Boosting Multiclass-classifier Performance with Borderline-SMOTE Augmentation for Predicting Disease Severity and Mortality of In-Patients with COVID-19 in South Florida

Authors

Subhosit Ray, Florida Atlantic University
Debarshi Datta, Florida Atlantic UniversityFollow
Laurie Martinez, Florida Atlantic University
David Newman, Florida Atlantic University
Safiya George Dalmida, Florida Atlantic University
Javad Hashemi, Florida Atlantic University
Candice Sareli, Florida Atlantic University
Paula Eckardt, Florida Atlantic University

Files

Publication Date

4-17-2024

Content Type

Poster

Recommended Citation

Ray, Subhosit; Datta, Debarshi; Martinez, Laurie; Newman, David; George Dalmida, Safiya; Hashemi, Javad; Sareli, Candice; and Eckardt, Paula, "Boosting Multiclass-classifier Performance with Borderline-SMOTE Augmentation for Predicting Disease Severity and Mortality of In-Patients with COVID-19 in South Florida" (2024). Baptist Health Academic Conference: AI in Healthcare Education and Research. 116.
https://scholarlycommons.baptisthealth.net/bhsf-academic-week-2024/116