不同模型的预测性能

数据集获取（Figshare）

• 源站标题：The prediction performance from different models.
• DOI：10.1371/journal.pone.0334881.t002
• 发布日期：2025-11-13
• 文件数：1 个
• 文件总体积：13.82 KB
• 源站页面：https://plos.figshare.com/articles/dataset/The_prediction_performance_from_different_models_/30612258

命令行下载

curl -L -o "Table 2.xls" "https://ndownloader.figshare.com/files/59542353"

文件清单

• Table 2.xls（13.82 KB）

文件总体积 13.82 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

Background
Risk of coronary heart disease (CHD) in a specific period of years can be assessed using scores calculated by models, such as pooled cohort equations (PCEs) and Framingham Risk Score. However, there are few studies on on-site estimation of CHD risk quantitatively with score calculation as auxiliary diagnosis. Nowadays, researchers introduce new technologies, such as machine learning, as effective CHD risk prediction models, but these models still need to be validated using real clinical data before promoting their use in real clinical settings.
Objective
The aim of this study is to predict CHD risk for high-risk population only using clinical data consisting of vital traits, lab measurement, diagnosis, medical device testing and medications. The prediction model can serve as an on-site quantitative indicator for the CHD risk of potential patients before diagnosis using coronary arteriography.
Methods
This work is designed as a retrospective study of a hospital-based cohort (The Second Affiliated Hospital of Guangxi Medical University), comprising 20,821 patients with CHD and 9,796 controls from 2017 to 2024. A two-layer machine learning model (TLML) is developed on the prediction results of the random forest and the gradient boosting decision tree to combine the merits of both models. The models were trained and validated with the clinical data in the cohort.
Results
The TLML presented in this study can have a good accuracy (0.79, 95% CI 0.79–0.80), sensitivity (0.79, 95% CI 0.79–0.80) and specificity (0.79, 95% CI 0.79–0.79) for on-site CHD prediction. Compared with the PCEs (accuracy = 0.59, sensitivity = 0.58 and specificity = 0.60), the TLML shows remarkably better on-site CHD prediction performance. Predictor importance analysis results show that age, diabetes, antihypertensive medications, total bilirubin, hypertension, obstructive sleep apnea-hypopnea syndrome, red cell count, hemoglobin, cystatin C, retinol-binding protein, gender and low-density lipoprotein cholesterol level are the most important variables for on-site CHD prediction. All the features mentioned were reported to have relationship with CHD on some levels in previous studies. A

许可

CC BY 4.0

引用

学术使用请引用 DOI 10.1371/journal.pone.0334881.t002（source: https://plos.figshare.com/articles/dataset/The_prediction_performance_from_different_models_/30612258）。