分层10折交叉验证性能

数据集获取（Figshare）

• 源站标题：<p>Stratified 10-Fold Cross-Validation Performance.</p>
• DOI：10.1371/journal.pone.0343096.t004
• 发布日期：2026-03-13
• 文件数：1 个
• 文件总体积：5.63 KB
• 源站页面：https://plos.figshare.com/articles/dataset/_p_Stratified_10-Fold_Cross-Validation_Performance_p_/31716509

命令行下载

curl -L -o "Table 4.xls" "https://ndownloader.figshare.com/files/62771528"

文件清单

• Table 4.xls（5.63 KB）

文件总体积 5.63 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
The COVID-19 pandemic has highlighted the critical need for robust, interpretable predictive models to guide clinical decision-making for hospitalized patients, particularly in resource-limited settings. While machine learning approaches have achieved high predictive performance, many lack the transparency required for clinical adoption. Bayesian networks provide a rigorous mathematical framework for modeling medical uncertainty and complex causal relationships while maintaining clinical interpretability.
Objective
To develop and validate an interpretable Bayesian network model for predicting clinical outcomes in hospitalized COVID-19 patients, including severity, complications, and mortality, and to compare its performance with existing predictive approaches. The model specifically addresses the needs of resource-limited settings where both interpretability and performance are critical.
Methods
This retrospective cohort study analyzed 124 hospitalized COVID-19 patients at Tanambao I University Hospital, Madagascar (March 2020–March 2022). Given limited genomic sequencing capacity during the study period, specific SARS-CoV-2 variants infecting individual patients could not be confirmed. After descriptive and statistical implicative analysis, we constructed a Bayesian network integrating predictive variables organized in three levels: input variables (demographic, vital signs, biological, radiological), intermediate variables (clinical scores), and target variables (severity, complications, evolution, death). Parameter learning used maximum likelihood estimation with Laplace smoothing. Model performance was evaluated using stratified 10-fold cross-validation and compared against logistic regression, random forest, and support vector machine approaches.
Results
The Bayesian network model exhibited strong diagnostic performance, with an AUC of 0.95 (95% CI: 0.91–0.99) for death prediction, 0.94 (95% CI: 0.90–0.98) for severe outcomes, and 0.93 (95% CI: 0.89–0.97) for unfavorable progression. Stratified 10-fold cross-validation yielded a mean accuracy of . The model outperformed logistic regression (AUC 0.89), random forest (AUC 0.91), and SVM (AUC 0.87)

许可

CC BY 4.0

引用

学术使用请引用 DOI 10.1371/journal.pone.0343096.t004（source: https://plos.figshare.com/articles/dataset/_p_Stratified_10-Fold_Cross-Validation_Performance_p_/31716509）。