预测颞下颌关节紊乱的模型：分类树分析的应用

数据集获取（Figshare）

• 源站标题：Model for Predicting Temporomandibular Dysfunction: Use of Classification Tree Analysis
• DOI：10.6084/m9.figshare.14304843.v1
• 发布日期：2021-03-25
• 文件数：2 个
• 文件总体积：60.00 KB
• 源站页面：https://scielo.figshare.com/articles/dataset/Model_for_Predicting_Temporomandibular_Dysfunction_Use_of_Classification_Tree_Analysis/14304843

命令行下载

curl -L -o "1806-4760-bdj-31-04-360-gf1.jpg" "https://ndownloader.figshare.com/files/27208317"
curl -L -o "Table_1.xls" "https://ndownloader.figshare.com/files/27208320"

文件清单

• 1806-4760-bdj-31-04-360-gf1.jpg（50.27 KB）
• Table_1.xls（9.73 KB）

文件总体积 60.00 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

Abstract The aim of this study was to construct a predictive model that uses classification tree statistical analysis to predict the occurrence of temporomandibular disorder, by dividing the sample into groups of high and low risk for the development of the disease. The use of predictive statistical approaches that facilitate the process of recognizing and/or predicting the occurrence of temporomandibular disorder is of interest to the scientific community, for the purpose of providing patients with more adequate solutions in each case. This was a cross-sectional analytical population-based study that involved a sample of 776 individuals who had sought medical or dental attendance at the Family Health Units in Recife, PE, Brazil. The sample was submitted to anamnesis using the instrument Research Diagnostic Criteria for Temporomandibular Disorders. The data were inserted into the software Statistical Package for the Social Sciences 20.0 and analyzed by the Pearson Chi-square test for bivariate analysis, and by the classification tree method for the multivariate analysis. Temporomandibular disorder could be predicted by orofacial pain, age and depression. The high-risk group was composed of individuals with orofacial pain, those between the ages of 25 and 59 years and those who presented depression. The low risk group was composed of individuals without orofacial pain. The authors were able to conclude that the best predictor for temporomandibular disorder was orofacial pain, and that the predictive model proposed by the classification tree could be applied as a tool for simplifying decision making relative to the occurrence of temporomandibular disorder.

许可

CC BY 4.0

引用

学术使用请引用 DOI 10.6084/m9.figshare.14304843.v1（source: https://scielo.figshare.com/articles/dataset/Model_for_Predicting_Temporomandibular_Dysfunction_Use_of_Classification_Tree_Analysis/14304843）。