A Bayesian group lasso classification for ADNI volumetrics data

Document Type

Article

Publication Date

10-1-2021

Abstract

The primary objective of this paper is to develop a statistically valid classification procedure for analyzing brain image volumetrics data obtained from the Alzheimer’s Disease Neuroimaging Initiative (ADNI) in elderly subjects with cognitive impairments. The Bayesian group lasso method thereby proposed for logistic regression efficiently selects an optimal model with the use of a spike and slab type prior. This method selects groups of attributes of a brain subregion encouraged by the group lasso penalty. We conduct simulation studies for high- and low-dimensional scenarios where our method is always able to select the true parameters that are truly predictive among a large number of parameters. The method is then applied on dichotomous response ADNI data which selects predictive atrophied brain regions and classifies Alzheimer’s disease patients from healthy controls. Our analysis is able to give an accuracy rate of 80% for classifying Alzheimer’s disease. The suggested method selects 29 brain subregions. The medical literature indicates that all these regions are associated with Alzheimer’s patients. The Bayesian method of model selection further helps selecting only the subregions that are statistically significant, thus obtaining an optimal model.

Identifier

85113995792 (Scopus)

Publication Title

Statistical Methods in Medical Research

External Full Text Location

https://doi.org/10.1177/09622802211022404

e-ISSN

14770334

ISSN

09622802

PubMed ID

34460337

First Page

2207

Last Page

2220

Issue

10

Volume

30

Fund Ref

Alzheimer's Disease Neuroimaging Initiative

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