Publication Date
Summer 8-1-2022
Abstract
This study compared the performance of machine learning models in classifying COVID-19 patients using exhaled breath signals and simulated datasets. Ground truth classification was determined by the gold standard Polymerase Chain Reaction (PCR) test results. A residual bootstrapped method generated the simulated datasets by fitting signal data to Autoregressive Moving Average (ARMA) models. Classification models included neural networks, k-nearest neighbors, naïve Bayes, random forest, and support vector machines. A Recursive Feature Elimination (RFE) study was performed to determine if reducing signal features would improve the classification models performance using Gini Importance scoring for the two classes. The top 25% of features determined by Gini Importance scores suggest that profiles from specific Volatile Organic Compounds (VOC) in patient breath may contribute to model performance.
Degree Name
Statistics
Level of Degree
Masters
Department Name
Mathematics & Statistics
First Committee Member (Chair)
James Degnan
Second Committee Member
Mohammad Motamed
Third Committee Member
Justin Baca
Keywords
COVID-19, machine learning, breath signals, simulation, autoregressive moving average
Document Type
Thesis
Recommended Citation
Segura, Aaron Christopher. "Machine Learning Model Comparison And Arma Simulation Of Exhaled Breath Signals Classifying COVID-19 Patients." (2022). https://digitalrepository.unm.edu/math_etds/170
