青少年成熟型糖尿病亚型预测的机器学习数据表1

数据集获取（Figshare）

• 源站标题：Data Sheet 1_Prediction of maturity-onset diabetes of the young subtypes using machine learning.pdf
• DOI：10.3389/fdgth.2026.1656161.s001
• 发布日期：2026-03-26
• 文件数：1 个
• 文件总体积：1.13 MB
• 源站页面：https://frontiersin.figshare.com/articles/dataset/Data_Sheet_1_Prediction_of_maturity-onset_diabetes_of_the_young_subtypes_using_machine_learning_pdf/31858879

命令行下载

curl -L -o "Data Sheet 1_Prediction of maturity-onset diabetes of the young subtypes using machine learning.pdf" "https://ndownloader.figshare.com/files/63114592"

文件清单

• Data Sheet 1_Prediction of maturity-onset diabetes of the young subtypes using machine learning.pdf（1.13 MB）

文件总体积 1.13 MB（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

IntroductionMaturity-onset diabetes of the young (MODY) is a monogenic type of diabetes caused by different pathogenic genetic variants in glucose metabolism-related genes, with GCK-MODY and HFN1A-MODY subtypes being the most frequent. Diagnosing the specific MODY subtype is essential for correct treatment and follow-up, but it requires gene sequencing, a time-consuming and costly process that depends on highly skilled professionals. Therefore, it is mandatory to develop tools that allow to correctly determine in which order to study the involved genes, reducing the number of sequencing procedures to find the causal variant and making the diagnostic process more efficient. This proof-of-concept study evaluates machine learning as a complement to clinical characterization and genetic testing, by optimizing binary classification models for explainable prediction of MODY subtypes, with a focus on GCK-MODY and HFN1A-MODY.
MethodsTo meet this aim, we analyzed medical data from a diabetes cohort from Buenos Aires, Argentina. By employing imputation and oversampling techniques we created 10 datasets for each subtype to feed a pipeline that trained, optimized and evaluated 10 machine learning techniques.
ResultsGaussian Naive Bayes achieved the best predictive power for GCK-MODY with a ROC AUC score of 0.724, meanwhile Random Forest yielded 0.712 for HNF1A-MODY. SHAP analysis provided insights into feature importance, highlighting the explainability of our approach.
Discussion and conclusionThis novel study demonstrates for the first time the viability of machine learning as a supplementary tool prior to MODY genetic testing, by providing cost-effective and explainable models able to assist health professionals in the diagnosis of MODY subtypes.

许可

CC BY 4.0

引用

学术使用请引用 DOI 10.3389/fdgth.2026.1656161.s001（source: https://frontiersin.figshare.com/articles/dataset/Data_Sheet_1_Prediction_of_maturity-onset_diabetes_of_the_young_subtypes_using_machine_learning_pdf/31858879）。