Predicting the Severity of Anxiety Attacks with Machine Learning
Publication Date
2025
Presentation Length
Poster/Gallery presentation
College
College of Sciences & Mathematics
Department
Math and Computer Science, Department of
Student Level
Undergraduate
SPARK Category
Research
Faculty Advisor
Dr. Christina Davis
WELL Core Type
Intellectual Wellness
SPARK Session
Poster: Data Science
Presentation Type
Poster
Summary
This project applies machine learning models to predict the severity of anxiety attacks based on psychological, health, and lifestyle factors. Gradient Boosting, Random Forest, and Lasso Regression were used for model training. The goal is to provide insights that can help healthcare professionals and patients in managing anxiety attacks more effectively.
Recommended Citation
Fall, Zeynab, "Predicting the Severity of Anxiety Attacks with Machine Learning" (2025). SPARK Symposium Presentations. 41.
https://repository.belmont.edu/spark_presentations/41
