Metrics with the dataset
用于中风预测和恰加斯病研究的不平衡数据分类数据集
基本信息
资源简介
Metrics with the dataset
【数据集背景】
本数据集源站标题为《Metrics with the dataset.》。源站首次发布：2025-05-13。
【数据内容】
源站原始描述：Machine learning has increasingly gained prominence in the healthcare sector due to its ability to address various challenges. However, a significant issue remains unresolved in this field: the handling of imbalanced data. This process is crucial for ensuring the efficiency of algorithms that utilize classification techniques, which are commonly applied in risk management, monitoring, diagnosis, and prognosis of patient health. This study conducts a comparative analysis of techniques for handlin…
【数据结构与技术规格】
文件数 1 个，合计 5.63 KB。Table 3.xls：application/vnd.ms-excel，5.63 KB。数据集 DOI：10.1371/journal.pone.0320966.t003。
【主题与分类】
源站学科分类：Biological Sciences not elsewhere classified、Information Systems not elsewhere classified、Mathematical Sciences not elsewhere classified、Science Policy。主题标签：particle swarm optimization、increasingly gained prominence、allocate available resources、address various challenges、reduce healthcare costs、healthcare sector due、new approach based、utilize classification techniques、handling imbalanced data、imbalanced data。
【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/Metrics_with_the_dataset_/29055732
使用许可：CC BY 4.0（https://creativecommons.org/licenses/by/4.0/）。
规范引用：Coimbra, André G.; Oliveira, Cleiane G.; Libório, Matheus P.; Mannan, Hasheem; Santos, Laercio I.; Fusco, Elisa; et al. (2025). Metrics with the 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：Metrics with the dataset.
- DOI：10.1371/journal.pone.0320966.t003
- 发布日期：2025-05-13
- 文件数：1 个
- 文件总体积：5.63 KB
- 源站页面：https://plos.figshare.com/articles/dataset/Metrics_with_the_dataset_/29055732
命令行下载
curl -L -o "Table 3.xls" "https://ndownloader.figshare.com/files/54509898"
文件清单
Table 3.xls（5.63 KB）
文件总体积 5.63 KB（源站 API 实测）。
数据加载示例（图像类）
from PIL import Image
import glob, os
files = (glob.glob(os.path.join(path, "**", "*.png"), recursive=True)
+ glob.glob(os.path.join(path, "**", "*.jpg"), recursive=True)
+ glob.glob(os.path.join(path, "**", "*.tif"), recursive=True))
print("图像文件数:", len(files))
img = Image.open(files[0]); print("尺寸/模式:", img.size, img.mode)
# torchvision Dataset 方式:
# from torchvision import datasets
# ds = datasets.ImageFolder(path) # 要求 子目录=类别
目录组织与标注格式以源站说明和下载后实际文件为准。
数据说明
Machine learning has increasingly gained prominence in the healthcare sector due to its ability to address various challenges. However, a significant issue remains unresolved in this field: the handling of imbalanced data. This process is crucial for ensuring the efficiency of algorithms that utilize classification techniques, which are commonly applied in risk management, monitoring, diagnosis, and prognosis of patient health. This study conducts a comparative analysis of techniques for handling imbalanced data and evaluates their effectiveness in combination with a set of classification algorithms, specifically focusing on stroke prediction. Additionally, a new approach based on Particle Swarm Optimization (PSO) and Naive Bayes was proposed. This approach was applied to the real problem of Chagas disease. The application of these techniques aims to improve the quality of life for individuals, reduce healthcare costs, and allocate available resources more efficiently, making it a preventive action.
许可
CC BY 4.0
引用
学术使用请引用 DOI 10.1371/journal.pone.0320966.t003（source: https://plos.figshare.com/articles/dataset/Metrics_with_the_dataset_/29055732）。
数据缺失？
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