基于神经网络模型的中国五种常见癌症住院费用影响因素分析数据集

数据集获取（Figshare）

• 源站标题：Analyzing factors influencing hospitalization costs for five common cancers in China using neural network models
• DOI：10.6084/m9.figshare.29040043.v1
• 发布日期：2025-05-12
• 文件数：3 个
• 文件总体积：200.47 KB
• 源站页面：https://tandf.figshare.com/articles/dataset/Analyzing_factors_influencing_hospitalization_costs_for_five_common_cancers_in_China_using_neural_network_models/29040043

命令行下载

curl -L -o "ijme_a_2494459_sm5078.pdf" "https://ndownloader.figshare.com/files/54459304"
curl -L -o "ijme_a_2494459_sm5077.pdf" "https://ndownloader.figshare.com/files/54459307"
curl -L -o "ijme_a_2494459_sm5075.pdf" "https://ndownloader.figshare.com/files/54459310"

文件清单

• ijme_a_2494459_sm5078.pdf（100.57 KB）
• ijme_a_2494459_sm5077.pdf（36.45 KB）
• ijme_a_2494459_sm5075.pdf（63.46 KB）

文件总体积 200.47 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

Malignant tumors are a major global health crisis, causing 25% of deaths in China, with lung, liver, thyroid, breast, and colon cancers being the most common. Understanding the factors influencing hospitalization costs for these cancers is crucial for public health and economics. This study aimed to identify key cost factors and develop a neural network model for predicting hospitalization costs, thereby providing tools to ease the financial burden on patients and healthcare systems.
Data on hospitalization costs for 30,893 cancer patients from secondary or higher-level hospitals in Zhuhai, Guangdong Province, between 2017 and 2022, were analyzed. Neural network classification and feature importance analysis were used to determine the main factors influencing costs and to develop predictive models. Model performance was evaluated using the area under the receiver operating characteristic curve (AUROC), with a 95% confidence interval (CI) calculated for the AUROC value.
The key factors influencing hospitalization costs for lung cancer are metastasis and malignant solid tumor (MST), with correlation coefficients of 0.126 and 0.086, respectively, both showing statistical significance (p p This study confirmed the strong clinical applicability of the neural network predictive model in analyzing hospitalization costs for lung and colon cancer and revealed the factors that influence hospitalization costs for these cancers.

许可

CC BY 4.0

引用

学术使用请引用 DOI 10.6084/m9.figshare.29040043.v1（source: https://tandf.figshare.com/articles/dataset/Analyzing_factors_influencing_hospitalization_costs_for_five_common_cancers_in_China_using_neural_network_models/29040043）。