A compact 15-feature set using LLM-generated content coverage and TF-IDF class similarities reaches 85.4% accuracy for Alzheimer's detection on ADReSS, beating 40 traditional linguistic features.
Devising a Set of Compact and Explainable Spoken Language Feature for Screening Alzheimer's Disease
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abstract
Alzheimer's disease (AD) has become one of the most significant health challenges in an aging society. The use of spoken language-based AD detection methods has gained prevalence due to their scalability due to their scalability. Based on the Cookie Theft picture description task, we devised an explainable and effective feature set that leverages the visual capabilities of a large language model (LLM) and the Term Frequency-Inverse Document Frequency (TF-IDF) model. Our experimental results show that the newly proposed features consistently outperform traditional linguistic features across two different classifiers with high dimension efficiency. Our new features can be well explained and interpreted step by step which enhance the interpretability of automatic AD screening.
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Devising a Set of Compact and Explainable Spoken Language Feature for Screening Alzheimer's Disease
A compact 15-feature set using LLM-generated content coverage and TF-IDF class similarities reaches 85.4% accuracy for Alzheimer's detection on ADReSS, beating 40 traditional linguistic features.