Identification of preclinical dementia according to ATN classification for stratified trial recruitment: A machine learning approach.

ABSTRACT:

Introduction

The Amyloid/Tau/Neurodegeneration (ATN) framework was proposed to identify the preclinical biological state of Alzheimer's disease (AD). We investigated whether ATN phenotype can be predicted using routinely collected research cohort data.

Methods

927 EPAD LCS cohort participants free of dementia or Mild Cognitive Impairment were separated into 5 ATN categories. We used machine learning (ML) methods to identify a set of significant features separating each neurodegeneration-related group from controls (A-T-(N)-). Random Forest and linear-kernel SVM with stratified 5-fold cross validations were used to optimize model whose performance was then tested in the ADNI database.

Results

Our optimal results outperformed ATN cross-validated logistic regression mode

SUBMITTER: Koychev I 

PROVIDER: S-EPMC10586674 | biostudies-literature | 2023

REPOSITORIES: biostudies-literature