Machine learning-based automation of COVID-19 screening using clinical dataset

Authors

Jezreel Sophia C. Lanuzo ⋅ PH Institute of Mathematical Sciences and Physics, University of the Philippines Los Baños
Joverlyn Gaudillo ⋅ PH Institute of Mathematical Sciences and Physics, University of the Philippines Los Baños
Ranzivelle Marianne Roxas-Villanueva ⋅ PH Institute of Mathematical Sciences and Physics, University of the Philippines Los Baños
Beatrice Tiangco ⋅ PH Augusto P. Sarmiento Cancer Institute, The Medical City and National Institutes of Health, University of the Philippines Manila
Jason Albia ⋅ PH Institute of Mathematical Sciences and Physics, University of the Philippines Los Baños

Abstract

Fast and effective screening of COVID-19 patients can help decrease its mortality rate. In this study, we employed machine learning methods on clinical datasets for automated COVID-19 screening. The model configuration with a decision tree as the classifier, forward selection as a feature selection method, and six features has 91% precision, 85% accuracy, 92% sensitivity, and 91% F1-score. We also determined the following relevant features identified by the employed feature selection technique that helped screen COVID-19 patients: lymphocytes, chest X-ray label, BMI, PCT, eGFR result, and comorbidity chronic lung disease. This study demonstrates the potential of machine learning models trained on clinical data in classifying COVID-19 patients to help facilitate the screening procedure.

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Published

2021-10-04

Issue

2021: Proceedings of the 39th Samahang Pisika ng Pilipinas Physics Conference

Section

Biological and Medical Physics

License

How to Cite

[1]

“Machine learning-based automation of COVID-19 screening using clinical dataset”, Proc. SPP, vol. 39, no. 1, pp. SPP–2021, Oct. 2021, Accessed: Apr. 13, 2026. [Online]. Available: https://proceedings.spp-online.org/article/view/SPP-2021-3C-05