Abstract Details

Title Conversational Coherence in Alzheimer's Disease and Mild Cognitive Impairment: A Decision Tree Classifier Analysis

Topic Aging, Dementia, and Behavioral Neurology

Presentation(s) S1 - Alzheimer’s Disease and Related Dementias (1:24 PM-1:36 PM)

Poster/Presentation
Number 003

Objective To leverage trustworthy, interpretable machine learning techniques by evaluating a Decision Tree classification of spontaneous conversational speech samples of participants with Alzheimer's Disease (AD), Amnestic Mild Cognitive Impairment (aMCI), and healthy controls.

Background

There is an increasing need for early and accurate screening for Alzheimer's Disease (AD) and Amnestic Mild Cognitive Impairment (aMCI). Machine learning applied to spontaneous speech is emerging as a promising, cost-effective, and rapid screening method. However, machine learning methods such as artificial neural networks are often challenging for clinicians to interpret.

Design/Methods A cohort of 153 participants was analyzed: 59 AD, 30 aMCI, and 64 healthy controls (HC), all frequency-matched for age and gender. Conversational samples, averaging two minutes, represented typical clinical interactions. Speech features were distilled and the most salient identified using an ANOVA F-test. A Decision Tree Classifier was employed, focusing on 'coherence'—the logical flow and consistency of conversation, operationalized as the cosine distance between word embedding representations of text. Coherence was quantified using GloVe, capturing semantic relationships between words. Different comparison approaches (e.g., between successive windows of text vs within single windows of text), window lengths (i.e., n-grams), and statistics (e.g., average, minimum, maximum, and standard deviation) were considered to identify patterns.

Results

The classifier registered an accuracy of approximately 95.42%, with a sensitivity of 96.63% for aMCI/AD and a specificity of 93.75% for HC.

Conclusions AD and aMCI groups demonstrated heightened conversational coherence compared to HC. Such deviations could indicate shifts in cognitive strategies or neural pathways. The Decision Tree Classifier provides both transparency and commendable performance. Validation in independent samples remains pivotal for ensuring its broader applicability and robustness.

Authors/Disclosures
Peter S. Pressman, MD (Univeristy of Colorado School of Medicine) PRESENTER	The institution of Dr. Pressman has received research support from the Doris Duke Fund to Retain Clinical Scientists . Dr. Pressman has received personal compensation in the range of $100,000-$499,999 for serving as a Associate Professor with University of Colorado School of Medicine.
Chelsea Chandler	No disclosure on file
Francesca Dino (University of Colorado, Anschutz)	No disclosure on file
Peter Foltz (University of Colorado)	No disclosure on file

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