Performance and explainability of feature selection-boosted tree-based classifiers for COVID-19 detection.

ABSTRACT: In this paper, we evaluate the performance and analyze the explainability of machine learning models boosted by feature selection in predicting COVID-19-positive cases from self-reported information. In essence, this work describes a methodology to identify COVID-19 infections that considers the large amount of information collected by the University of Maryland Global COVID-19 Trends and Impact Survey (UMD-CTIS). More precisely, this methodology performs a feature selection stage based on the recursive feature elimination (RFE) method to reduce the number of input variables without compromising detection accuracy. A tree-based supervised machine learning model is then optimized with the selected features to detect COVID-19-active cases. In contrast to previous approaches that use a limite

SUBMITTER: Rufino J 

PROVIDER: S-EPMC10758803 | biostudies-literature | 2024 Jan

REPOSITORIES: biostudies-literature