Classification of AD and bvFTD using neuropsychological and neuropsychiatric variables: a machine learning study.

ABSTRACT:

Introduction

Machine learning (ML) is increasingly used for clinical classification of Alzheimer's disease (AD) and related dementias. Prior studies identified useful diagnostic features for AD and behavioral variant frontotemporal dementia (bvFTD), though they often lack pathological verification. We applied ML to classify AD and bvFTD autopsy status using initial visit neuropsychological and neuropsychiatric data.

Methods

Data from the National Alzheimer's Coordinating Center Uniform Data Set and Neuropathology Data Set were analyzed using logistic regression, support vector machines, random forest, and artificial neural networks to classify autopsy-confirmed diagnosis based on symptom and cognitive data.

Results

Among 1616 participants (AD = 1498, bvFTD = 118), all

SUBMITTER: Goodwin GJ 

PROVIDER: S-EPMC12538632 | biostudies-literature | 2025 Oct

REPOSITORIES: biostudies-literature