儿童和青少年脂肪肝预测因素调查表数据

数据集获取（Figshare）

• 源站标题：Table 1_Investigation of predictive factors for fatty liver in children and adolescents using artificial intelligence.docx
• DOI：10.3389/fped.2025.1537098.s001
• 发布日期：2025-08-11
• 文件数：1 个
• 文件总体积：14.45 KB
• 源站页面：https://frontiersin.figshare.com/articles/dataset/Table_1_Investigation_of_predictive_factors_for_fatty_liver_in_children_and_adolescents_using_artificial_intelligence_docx/29882747

命令行下载

curl -L -o "Table 1_Investigation of predictive factors for fatty liver in children and adolescents using artificial intelligence.docx" "https://ndownloader.figshare.com/files/57105020"

文件清单

• Table 1_Investigation of predictive factors for fatty liver in children and adolescents using artificial intelligence.docx（14.45 KB）

文件总体积 14.45 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

BackgroundChildhood obesity is a growing problem worldwide, leading to non-alcoholic fatty liver disease (NAFLD), which is the most common liver disease in children. Liver biopsy is the gold standard for NAFLD diagnosis. Machine learning algorithms could assist in an early diagnostic approach and leading to a favorable prognosis.
ObjectiveThis study aimed to identify predictive factors for NAFLD in children and adolescents using machine learning models, focusing on liver biopsy outcomes such as fibrosis, infiltration, ballooning, and steatosis.
MethodsData from 659 children suspected of NAFLD, who underwent liver biopsy at Mofid Children’'s Hospital between 2011 and 2023, were analyzed. The dataset included categorical and numerical variables, which were processed using one-hot encoding and standardization. Several machine learning models were trained and evaluated, including CatBoost, AdaBoost, Random Forest, and others. Model performance was assessed using cross-validation with accuracy, precision, recall, F1 score, and ROC-AUC metrics. Feature importance was determined through permutation analysis.
ResultsAmong NAFLD patients, the CatBoost Classifier achieved the highest accuracy (91.8%) and ROC-AUC score (92.3%) in cross-validation. In addition, the adjusted models showed better results. That is, the F1 for the CatBoost raised from 83% to 89% (AUC: 0.86–0.92), for the GradientBoosting from 76% to 81% (AUC: 0.81–0.85), and for Bernolli Naive Bayes from 78% to 82% (AUC: 0.82–0.85).
ConclusionMachine learning models, particularly CatBoost, demonstrated strong predictive capabilities for NAFLD diagnosis in children.

许可

CC BY 4.0

引用

学术使用请引用 DOI 10.3389/fped.2025.1537098.s001（source: https://frontiersin.figshare.com/articles/dataset/Table_1_Investigation_of_predictive_factors_for_fatty_liver_in_children_and_adolescents_using_artificial_intelligence_docx/29882747）。