Date of Award
8-2024
Degree Type
Creative Project
Degree Name
Master of Science (MS)
Department
Economics and Finance
Committee Chair(s)
Carly Fox
Committee
Carly Fox
Committee
Todd Griffith
Committee
Pedram Jahangiry
Abstract
This thesis rigorously evaluates the application of an array of natural language processing (NLP) techniques and machine learning models to identify linguistic signatures indicative of dementia, as sourced from the DementiaBank Pitt corpus. Utilizing a binary classification paradigm, this study meticulously integrates sophisticated embedding methods—including Doc2Vec, Word2Vec, GloVe, and BERT—with traditional machine learning algorithms such as Random Forest, Multinomial Naïve Bayes, ADA boost, KNN classifier, and Logistic Regression, alongside deep learning architectures like LSTM, Bi-LSTM, and CNN-LSTM. The efficacy of these methodologies is evaluated based on their capacity to differentiate between transcribed speech impacted by dementia and that from control subjects. To enhance interpretability, this research also employs feature importance analysis through LIME, SHAP, permutation importance, and integrated gradients, shedding light on the variables most instrumental in driving model predictions. The results of this comprehensive analysis not only illuminate the robust potential of these combined NLP and machine learning approaches in the context of medical screening but also contribute additional valuable insights to the field of NLP and dementia screening specifically.
Recommended Citation
Bushnell, Cailean, "Unveiling Key Features: A Comparative Study of Machine Learning Models for Alzheimer's Detection" (2024). All Graduate Reports and Creative Projects, Fall 2023 to Present. 42.
https://digitalcommons.usu.edu/gradreports2023/42
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