ArticleAlzheimer's & dementia : the journal of the Alzheimer's Association2025
Classification of AD and bvFTD using neuropsychological and neuropsychiatric variables: a machine learning study.
Article in Alzheimer's & dementia : the journal of the Alzheimer's Association, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.
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Who cites it
4 citing papers in PubMed.
- Memory, social cognition, and beyond: Neuropsychological markers differentiating Alzheimer's disease and frontotemporal dementia.Journal of Alzheimer's disease : JAD · 2026Review
- Hybrid multimodal late fusion frameworks for bvFTD classification in imbalanced dementia datasets.Frontiers in aging neuroscience · 2026Article
- The ICN-UN Battery: A Machine Learning-Optimized Tool for Expeditious Alzheimer's Disease Diagnosis.Diagnostics (Basel, Switzerland) · 2025Article
- Classification of AD and bvFTD using neuropsychological and neuropsychiatric variables: a machine learning study.Alzheimer's & dementia : the journal of the Alzheimer's Association · 2025Article
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5 authors.
Funding
Abstract
introductionMachine 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.
methodsData 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.
resultsAmong 1616 participants (AD = 1498, bvFTD = 118), all algorithms achieved high accuracy (80% to 90%) and discriminatory ability (AUC = 0.89 to 0.95). Apathy, disinhibition, and digit-symbol substitution were the most important classification features. DISCUSSION: Findings emphasize the value of specific clinical disease markers to support differential diagnosis of AD and bvFTD. HIGHLIGHTS: Four ML algorithms were used for the classification of AD and bvFTD. Neuropsychological subtests and neuropsychiatric symptoms were input features. Models had high classification accuracy and discrimination. We identified important and accessible clinical features for classification.
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