ANOVA准确性结果
用于多模态糖尿病视网膜病变诊断方法准确性评估的方差分析结果表格。
基本信息
资源简介
ANOVA准确性结果
【一句话概览】
本研究提出一种新型多模态模糊知识图谱框架 FKG-MM，用于整合多来源、多模态医疗数据，以应对多模态医疗数据融合及机器学习在医疗数据不确定性与多样性下的表示和计算困难。基于模糊知识图谱模型，利用 BRSET 多模态糖尿病视网膜病变数据集开展实验，验证图像特征与表格医疗数据特征结合时，特征选择方法在五种方法中可靠性最高；并确认结合图像数据与表格医疗数据后，FKG-MM 准确率较仅诊断表格数据的相关方法提高 12–14%（来源 figshare，CC BY 4.0 许可）。
【数据集背景】
本数据集源站标题为《ANOVA results for accuracy.》。源站首次发布：2026-01-02。
【数据内容】
源站原始描述：Artificial Intelligence (AI) has been dramatically applied to healthcare in various tasks to support clinicians in disease diagnosis and prognosis. It has been known that accurate diagnosis must be drawn from multiple evidence, namely clinical records, X-Ray images, IoT data, etc called the multi-modal data. Despite the existence of various approaches for multi-modal medical data fusion, the development of comprehensive systems capable of integrating data from multiple sources and modalities rem…
【数据结构与技术规格】
文件数 1 个，合计 5.63 KB。Table 9.xls：application/vnd.ms-excel，5.63 KB。数据集 DOI：10.1371/journal.pone.0339864.t009。
【主题与分类】
源站学科分类：Space Science、Biotechnology、Biological Sciences not elsewhere classified、Information Systems not elsewhere classified、Science Policy。主题标签：related methods diagnosing、namely clinical records、experiment scenarios utilizing、experiment also confirms、comprehensive systems capable、combining image features、12 &# 8211、modal diabetic retinopathy、accurate diagnosis must、feature selection method。
【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。
【获取与许可】
源站页面：https://plos.figshare.com/articles/dataset/_p_ANOVA_results_for_accuracy_p_/30991767
使用许可：CC BY 4.0（https://creativecommons.org/licenses/by/4.0/）。
规范引用：Tan, Nguyen Hong; Tuan, Tran Manh; Chuan, Pham Minh; Hoang, Nguyen Duc; Thanh, Le Quang; Son, Le Hoang (2026). ANOVA results for accuracy.. PLOS ONE. Dataset
下载信息
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使用方式
数据集获取（Figshare）
- 源站标题：<p>ANOVA results for accuracy.</p>
- DOI：10.1371/journal.pone.0339864.t009
- 发布日期：2026-01-02
- 文件数：1 个
- 文件总体积：5.63 KB
- 源站页面：https://plos.figshare.com/articles/dataset/_p_ANOVA_results_for_accuracy_p_/30991767
命令行下载
curl -L -o "Table 9.xls" "https://ndownloader.figshare.com/files/60756249"
文件清单
Table 9.xls（5.63 KB）
文件总体积 5.63 KB（源站 API 实测）。
数据说明
Artificial Intelligence (AI) has been dramatically applied to healthcare in various tasks to support clinicians in disease diagnosis and prognosis. It has been known that accurate diagnosis must be drawn from multiple evidence, namely clinical records, X-Ray images, IoT data, etc called the multi-modal data. Despite the existence of various approaches for multi-modal medical data fusion, the development of comprehensive systems capable of integrating data from multiple sources and modalities remains a considerable challenge. Besides, many machine learning models face difficulties in representation and computation due to the uncertainty and diversity of medical data. This study proposes a novel multi-modal fuzzy knowledge graph framework, called FKG-MM, which integrates multi-modal medical data from multiple sources, offering enhanced computational performance compared to unimodal data. In addition, the FKG-MM framework is based on the fuzzy knowledge graph model, one of the models that represent and compute effectively with medical data in tabular form. Through some experiment scenarios utilizing the well-known BRSET dataset on multi-modal diabetic retinopathy, it has been experimentally validated that the feature selection method, when combining image features with tabular medical data features, gives the highest reliability results among 5 methods including Feature Selection Method, Tensor Product, Hadamard Product, Filter Selection, and Wrapper Selection. In addition, the experiment also confirms that the accuracy of FKG-MM increases by 12–14% when combining image data with tabular medical data than the related methods diagnosing only on tabular data.
许可
CC BY 4.0
引用
学术使用请引用 DOI 10.1371/journal.pone.0339864.t009（source: https://plos.figshare.com/articles/dataset/_p_ANOVA_results_for_accuracy_p_/30991767）。
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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