心脏数据集上的比较结果
包含OMWOA算法与KELM模型在心脏数据集上进行疾病诊断的比较实验结果，以表格形式呈现。
基本信息
资源简介
心脏数据集上的比较结果
【数据集背景】
本数据集源站标题为《Comparison results on the heart dataset.》。源站首次发布：2025-06-03。
【数据内容】
源站原始描述：Swarm intelligence optimization algorithms represent a significant branch of nature-inspired computational methods, designed to solve complex optimization problems by simulating the collective behavior of biological systems. Whale optimization algorithm (WOA) is a newly developed meta-heuristic algorithm, which is mainly based on the predation behavior of humpback whales in the ocean. This study proposes an enhanced version of the WOA, named the Outpost-based Multi-population Whale Optimization…
【数据结构与技术规格】
文件数 1 个，合计 9.73 KB。Table 12.xls：application/vnd.ms-excel，9.73 KB。数据集 DOI：10.1371/journal.pone.0325272.t012。
【主题与分类】
源站学科分类：Space Science、Biological Sciences not elsewhere classified、Information Systems not elsewhere classified、Mathematical Sciences not elsewhere classified。主题标签：newly developed meta、inspired computational methods、ieee cec 2017、experimental results demonstrate、art evolutionary algorithms、algorithm &# 8217、whale optimization algorithm、omwoa &# 8217、population enhanced mechanism、proposed algorithm。
【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/Comparison_results_on_the_heart_dataset_/29230052
使用许可：CC BY 4.0（https://creativecommons.org/licenses/by/4.0/）。
规范引用：Tang, Kankan; Zhang, Lin (2025). Comparison results on the heart 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：Comparison results on the heart dataset.
- DOI：10.1371/journal.pone.0325272.t012
- 发布日期：2025-06-03
- 文件数：1 个
- 文件总体积：9.73 KB
- 源站页面：https://plos.figshare.com/articles/dataset/Comparison_results_on_the_heart_dataset_/29230052
命令行下载
curl -L -o "Table 12.xls" "https://ndownloader.figshare.com/files/55093721"
文件清单
Table 12.xls（9.73 KB）
文件总体积 9.73 KB（源站 API 实测）。
数据说明
Swarm intelligence optimization algorithms represent a significant branch of nature-inspired computational methods, designed to solve complex optimization problems by simulating the collective behavior of biological systems. Whale optimization algorithm (WOA) is a newly developed meta-heuristic algorithm, which is mainly based on the predation behavior of humpback whales in the ocean. This study proposes an enhanced version of the WOA, named the Outpost-based Multi-population Whale Optimization Algorithm (OMWOA), which integrates two key mechanisms: the outpost mechanism and a multi-population enhanced mechanism. These modifications aim to improve the algorithm’s performance in terms of solution accuracy and convergence rate. The effectiveness of OMWOA is thoroughly evaluated by benchmarking it against state-of-the-art evolutionary algorithms from the IEEE CEC 2017 and IEEE CEC 2022 competitions. Additionally, this study provides a detailed analysis of the influence of the outpost and multi-population mechanisms on OMWOA’s performance, as well as its scalability in problems of varying dimensionalities. To validate its applicability in real-world problems, the proposed algorithm is combined with Kernel Extreme Learning Machine (KELM) for solving medical disease diagnosis tasks. The experimental results demonstrate the superior performance of OMWOA in terms of diagnostic accuracy across five medical datasets, highlighting its potential for real-world applications.
许可
CC BY 4.0
引用
学术使用请引用 DOI 10.1371/journal.pone.0325272.t012（source: https://plos.figshare.com/articles/dataset/Comparison_results_on_the_heart_dataset_/29230052）。
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。
