Multivariate logistic analysis

数据集获取（Figshare）

• 源站标题：<p>Multivariate logistic analysis.</p>
• DOI：10.1371/journal.pone.0336058.t003
• 发布日期：2025-12-19
• 文件数：1 个
• 文件总体积：9.73 KB
• 源站页面：https://plos.figshare.com/articles/dataset/_p_Multivariate_logistic_analysis_p_/30922569

命令行下载

curl -L -o "Table 3.xls" "https://ndownloader.figshare.com/files/60516321"

文件清单

• Table 3.xls（9.73 KB）

文件总体积 9.73 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
With the advancement of globalization, the prevalence of cognitive dysfunction in the elderly population has risen significantly. Early intervention may dramatically alleviate the disease burden and reduce economic costs associated with cognitive impairment. This study aims to construct a risk prediction model for cognitive dysfunction based on machine learning (ML) algorithms, providing healthcare professionals and patients with a more accurate and effective tool for risk assessment.
Methods
This study included 1,325 elderly participants who completed cognitive assessments and comprehensive laboratory blood tests. Risk factors for cognitive dysfunction were identified through univariate analysis, multivariate logistic regression, LASSO regression, and the Boruta algorithm. Nine ML methods—Random Forest (RF), Light Gradient Boosting Machine (LightGBM), Extreme Gradient Boosting (XGBoost), Logistic Regression, K-Nearest Neighbor (KNN), Support Vector Machine (SVM), Artificial Neural Network (ANN), Decision Tree, and Elastic Net—were employed to construct the prediction models. The Shapley Additive Explanations (SHAP) algorithm was utilized to interpret the final model.
Results
The Random Forest model exhibited the highest predictive performance, with an AUC value exceeding those of other models. SHAP analysis identified age, race, education level, diabetes, and depression as the primary predictors of cognitive dysfunction in the elderly. The calibration curve indicated a strong alignment between the model’s predictions and actual outcomes, while the decision curve confirmed the model’s clinical applicability.
Conclusion
Age, race, education level, diabetes, and depression are significant influencing factors of cognitive dysfunction in the elderly. Among the ML algorithms evaluated, the Random Forest model exhibited the best predictive performance.

许可

CC BY 4.0

引用

学术使用请引用 DOI 10.1371/journal.pone.0336058.t003（source: https://plos.figshare.com/articles/dataset/_p_Multivariate_logistic_analysis_p_/30922569）。