Pain development is common following a traumatic experience. Moderate-severe pain is defined as any severity response of 4 or higher on a 0-10 Numeric Pain Rating Scale for overall body pain and can negatively affect an individual’s quality of life as well as bodily functions. The aim of this thesis is to use data from the Advancing Understanding of RecOvery afteR traumA (AURORA) Study to develop machine learning classification models to accurately classify if a patient will develop month-3 moderate-severe pain following trauma exposure. After data cleaning, imputation, and preprocessing, there were a total of 271 observations and 157 miRNA features. The data were partitioned into a 70:30 train test ratio to exclusively derive the models on the training data and exclusively validate the models on the test data. Data handling, model building, and data analysis were all done in R. I created the logistic regression, least absolute shrinkage and selection operator (LASSO) logistic regression, naïve Bayes, random forest, and polynomial support vector machine (SVM) models using the stats, glmnnet, e1071, randomForest, and e1071 packages, respectively. Of the 5 models created, polynomial SVM had the best external accuracy and F1 score at 0.5909 with a 95% confidence interval (CI) of (0.4809, 0.6946) and 0.7313, respectively. On the other hand, naïve Bayes overall had the worst external accuracy and F1 score at 0.4886 with a 95% CI of (0.3805, 0.5975) and 0.5455, respectively. Logistic regression had the best external AUC score of 0.5313, followed closely by random forest (AUC = 0.5260) and polynomial SVM (AUC = 0.5209). The performance rates of my models were worse compared with others found in the literature that were used to classify pain following trauma exposure. Improvements to the model building process is needed for better classification of individuals at risk of developing moderate-severe pain following trauma exposure in the early stages when presented to the emergency department.Bachelor of Science in Public Healt
