Date Presented
Spring 5-15-2022
Document Type
Thesis
Degree Name
Bachelor of Science
Department
Computer Science
First Advisor
Dr. Zakaria Kurdi
Second Advisor
Dr. Randy Ribler
Third Advisor
Dr. Laura Kicklighter
Abstract
The goal of this work is to build a classifier that can identify whether a patient is suffering from Alzheimer’s Disease of the Dementia Type (AD). A corpus of 2751 texts was used from the DementiaBank database, where each conversation is transcribed and marked using the CHAT format. Each text was analyzed by frequency of disfluencies, use of aphasic language, and lexical features. All parsed data was used to train a Random Forest, Naïve Bayes, and Support Vector Machine algorithm. These classification algorithms will be tested on the combination of all features, as well as each set of features individually.
Recommended Citation
Hurowitz, Joseph, "Dementia Classification through Textual Analysis with Machine Learning Algorithms" (2022). Undergraduate Theses and Capstone Projects. 240.
https://digitalshowcase.lynchburg.edu/utcp/240