2型糖尿病数据库中缺失的数据估计分析

M. Giardina, Yongyang Huo, F. Azuaje, P. Mccullagh, R. Harper
{"title":"2型糖尿病数据库中缺失的数据估计分析","authors":"M. Giardina, Yongyang Huo, F. Azuaje, P. Mccullagh, R. Harper","doi":"10.1109/CBMS.2005.13","DOIUrl":null,"url":null,"abstract":"Type II diabetes is one of the most common causes of disability and death in the United Kingdom. This investigation analysed data acquired from diabetic patients at the Ulster Hospital in Northern Ireland in terms of statistical descriptive indicators and missing values. Such data are noisy and incomplete. This paper reports a comprehensive missing data estimation analysis. Five missing value imputation methods were compared, including k-Nearest Neighbours (k-NN) and correlation-based estimation models. From this analysis it can be concluded that a feature-based correlation method known as EMImpute/spl I.bar/Columns is a promising approach to estimating missing values. Nevertheless, k-NN methods may be useful to provide relatively accurate estimations with lower error variability. These estimation techniques will support the implementation of supervised and unsupervised learning tools for coronary heart disease risk assessment, a major complication of diabetes.","PeriodicalId":119367,"journal":{"name":"18th IEEE Symposium on Computer-Based Medical Systems (CBMS'05)","volume":"149 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2005-06-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"10","resultStr":"{\"title\":\"A missing data estimation analysis in type II diabetes databases\",\"authors\":\"M. Giardina, Yongyang Huo, F. Azuaje, P. Mccullagh, R. Harper\",\"doi\":\"10.1109/CBMS.2005.13\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"Type II diabetes is one of the most common causes of disability and death in the United Kingdom. This investigation analysed data acquired from diabetic patients at the Ulster Hospital in Northern Ireland in terms of statistical descriptive indicators and missing values. Such data are noisy and incomplete. This paper reports a comprehensive missing data estimation analysis. Five missing value imputation methods were compared, including k-Nearest Neighbours (k-NN) and correlation-based estimation models. From this analysis it can be concluded that a feature-based correlation method known as EMImpute/spl I.bar/Columns is a promising approach to estimating missing values. Nevertheless, k-NN methods may be useful to provide relatively accurate estimations with lower error variability. These estimation techniques will support the implementation of supervised and unsupervised learning tools for coronary heart disease risk assessment, a major complication of diabetes.\",\"PeriodicalId\":119367,\"journal\":{\"name\":\"18th IEEE Symposium on Computer-Based Medical Systems (CBMS'05)\",\"volume\":\"149 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2005-06-23\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"10\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"18th IEEE Symposium on Computer-Based Medical Systems (CBMS'05)\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/CBMS.2005.13\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"18th IEEE Symposium on Computer-Based Medical Systems (CBMS'05)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/CBMS.2005.13","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 10

摘要

2型糖尿病是英国最常见的致残和死亡原因之一。这项调查分析了从北爱尔兰阿尔斯特医院的糖尿病患者获得的统计描述性指标和缺失值方面的数据。这样的数据是嘈杂和不完整的。本文报道了一种全面的缺失数据估计分析。比较了5种缺失值估计方法,包括k-最近邻(k-NN)和基于相关的估计模型。从这个分析可以得出结论,基于特征的相关方法,即EMImpute/spl .bar/Columns,是估计缺失值的一种很有前途的方法。然而,k-NN方法可能有助于提供相对准确的估计和较低的误差变异性。这些估计技术将支持实施冠心病风险评估的监督和无监督学习工具,冠心病是糖尿病的主要并发症。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
A missing data estimation analysis in type II diabetes databases
Type II diabetes is one of the most common causes of disability and death in the United Kingdom. This investigation analysed data acquired from diabetic patients at the Ulster Hospital in Northern Ireland in terms of statistical descriptive indicators and missing values. Such data are noisy and incomplete. This paper reports a comprehensive missing data estimation analysis. Five missing value imputation methods were compared, including k-Nearest Neighbours (k-NN) and correlation-based estimation models. From this analysis it can be concluded that a feature-based correlation method known as EMImpute/spl I.bar/Columns is a promising approach to estimating missing values. Nevertheless, k-NN methods may be useful to provide relatively accurate estimations with lower error variability. These estimation techniques will support the implementation of supervised and unsupervised learning tools for coronary heart disease risk assessment, a major complication of diabetes.
求助全文
通过发布文献求助,成功后即可免费获取论文全文。 去求助
来源期刊
自引率
0.00%
发文量
0
×
引用
GB/T 7714-2015
复制
MLA
复制
APA
复制
导出至
BibTeX EndNote RefMan NoteFirst NoteExpress
×
提示
您的信息不完整,为了账户安全,请先补充。
现在去补充
×
提示
您因"违规操作"
具体请查看互助需知
我知道了
×
提示
确定
请完成安全验证×
copy
已复制链接
快去分享给好友吧!
我知道了
右上角分享
点击右上角分享
0
联系我们:info@booksci.cn Book学术提供免费学术资源搜索服务,方便国内外学者检索中英文文献。致力于提供最便捷和优质的服务体验。 Copyright © 2023 布克学术 All rights reserved.
京ICP备2023020795号-1
ghs 京公网安备 11010802042870号
Book学术文献互助
Book学术文献互助群
群 号:481959085
Book学术官方微信