首页> 外文期刊>IEEE/ACM transactions on computational biology and bioinformatics >The Receiver Operational Characteristic for Binary Classification with Multiple Indices and Its Application to the Neuroimaging Study of Alzheimer's Disease
【24h】

The Receiver Operational Characteristic for Binary Classification with Multiple Indices and Its Application to the Neuroimaging Study of Alzheimer's Disease

机译:具有多个指标的二进制分类的接收器操作特征及其在阿尔茨海默氏病神经成像研究中的应用

获取原文
获取原文并翻译 | 示例

摘要

Given a single index, the receiver operational characteristic (ROC) curve analysis is routinely utilized for characterizing performances in distinguishing two conditions/groups in terms of sensitivity and specificity. Given the availability of multiple data sources (referred to as multi-indices), such as multimodal neuroimaging data sets, cognitive tests, and clinical ratings and genomic data in Alzheimer's disease (AD) studies, the single-index-based ROC underutilizes all available information. For a long time, a number of algorithmic/analytic approaches combining multiple indices have been widely used to simultaneously incorporate multiple sources. In this study, we propose an alternative for combining multiple indices using logical operations, such as "AND,” "OR,” and "at least $(n)$” (where $(n)$ is an integer), to construct multivariate ROC (multiV-ROC) and characterize the sensitivity and specificity statistically associated with the use of multiple indices. With and without the "leave-one-out” cross-validation, we used two data sets from AD studies to showcase the potentially increased sensitivity/specificity of the multiV-ROC in comparison to the single-index ROC and linear discriminant analysis (an analytic way of combining multi-indices). We conclude that, for the data sets we investigated, the proposed multiV-ROC approach is capable of providing a natural and practical alternative with improved classification accuracy as compared to univariate ROC and linear discriminant analysis.
机译:在给定一个指标的情况下,通常使用接收机工作特性(ROC)曲线分析来表征在区分灵敏度和特异性方面的两个条件/组时的性能。鉴于在阿尔茨海默氏病(AD)研究中有多个数据源(称为多索引)(例如多模式神经影像数据集,认知测试以及临床评分和基因组数据)的可用性,基于单索引的ROC未能充分利用所有可用数据信息。长期以来,许多将多个索引结合在一起的算法/分析方法已被广泛用于同时合并多个来源。在这项研究中,我们提出了使用逻辑运算(例如“ AND”,“ OR”和“至少$(n)$”(其中$(n)$是整数))组合多个索引的替代方法,以构造多变量ROC(multiV-ROC)并表征与使用多个指标相关的统计学敏感性和特异性在有和没有“留一法”交叉验证的情况下,我们使用了来自AD研究的两个数据集来展示潜在的增加与单指标ROC和线性判别分析(组合多个指标的分析方法)相比,multiV-ROC的敏感性/特异性。我们得出结论,对于我们调查的数据集,与单变量ROC和线性判别分析相比,拟议的multiV-ROC方法能够提供一种自然且实用的替代方法,并具有提高的分类精度。

著录项

相似文献

  • 外文文献
  • 中文文献
  • 专利
获取原文

客服邮箱:kefu@zhangqiaokeyan.com

京公网安备:11010802029741号 ICP备案号:京ICP备15016152号-6 六维联合信息科技 (北京) 有限公司©版权所有
  • 客服微信

  • 服务号