从传统代谢标志物到集成学习：机器学习模型在青少年NAFLD风险预测中的比较应用数据表1

数据集获取（Figshare）

• 源站标题：Data Sheet 1_From traditional metabolic markers to ensemble learning: comparative application of machine learning models for predicting NAFLD risk in adolescents.csv
• DOI：10.3389/fendo.2025.1681686.s001
• 发布日期：2025-10-29
• 文件数：1 个
• 文件总体积：157.44 KB
• 源站页面：https://frontiersin.figshare.com/articles/dataset/Data_Sheet_1_From_traditional_metabolic_markers_to_ensemble_learning_comparative_application_of_machine_learning_models_for_predicting_NAFLD_risk_in_adolescents_csv/30474773

命令行下载

curl -L -o "Data Sheet 1_From traditional metabolic markers to ensemble learning: comparative application of machine learning models for predicting NAFLD risk in .csv" "https://ndownloader.figshare.com/files/59141123"

文件清单

• Data Sheet 1_From traditional metabolic markers to ensemble learning: comparative application of machine learning models for predicting NAFLD risk in .csv（157.44 KB）

文件总体积 157.44 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

BackgroundNon-alcoholic fatty liver disease (NAFLD) is increasingly prevalent among adolescents and poses a significant public health challenge. Due to limitations in imaging and invasive diagnostic methods such as liver biopsy, there is a pressing need for accurate, cost-effective, and non-invasive risk prediction tools. This study aims to develop and compare multiple machine learning (ML) models to predict NAFLD risk in adolescents using routine anthropometric and laboratory data from the National Health and Nutrition Examination Survey (NHANES) 2011–2020 dataset.
MethodsData from 2,132 U.S. adolescents (NHANES 2011–2020) were analyzed. Nine machine learning (ML) models were developed using features selected by Light Gradient Boosting Machine (LightGBM). Performance was assessed by AUC, accuracy, sensitivity, precision, F1-score, and calibration. The Extra Trees (ET) model was further compared with TyG-based logistic regression models. Model interpretability was evaluated using SHapley Additive exPlanations (SHAP), and an interactive online prediction tool was deployed.
ResultsNAFLD prevalence was 13.0%. The ET model achieved the best overall performance (AUC = 0.784, ACC = 0.773, Kappa = 0.320), outperforming other ML algorithms and TyG-based models, which showed higher sensitivity but poorer precision. SHAP analysis identified waist circumference, triglycerides, insulin, and HDL as key predictors, revealing nonlinear threshold effects. The online tool allows individualized risk estimation based on routine clinical variables.
ConclusionThe ET-based ML model provides an accurate and interpretable approach for adolescent NAFLD risk prediction. By surpassing traditional metabolic indicators and offering an accessible web-based calculator, it supports scalable, cost-effective early screening and targeted prevention strategies.

许可

CC BY 4.0

引用

学术使用请引用 DOI 10.3389/fendo.2025.1681686.s001（source: https://frontiersin.figshare.com/articles/dataset/Data_Sheet_1_From_traditional_metabolic_markers_to_ensemble_learning_comparative_application_of_machine_learning_models_for_predicting_NAFLD_risk_in_adolescents_csv/30474773）。