使用数据挖掘技术预测儿科哮喘住院
基于澳大利亚维多利亚州Barwon地区5-11岁儿童哮喘住院数据,使用数据挖掘技术进行住院预测的研究数据集。

基本信息
资源简介
使用数据挖掘技术预测儿科哮喘住院
【数据集背景】
本数据集源站标题为《Prediction of paediatric asthma hospitalisation using data mining techniques》。源站首次发布:2017-11-21。
【数据内容】
源站原始描述:Research into the prevalence of hospitalisation among childhood asthma cases is undertaken, using a data set local to the Barwon region of Victoria. Participants were the parents/guardians on behalf of children aged between 5-11 years. Various data mining techniques are used, including segmentation, association and classification to assist in predicting and exploring the instances of childhood hospitalisation due to asthma. Results from this study indicate that children in inner city and metropo…
【数据结构与技术规格】
文件数 1 个,合计 628.05 KB。monash_7865.pdf:application/pdf,628.05 KB。数据集 DOI:10.26180/5619517.v1。
【主题与分类】
源站学科分类:Bioinformatics and computational biology not elsewhere classified、Applications in life sciences、Pattern recognition、Data mining and knowledge discovery。主题标签:Bioinformatics Congresses、Computational biology Congresses、Computer vision in medicine Congresses、Computational biology Methods Congresses、Pattern recognition, automated Methods Congresses、Asthma、Epidemiology、Hospitalisation、Data mining、2008。
【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://bridges.monash.edu/articles/dataset/Prediction_of_paediatric_asthma_hospitalisation_using_data_mining_techniques/5619517
使用许可:In Copyright(http://rightsstatements.org/vocab/InC/1.0/)。
规范引用:Schmidt, Sam; Li, Gang; Chen, Yi-Ping Phoebe (2017). Prediction of paediatric asthma hospitalisation using data mining techniques. Monash University. Dataset
【使用注意】
· 源站最后修改于 2026-02-12,引用时请注意版本时效。
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:Prediction of paediatric asthma hospitalisation using data mining techniques
- DOI:10.26180/5619517.v1
- 发布日期:2017-11-21
- 文件数:1 个
- 文件总体积:628.05 KB
- 源站页面:https://bridges.monash.edu/articles/dataset/Prediction_of_paediatric_asthma_hospitalisation_using_data_mining_techniques/5619517
命令行下载
curl -L -o "monash_7865.pdf" "https://ndownloader.figshare.com/files/9783271"
文件清单
monash_7865.pdf(628.05 KB)
文件总体积 628.05 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
Research into the prevalence of hospitalisation among childhood asthma cases is undertaken, using a data set local to the Barwon region of Victoria. Participants were the parents/guardians on behalf of children aged between 5-11 years. Various data mining techniques are used, including segmentation, association and classification to assist in predicting and exploring the instances of childhood hospitalisation due to asthma. Results from this study indicate that children in inner city and metropolitan areas may overutilise emergency department services. In addition, this study found that the prediction of hospitalisaion for asthma in children was greater for those with a written asthma management plan. PRIB 2008 proceedings found at: http://dx.doi.org/10.1007/978-3-540-88436-1
Contributors: Monash University. Faculty of Information Technology. Gippsland School of Information Technology ;
Chetty, Madhu ;
Ahmad, Shandar ;
Ngom, Alioune ;
Teng, Shyh Wei ;
Third IAPR International Conference on Pattern Recognition in Bioinformatics (PRIB) (3rd : 2008 : Melbourne, Australia) ;
Coverage:
Rights: Copyright by Third IAPR International Conference on Pattern Recognition in Bioinformatics. All rights reserved.
许可
In Copyright
引用
学术使用请引用 DOI 10.26180/5619517.v1(source: https://bridges.monash.edu/articles/dataset/Prediction_of_paediatric_asthma_hospitalisation_using_data_mining_techniques/5619517)。
数据缺失?
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