临床验证数据集
用于冠心病风险预测验证的临床表格数据集，包含Framingham等公开数据集的血脂、血压等特征，适用于模型评估。
基本信息
资源简介
临床验证数据集
【数据集背景】
本数据集源站标题为《This is the clinical validation dataset.》。源站首次发布：2025-09-12。
【数据内容】
源站原始描述：Background Coronary Heart Disease (CHD) is one of the major burdens of cardiovascular diseases worldwide. Traditional diagnostic methods, such as coronary angiography and electrocardiogram, face challenges including high costs, subjectivity, and high misdiagnosis rates. To address these issues, this study proposes a prediction framework for CHD based on the LightGBM algorithm, aiming to improve the accuracy and interpretability of CHD risk prediction. Methods This study utilized three publicly a…
主表 pone.0330377.s001.csv 含 22 列、样本前 700 行结构完整。
【数据结构与技术规格】
文件数 1 个，合计 31.90 KB。pone.0330377.s001.csv：text/csv，31.90 KB。数据集 DOI：10.1371/journal.pone.0330377.s001。
【主题与分类】
源站学科分类：Genetics、Biotechnology、Biological Sciences not elsewhere classified、Information Systems not elsewhere classified、Mathematical Sciences not elsewhere classified、Cancer。主题标签：traditional diagnostic methods、shapley additive explanations、evaluated using metrics、enhancing clinical decision、cardiovascular diseases worldwide、model &# 8217、high misdiagnosis rates、alizadeh sani datasets、predicting chd risk、chd scoring system。
【适用方向】
结构化表格数据可直接用于统计建模、特征工程与队列分析。结构化表格数据可直接用于统计建模、特征工程与队列分析。
【获取与许可】
源站页面：https://plos.figshare.com/articles/dataset/This_is_the_clinical_validation_dataset_/30114957
使用许可：CC BY 4.0（https://creativecommons.org/licenses/by/4.0/）。
规范引用：Deng, Lang; Lu, Kongjie; Hu, Huanhuan (2025). This is the clinical validation dataset.. PLOS ONE. Dataset
下载信息
注册下载
需要注册 Kaggle 账号并登录后下载，适合需要跟踪下载记录和使用 API 的用户。
暂未开放公开下载
无需注册即可直接获取公开样本或文档，适合快速预览和评估数据集质量。
免登录有偿下载
公开数据集受托下载与技术交付服务。
提供高速下载与技术交付服务（收技术服务费，非数据销售）
暂未开放当前数据集主要来源为 Kaggle 公开托管，完整影像包建议通过原始链接或 Kaggle API 获取。
使用方式
数据集获取（Figshare）
- 源站标题：This is the clinical validation dataset.
- DOI：10.1371/journal.pone.0330377.s001
- 发布日期：2025-09-12
- 文件数：1 个
- 文件总体积：31.90 KB
- 源站页面：https://plos.figshare.com/articles/dataset/This_is_the_clinical_validation_dataset_/30114957
命令行下载
curl -L -o "pone.0330377.s001.csv" "https://ndownloader.figshare.com/files/57919968"
文件清单
pone.0330377.s001.csv（31.90 KB）
文件总体积 31.90 KB（源站 API 实测）。
数据说明
Background
Coronary Heart Disease (CHD) is one of the major burdens of cardiovascular diseases worldwide. Traditional diagnostic methods, such as coronary angiography and electrocardiogram, face challenges including high costs, subjectivity, and high misdiagnosis rates. To address these issues, this study proposes a prediction framework for CHD based on the LightGBM algorithm, aiming to improve the accuracy and interpretability of CHD risk prediction.
Methods
This study utilized three publicly available datasets: BRFSS_2015, Framingham, and Z-Alizadeh Sani. The BRFSS_2015 dataset was used for model training, while the Framingham and Z-Alizadeh Sani datasets were employed for validation. Data preprocessing included cleaning, feature engineering, and handling missing values. The LightGBM model was selected for its efficiency and performance, and SHAP (SHapley Additive exPlanations) values were used to enhance model interpretability. Model performance was evaluated using metrics such as accuracy, precision, recall, F1-score, and AUROC. A CHD scoring system was developed based on the model’s predictions to assist clinicians in risk assessment.
Results
The LightGBM model demonstrated excellent performance, achieving an accuracy of 90.60% and an AUROC of 81.06% on the BRFSS_2015 dataset. After parameter tuning, the model’s accuracy improved to 90.61%, and the AUROC increased to 81.11%. On the Framingham dataset, the accuracy improved from 83.96% to 85.26%, and the AUROC increased from 62.86% to 67.37%. On the Z-Alizadeh Sani dataset, the accuracy improved from 78.69% to 80.33%, and the precision increased from 74.40% to 76.36%.
Conclusions
SHAP analysis revealed that age, smoking status, diabetes, hypertension, and high cholesterol were the most influential features in predicting CHD risk. The developed CHD scoring system provided a user-friendly tool for clinicians to assess patient risk levels effectively.
许可
CC BY 4.0
引用
学术使用请引用 DOI 10.1371/journal.pone.0330377.s001（source: https://plos.figshare.com/articles/dataset/This_is_the_clinical_validation_dataset_/30114957）。
数据缺失？
依托陕西、四川两大基地，我们与超过十家三甲医院建立直接合作关系，覆盖合规授权、采集治理、专业标注、数据交付的全流程，为AI医疗团队提供即拿即用的高质量临床数据。
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