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

  • Authors:

  • Yuko Araki;Atsushi Kawaguchi;Fumio Yamashita

  • Affiliations:

  • Biostatistics Center, Kurume University, Kurume, Japan 830-0011;Biostatistics Center, Kurume University, Kurume, Japan 830-0011;Division of Ultrahigh Field MRI, Institute for Biomedical Sciences, Iwate Medical University, Morioka, Japan 020-8505

  • Venue:
  • Advances in Data Analysis and Classification
  • Year:
  • 2013

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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.