模型相关变量的ROC分析
包含902例患者超声心动图和临床数据，用于机器学习预测基底室间隔肥厚（BSH）并进行ROC分析。
基本信息
资源简介
模型相关变量的ROC分析
【一句话概览】
本数据集是 figshare 公开的多模态数据集，收录 1 个文件、合计 5.63 KB，主题涵盖 shapley additive explanation、recognized morphological change、nearest neighbor classification、mitral valve peak、interpretable machine learning，可用于跨模态对齐与融合建模研究，CC BY 4.0 许可开放获取。
【数据集背景】
本数据集源站标题为《ROC analysis of model-related variable.》。源站首次发布：2025-06-30。
【数据内容】
源站原始描述：Background The basal septal hypertrophy(BSH) is an often under-recognized morphological change in the left ventricle. This is a common echocardiographic finding with a prevalence of approximately 7–20%, which may indicate early structural and functional remodeling of the left ventricle in certain pathologies. It also poses a risk of severe left ventricular outflow tract obstruction and is a significant cause of postoperative complications in patients undergoing transcatheter aortic valve implant…
【数据结构与技术规格】
文件数 1 个，合计 5.63 KB。Table 2.xls：application/vnd.ms-excel，5.63 KB。数据集 DOI：10.1371/journal.pone.0325992.t002。
【主题与分类】
源站学科分类：Biotechnology、Biological Sciences not elsewhere classified、Mathematical Sciences not elsewhere classified、Developmental Biology、Plant Biology。主题标签：shapley additive explanation、recognized morphological change、nearest neighbor classification、mitral valve peak、interpretable machine learning、first central hospital、common echocardiographic finding、capturing nonlinear relationships、947 &# 8211、93 &# 8211。
【适用方向】
多模态数据适合跨模态对齐、融合建模与联合表征学习方向的研究。
【获取与许可】
源站页面：https://plos.figshare.com/articles/dataset/ROC_analysis_of_model-related_variable_/29439121
使用许可：CC BY 4.0（https://creativecommons.org/licenses/by/4.0/）。
规范引用：Gao, Lei; Tian, Boyan; Jia, Qiqi; He, Xingyu; Zhao, Guannan; Wang, Yueheng (2025). ROC analysis of model-related variable.. 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：ROC analysis of model-related variable.
- DOI：10.1371/journal.pone.0325992.t002
- 发布日期：2025-06-30
- 文件数：1 个
- 文件总体积：5.63 KB
- 源站页面：https://plos.figshare.com/articles/dataset/ROC_analysis_of_model-related_variable_/29439121
命令行下载
curl -L -o "Table 2.xls" "https://ndownloader.figshare.com/files/55781773"
文件清单
Table 2.xls（5.63 KB）
文件总体积 5.63 KB（源站 API 实测）。
数据说明
Background
The basal septal hypertrophy(BSH) is an often under-recognized morphological change in the left ventricle. This is a common echocardiographic finding with a prevalence of approximately 7–20%, which may indicate early structural and functional remodeling of the left ventricle in certain pathologies. It also poses a risk of severe left ventricular outflow tract obstruction and is a significant cause of postoperative complications in patients undergoing transcatheter aortic valve implantation (TAVI). Compared to traditional algorithms, machine learning algorithms are more effective at capturing nonlinear relationships and developing more accurate diagnostic and predictive models.
However, no predictive models for BSH have been developed using machine learning algorithms.
Objective
To evaluate the effectiveness of five machine learning algorithms in predicting thickening of the basal segment of the interventricular septum and to develop a simple, yet efficient, prediction model for BSH.
Methods
Echocardiographic and clinical data from 902 patients were collected from the First Central Hospital of Baoding City, including 91 BSH patients and 811 non-BSH patients. The data were divided into training and test sets in a 7:3 ratio. Five machine learning algorithms -XGBoost, Random Forest(RF), Dicision tree(DT), K-Nearest Neighbor classification(KNN), and Naive Bayes(NB) were applied to construct the models, combined with logistic regression (LR) based on Lasso regression. The performance of each model was evaluated using Receiver Operating Characteristic curve (ROC),calibration curves and Decision Curve Analysis (DCA)curve, with the model demonstrating the best performance being selected. The shapley additive explanation (SHAP) method was employed to interpret the XBoost and RF models.
Results
The logistic regression (LR) of the Lasso regression model showed that IVS-AO Angle, Left Ventricular Mass Index (LVMI), Diastolic Left Ventricular Internal Diameter Index (LVIDdI), Systolic Blood Pressure (SBP), Diastolic Blood Pressure (DBP), Distance from mitral valve closure point to basal segment of interventricular septum (MVCP-Sd), GLU, and Mitral Valve peak A (MV-
许可
CC BY 4.0
引用
学术使用请引用 DOI 10.1371/journal.pone.0325992.t002（source: https://plos.figshare.com/articles/dataset/ROC_analysis_of_model-related_variable_/29439121）。
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。
⚡️ 需要数据支持或标注服务？立即联系我们获取专业方案。
