应用新颖技术组合开发糖尿病并发症预测模型

数据集获取（Figshare）

• 源站标题：Applying a Novel Combination of Techniques to Develop a Predictive Model for Diabetes Complications
• DOI：10.1371/journal.pone.0121569
• 发布日期：2015-04-22
• 文件数：3 个
• 文件总体积：253.33 KB
• 源站页面：https://plos.figshare.com/articles/dataset/_Applying_a_Novel_Combination_of_Techniques_to_Develop_a_Predictive_Model_for_Diabetes_Complications_/1391002

命令行下载

curl -L -o "S1_File.docx" "https://ndownloader.figshare.com/files/2035268"
curl -L -o "S2_File.docx" "https://ndownloader.figshare.com/files/2035269"
curl -L -o "S3_File.docx" "https://ndownloader.figshare.com/files/2035270"

文件清单

• S1_File.docx（38.00 KB）
• S2_File.docx（77.13 KB）
• S3_File.docx（138.20 KB）

文件总体积 253.33 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

Among the many related issues of diabetes management, its complications constitute the main part of the heavy burden of this disease. The aim of this paper is to develop a risk advisor model to predict the chances of diabetes complications according to the changes in risk factors. As the starting point, an inclusive list of (k) diabetes complications and (n) their correlated predisposing factors are derived from the existing endocrinology text books. A type of data meta-analysis has been done to extract and combine the numeric value of the relationships between these two. The whole n (risk factors) - k (complications) model was broken down into k different (n-1) relationships and these (n-1) dependencies were broken into n (1-1) models. Applying regression analysis (seven patterns) and artificial neural networks (ANN), we created models to show the (1-1) correspondence between factors and complications. Then all 1-1 models related to an individual complication were integrated using the naïve Bayes theorem. Finally, a Bayesian belief network was developed to show the influence of all risk factors and complications on each other. We assessed the predictive power of the 1-1 models by R2, F-ratio and adjusted R2 equations; sensitivity, specificity and positive predictive value were calculated to evaluate the final model using real patient data. The results suggest that the best fitted regression models outperform the predictive ability of an ANN model, as well as six other regression patterns for all 1-1 models.

许可

CC BY 4.0

引用

学术使用请引用 DOI 10.1371/journal.pone.0121569（source: https://plos.figshare.com/articles/dataset/_Applying_a_Novel_Combination_of_Techniques_to_Develop_a_Predictive_Model_for_Diabetes_Complications_/1391002）。