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

Journal article

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 models by between 2.2% and 8.3%. The optimal feature sets were not consistent across the 4 models with the AD pathologic change vs controls set differing the most from the rest. Because of that we have identified a subset of 10 features that yield results very close or identical to the optimal. DISCUSSION: Our study demonstrates the gains offered by ML in generating ATN risk prediction over logistic regression models among pre-dementia individuals.

Authors: Koychev, I; Marinov, E; Young, S; Lazarova, S; Grigorova, D

• Publication status: Published
• Peer review status: Peer reviewed
• Publisher: Public Library of Science
• Journal: PLoS ONE
• Volume: 18
• Issue: 10
• Pages: e0288039-e0288039
• Publication date: 2023-10-19
• DOI: 10.1371/journal.pone.0288039
• EISSN: 1932-6203
• ISSN: 1932-6203
• Language: English
• Keywords: Support vector machine; Machine learning; Cohort; Pathology; Artificial intelligence; Neurodegeneration; Random forest; Medicine; Dementia; Disease; Computer science; Logistic regression