Regularized logistic discrimination with basis expansions for the early detection of Alzheimer’s disease based on three-dimensional MRI data

Yuko Araki (), Atsushi Kawaguchi and Fumio Yamashita

Advances in Data Analysis and Classification, 2013, vol. 7, issue 1, 109-119

Abstract: In recent years, evidence has emerged indicating that magnetic resonance imaging (MRI) brain scans provide valuable diagnostic information about Alzheimer’s disease. It has been shown that MRI brain scans are capable of both diagnosing Alzheimer’s disease itself at an early stage and identifying people at risk of developing Alzheimer’s. In this article, we have investigated statistical methods for classifying Alzheimer’s disease patients based on three-dimensional MRI data via L2-type regularized logistic discrimination with basis expansions. Preceding studies adopted an open approach when applying three-dimensional data analysis. Our proposed classification model with dimension reduction techniques offers discriminant functions with excellent prediction performance in terms of sensitivity and specificity. Copyright Springer-Verlag Berlin Heidelberg 2013

Keywords: Regularized logistic discrimination; Three-dimensional MRI data; Alzheimer’s disease; Dimension reduction; Tuning parameter selection; 97K80 (search for similar items in EconPapers)
Date: 2013
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Persistent link: https://EconPapers.repec.org/RePEc:spr:advdac:v:7:y:2013:i:1:p:109-119

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DOI: 10.1007/s11634-013-0127-5

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