Date of Award
2019
Degree Type
Thesis
Degree Name
Honors Thesis
Department
Mathematics
First Advisor
Rasitha Jayasekare
Abstract
Multi-class classification models are used to predict categorical response variables with more than two possible outcomes. A collection of multi-class classification techniques such as Multinomial Logistic Regression, Na\"{i}ve Bayes, and Support Vector Machine is used in predicting patients’ drug reactions and adverse drug effects based on patients’ demographic and drug administration. The newly released 2018 data on drug reactions and adverse drug effects from U.S. Food and Drug Administration are tested with the models. The applicability of model evaluation measures such as sensitivity, specificity and prediction accuracy in multi-class settings, are also discussed.
Recommended Citation
Puhl, Victoria, "Utilizing Multi-level Classification Techniques to Predict Adverse Drug Effects and Reactions" (2019). Undergraduate Honors Thesis Collection. 506.
https://digitalcommons.butler.edu/ugtheses/506