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Using Verb Fluency, Natural Language Processing, and Machine Learning to Detect Alzheimer's Disease.

Aradhana Soni, Benjamin Amrhein, Matthew Baucum

    Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference
    |December 11, 2021
    PubMed
    Summary

    Early Alzheimer's disease (AD) detection is crucial. A verb fluency (VF) task combined with machine learning models accurately identifies AD risk, offering a powerful new diagnostic approach.

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    Area of Science:

    • Neuroscience
    • Computational Biology
    • Cognitive Psychology

    Background:

    • Alzheimer's disease (AD) significantly impairs memory and cognition.
    • Early detection and intervention are critical for managing AD due to the lack of a cure.
    • Verbal fluency tasks are sensitive neuropsychological tools for assessing cognitive decline in AD.

    Purpose of the Study:

    • To develop and evaluate an approach for detecting Alzheimer's disease (AD) risk using a verb fluency (VF) task.
    • To apply machine learning techniques for analyzing VF task data to differentiate between AD patients and controls.
    • To compare the efficacy of different machine learning models in AD detection based on VF performance.

    Main Methods:

    • Utilized a verb fluency (VF) task, focusing on the number of verbs listed within a set time.
    • Employed machine learning algorithms: Random Forest (RF), Neural Network (NN), Recurrent Neural Network (RNN), and Natural Language Processing (NLP).
    • Evaluated model performance in stratifying subjects into Alzheimer's disease (AD) and control groups.

    Main Results:

    • Random Forest (RF) models achieved up to 76% accuracy in distinguishing AD from control groups, requiring data preprocessing.
    • Recurrent Neural Network (RNN) and Natural Language Processing (NLP) models demonstrated 67% accuracy with minimal preprocessing.
    • The accuracy differences between models were not statistically significant, highlighting the potential of VF tasks.

    Conclusions:

    • Verb fluency (VF) tasks offer a promising, accessible method for the early detection of Alzheimer's disease (AD).
    • Machine learning models, particularly RF, can effectively analyze VF data for AD risk assessment.
    • The study supports the use of VF tasks as a valuable tool in the early diagnostic pathway for AD.