中国社区和初级保健环境中胰岛素敏感性评估的机器学习算法开发数据集

数据集获取（Figshare）

• 源站标题：Table_1_Development and validation of machine learning-augmented algorithm for insulin sensitivity assessment in the community and primary care settings: a population-based study in China.docx
• DOI：10.3389/fendo.2024.1292346.s001
• 发布日期：2024-01-25
• 文件数：1 个
• 文件总体积：18.70 KB
• 源站页面：https://frontiersin.figshare.com/articles/dataset/Table_1_Development_and_validation_of_machine_learning-augmented_algorithm_for_insulin_sensitivity_assessment_in_the_community_and_primary_care_settings_a_population-based_study_in_China_docx/25059005

命令行下载

curl -L -o "Table_1_Development and validation of machine learning-augmented algorithm for insulin sensitivity assessment in the community and primary care settin.docx" "https://ndownloader.figshare.com/files/44223188"

文件清单

• Table_1_Development and validation of machine learning-augmented algorithm for insulin sensitivity assessment in the community and primary care settin.docx（18.70 KB）

文件总体积 18.70 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

ObjectiveInsulin plays a central role in the regulation of energy and glucose homeostasis, and insulin resistance (IR) is widely considered as the “common soil” of a cluster of cardiometabolic disorders. Assessment of insulin sensitivity is very important in preventing and treating IR-related disease. This study aims to develop and validate machine learning (ML)-augmented algorithms for insulin sensitivity assessment in the community and primary care settings.
MethodsWe analyzed the data of 9358 participants over 40 years old who participated in the population-based cohort of the Hubei center of the REACTION study (Risk Evaluation of Cancers in Chinese Diabetic Individuals). Three non-ensemble algorithms and four ensemble algorithms were used to develop the models with 70 non-laboratory variables for the community and 87 (70 non-laboratory and 17 laboratory) variables for the primary care settings to screen the classifier of the state-of-the-art. The models with the best performance were further streamlined using top-ranked 5, 8, 10, 13, 15, and 20 features. Performances of these ML models were evaluated using the area under the receiver operating characteristic curve (AUROC), the area under the precision-recall curve (AUPR), and the Brier score. The Shapley additive explanation (SHAP) analysis was employed to evaluate the importance of features and interpret the models.
ResultsThe LightGBM models developed for the community (AUROC 0.794, AUPR 0.575, Brier score 0.145) and primary care settings (AUROC 0.867, AUPR 0.705, Brier score 0.119) achieved higher performance than the models constructed by the other six algorithms. The streamlined LightGBM models for the community (AUROC 0.791, AUPR 0.563, Brier score 0.146) and primary care settings (AUROC 0.863, AUPR 0.692, Brier score 0.124) using the 20 top-ranked variables also showed excellent performance. SHAP analysis indicated that the top-ranked features included fasting plasma glucose (FPG), waist circumference (WC), body mass index (BMI), triglycerides (TG), gender, waist-to-height ratio (WHtR), the number of daughters born, resting pulse rate (RPR), etc.
ConclusionThe ML models using the LightGBM algorithm ar

许可

CC BY 4.0

引用

学术使用请引用 DOI 10.3389/fendo.2024.1292346.s001（source: https://frontiersin.figshare.com/articles/dataset/Table_1_Development_and_validation_of_machine_learning-augmented_algorithm_for_insulin_sensitivity_assessment_in_the_community_and_primary_care_settings_a_population-based_study_in_China_docx/25059005）。