表1_使用集成学习与贝叶斯优化优化预测糖尿病并发症：一种成本高效的基于实验室的方法.xlsx

数据集获取（Figshare）

• 源站标题：Table 1_Optimized prediction of diabetes complications using ensemble learning with Bayesian optimization: a cost-efficient laboratory-based approach.xlsx
• DOI：10.3389/fendo.2025.1593068.s001
• 发布日期：2025-06-20
• 文件数：1 个
• 文件总体积：448.44 KB
• 源站页面：https://frontiersin.figshare.com/articles/dataset/Table_1_Optimized_prediction_of_diabetes_complications_using_ensemble_learning_with_Bayesian_optimization_a_cost-efficient_laboratory-based_approach_xlsx/29370419

命令行下载

curl -L -o "Table 1_Optimized prediction of diabetes complications using ensemble learning with Bayesian optimization: a cost-efficient laboratory-based approach.xlsx" "https://ndownloader.figshare.com/files/55520354"

文件清单

• Table 1_Optimized prediction of diabetes complications using ensemble learning with Bayesian optimization: a cost-efficient laboratory-based approach.xlsx（448.44 KB）

文件总体积 448.44 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

Background and objectiveThe increasing global prevalence of diabetes has led to a surge in complications, significantly burdening healthcare systems and affecting patient quality of life. Early prediction of these complications is critical for timely intervention, yet existing models often rely heavily on clinical indicators while underutilizing fundamental laboratory test parameters. This study aims to bridge this gap by leveraging the 12 most frequently tested laboratory indicators in diabetic patients to develop an optimized predictive model for diabetes complications.
MethodsA comprehensive dataset was established through meticulous data collection from a high-volume tertiary hospital, followed by extensive data cleaning and classification. Various machine learning classifiers, including Random Forest, XGBoost, Support Vector Machine (SVM), and Multilayer Perceptron (MLP), were trained on this dataset to evaluate their predictive performance. We further introduced an ensemble learning model with Bayesian optimization to enhance accuracy and cost-efficiency. Additionally, feature importance analysis was conducted to refine the model by reducing testing costs while maintaining high predictive accuracy.
ResultsOur ensemble model with Bayesian optimization demonstrated superior performance, achieving over 90% accuracy in predicting various diabetic complications, with an outstanding 98.50% accuracy and 99.76% AUC for diabetic nephropathy. Feature correlation analysis enabled a refined model that not only improved predictive accuracy but also reduced overall medical costs by 2.5% through strategic feature elimination.
ConclusionsThis study makes three key contributions: (1) Development of a high-quality dataset based on fundamental laboratory indicators, (2) Creation of a highly accurate predictive model using ensemble learning and Bayesian optimization, particularly excelling in diabetic nephropathy prediction, and (3) Implementation of a cost-efficient diagnostic approach that reduces testing expenses without compromising accuracy. The proposed model provides a strong foundation for future research and practical clinical applications, demonstrating the potentia

许可

CC BY 4.0

引用

学术使用请引用 DOI 10.3389/fendo.2025.1593068.s001（source: https://frontiersin.figshare.com/articles/dataset/Table_1_Optimized_prediction_of_diabetes_complications_using_ensemble_learning_with_Bayesian_optimization_a_cost-efficient_laboratory-based_approach_xlsx/29370419）。