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

Author

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  • Yuko Araki
  • Atsushi Kawaguchi
  • Fumio Yamashita

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

Suggested Citation

  • Yuko Araki & Atsushi Kawaguchi & Fumio Yamashita, 2013. "Regularized logistic discrimination with basis expansions for the early detection of Alzheimer’s disease based on three-dimensional MRI data," Advances in Data Analysis and Classification, Springer;German Classification Society - Gesellschaft für Klassifikation (GfKl);Japanese Classification Society (JCS);Classification and Data Analysis Group of the Italian Statistical Society (CLADAG);International Federation of Classification Societies (IFCS), vol. 7(1), pages 109-119, March.
    • Handle: RePEc:spr:advdac:v:7:y:2013:i:1:p:109-119
    DOI: 10.1007/s11634-013-0127-5
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    References listed on IDEAS

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    1. Philip T. Reiss & R. Todd Ogden, 2010. "Functional Generalized Linear Models with Images as Predictors," Biometrics, The International Biometric Society, vol. 66(1), pages 61-69, March.
    2. Simon N. Wood, 2011. "Fast stable restricted maximum likelihood and marginal likelihood estimation of semiparametric generalized linear models," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 73(1), pages 3-36, January.
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    Cited by:

    1. Philip T. Reiss & Jeff Goldsmith & Han Lin Shang & R. Todd Ogden, 2017. "Methods for Scalar-on-Function Regression," International Statistical Review, International Statistical Institute, vol. 85(2), pages 228-249, August.

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