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Activity Number: 278 - Combining Markers for Classification in Practical Tasks
Type: Invited
Date/Time: Tuesday, July 31, 2018 : 8:30 AM to 10:20 AM
Sponsor: Section on Medical Devices and Diagnostics
Abstract #326601
Title: WITHDRAWN: Combining Biomarkers to Improve Classification Accuracy Under Heterogeneous Transformations
Author(s): Aiyi Liu and Wei Zhang
Keywords: Diagnostic Accuracy; Area Under ROC Curve; Likelihood Ratio; Box-Cox Transformation ; Case-Control Studies; Biomarkers

Autism is a pervasive developmental disorder characterized by impairments in communication, language, and reciprocal social interaction and by patterns of restricted and repetitive interests or behaviours, as de?ned by the Diagnostic and Statistical Manual of Mental Disorders DSM-IV-TR. Several growth-related hormonal biomarkers including insulin-like growth factors and growth hormone binding protein have been shown to significantly elevated among autistic children.

This paper addresses the issue of combing multiple biomarkers such as growth-related hormonal biomarkers to improve diagnostic accuracy of a disease, such as Autism. Among existing methods in the literature, linear combination is perhaps the most popular and practical method for combining biomarkers achieve better diagnostic accuracy. Unlike the likelihood ratio, however, the optimal linear combination is not transformation-invariant. We investigate the effect of transformations on the combination and accuracy and proposed corresponding combination methods. Growth-related hormone data from a case-control study on autistic children are used to exemplify the methods.

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