_a_Chinese_population_XLS/24503296）。