Date of Award
Summer 2022
Rights
Access is available to all users
Document Type
Thesis
Degree Name
Master of Science (MS) in Computer Science
Department
Computer Science
Abstract
The objective of this study is to determine if diagnosis documents can be used with document classification to automatically diagnose mental health conditions. Document classification allows text documents to be analyzed and organized into their appropriate classes based on the features and words presented in the text. One application of this is within the medical field to automatically classify different patient diagnosis based on medical or patient notes. This research applied mental health diagnosis documents to automatically diagnose a group of patients with a mental health condition based on text-based survey data. This classification was approached through several feature engineering and machine learning models to determine the optimal methods for diagnosis classification. A model was created that successfully classified diagnosis documents to their appropriate mental health condition, but due to limitation in the patient dataset, no model successfully classified patient diagnoses.
Creative Commons License
This work is licensed under a Creative Commons Attribution-NonCommercial-No Derivative Works 4.0 International License.
Recommended Citation
Tadlock, William M., "Modeling document classification to automate mental health diagnosis" (2022). EWU Masters Thesis Collection. 773.
https://dc.ewu.edu/theses/773
