Abstracts Mathematics

Machine Learning Model Comparison And Arma Simulation Of Exhaled Breath Signals Classifying COVID-19 Patients

by Aaron Christopher Segura

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.