ANOVA results for total time
糖尿病视网膜病变多模态融合实验的总时间ANOVA结果表格数据。
基本信息
资源简介
ANOVA results for total time
【数据集背景】
本数据集源站标题为《ANOVA results for total time.》。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 15.xls：application/vnd.ms-excel，5.63 KB。数据集 DOI：10.1371/journal.pone.0339864.t015。
【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_total_time_p_/30991785
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 total time.. 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 total time.</p>
- DOI：10.1371/journal.pone.0339864.t015
- 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_total_time_p_/30991785
命令行下载
curl -L -o "Table 15.xls" "https://ndownloader.figshare.com/files/60756267"
文件清单
Table 15.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.t015（source: https://plos.figshare.com/articles/dataset/_p_ANOVA_results_for_total_time_p_/30991785）。
数据缺失？
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