癌症幸存者认知障碍预测

数据集获取（Figshare）

• 源站标题：<b>Cognitive impairment prediction in cancer survivors</b>
• DOI：10.6084/m9.figshare.30569807.v1
• 发布日期：2025-11-07
• 文件数：1 个
• 文件总体积：129.35 KB
• 源站页面：https://figshare.com/articles/dataset/_b_Cognitive_impairment_prediction_in_cancer_survivors_b_/30569807

命令行下载

curl -L -o "Dataset for Cognitive Failure (Cancer Survivors Tehran Iran).xlsx" "https://ndownloader.figshare.com/files/59403539"

文件清单

• Dataset for Cognitive Failure (Cancer Survivors Tehran Iran).xlsx（129.35 KB）

文件总体积 129.35 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

Objectives: Cancer-related cognitive impairment (CI) refers to the reduction of cognitive abilities following treatment that affects the quality of survivors’ lives. Self-report instruments such as Cognitive Failure Questionnaire (CFQ) provide a quick and economical method for early detection. The objective of this study is to develop machine learning models by utilizing demographic predictors to categorize survivors into low, medium, and high CI groups.
Methods: Data from 570 cancer survivors were analyzed using CFQ scores as the outcome measure. Demographic variables were employed as inputs to four supervised algorithms: Logistic Regression (LR), Support Vector Machine (SVM), Artificial Neural Network (ANN), and k-Nearest Neighbors (KNN). Model performance was assessed using accuracy, sensitivity, specificity, and area under the ROC curve (AUC).

许可

CC BY 4.0

引用

学术使用请引用 DOI 10.6084/m9.figshare.30569807.v1（source: https://figshare.com/articles/dataset/_b_Cognitive_impairment_prediction_in_cancer_survivors_b_/30569807）。