article

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

Authors:

Napatkamon Ayutyanont,

Hillary Protas,

William Jagust,

Kewei ChenAuthors Info & Claims

IEEE/ACM Transactions on Computational Biology and Bioinformatics (TCBB), Volume 10, Issue 1

Pages 173 - 180

https://doi.org/10.1109/TCBB.2012.141

Published: 01 January 2013 Publication History

Abstract

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.

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Cited By

Ashish NPatawari AChhabra SToga A(2016)Name Similarity for Composite Element Name MatchingProceedings of the 7th ACM International Conference on Bioinformatics, Computational Biology, and Health Informatics10.1145/2975167.2975203(345-354)Online publication date: 2-Oct-2016
https://dl.acm.org/doi/10.1145/2975167.2975203

Index Terms

The Receiver Operational Characteristic for Binary Classification with Multiple Indices and Its Application to the Neuroimaging Study of Alzheimer's Disease
1. Applied computing
  1. Life and medical sciences
2. Computing methodologies
  1. Machine learning
    1. Learning paradigms
      1. Supervised learning
        Supervised learning by classification
    2. Machine learning approaches
      1. Classification and regression trees

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cover image IEEE/ACM Transactions on Computational Biology and Bioinformatics

IEEE/ACM Transactions on Computational Biology and Bioinformatics Volume 10, Issue 1

January 2013

270 pages

ISSN:1545-5963

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IEEE Computer Society Press

Washington, DC, United States

Publication History

Published: 01 January 2013

Published in TCBB Volume 10, Issue 1

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Cited By

Ashish NPatawari AChhabra SToga A(2016)Name Similarity for Composite Element Name MatchingProceedings of the 7th ACM International Conference on Bioinformatics, Computational Biology, and Health Informatics10.1145/2975167.2975203(345-354)Online publication date: 2-Oct-2016
https://dl.acm.org/doi/10.1145/2975167.2975203

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