基础模型的性能

数据集获取（Figshare）

• 源站标题：<p>Performance of the basis models.</p>
• DOI：10.1371/journal.pone.0314831.t002
• 发布日期：2025-12-26
• 文件数：1 个
• 文件总体积：5.63 KB
• 源站页面：https://plos.figshare.com/articles/dataset/_p_Performance_of_the_basis_models_p_/30954612

命令行下载

curl -L -o "Table 2.xls" "https://ndownloader.figshare.com/files/60639276"

文件清单

• Table 2.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

Objective
Gliomas are among the most common and heterogeneous primary tumours of the central nervous system. Accurate grading is essential for treatment planning and prognosis, yet conventional histopathological approaches are limited by subjectivity and poor reproducibility. This study aimed to develop a machine learning–based prediction model that integrates clinical and molecular characteristics to improve early glioma grading, thereby enhancing diagnostic accuracy and supporting individualized treatment strategies.
Methods
An efficient prediction model for low-grade gliomas (LGGs) and glioblastoma (grade IV, GBM) was developed by utilizing the clinical and molecular characteristics of gliomas from The Cancer Genome Atlas (TCGA) dataset. A novel integration of recursive feature elimination (RFE) with random forest (RF) and elastic net regression (ENR) was implemented to select features efficiently. Additionally, the synthetic minority oversampling technique (SMOTE) was applied to balance the training set, and K-nearest neighbours (KNN), support vector machine (SVM), and other algorithms were optimized through random-search hyper-parameter optimization (HPO) with five-fold cross-validation, yielding nine distinct machine learning (ML) models. Ultimately, by applying the voting and stacking algorithms, 34 ensemble learning models were constructed. Furthermore, all the models were externally validated using the Chinese Glioma Genome Atlas (CGGA) dataset. Finally, SHapley Additive exPlanations (SHAP) analysis was conducted to elucidate the prediction processes of the ensemble models.
Results
Feature selection revealed 11 key grading features, including Tumour Protein 53 (TP53) and Isocitrate Dehydrogenase 1 (IDH1). Among the 9 basic models constructed by combining optimization techniques such as SMOTE, the RF model had the best performance (Area Under Curve (AUC) of 0.916 for TCGA and 0.797 for CGGA). Among the 34 integrated models constructed, the Voting25 model integrating RF, Extreme Gradient Boosting (XGBoost), and KNN achieved AUC values of 0.928 and 0.794, respectively, on the TCGA and CGGA datasets, demonstrating overall optimal predictive performance.
Con

许可

CC BY 4.0

引用

学术使用请引用 DOI 10.1371/journal.pone.0314831.t002（source: https://plos.figshare.com/articles/dataset/_p_Performance_of_the_basis_models_p_/30954612）。