Assessing Machine Learning classifiers in COVID-19: The Role of Clinical, Laboratory, and Radiological Features in Predicting Oxygen Saturation

Abstract

Background Oxygen saturation is vital for evaluating COVID-19 severity in hospitalized patients, with levels below 90% indicating respiratory distress and a potential need for intensive care. Objective This study develops machine learning models that integrate CT-based features with clinical and laboratory data to predict binary oxygen saturation outcomes in COVID-19 patients. Method A retrospective study of 1008 COVID-19 patients admitted between October 2020 and May 2021, using 70% of data for training and 30% for testing. Classifiers used: Linear SVM, SVM with RBF kernels, Logistic Regression, Random Forests, Naïve Bayes, and XGBoost. Performance assessed by validation AUC and 10-fold cross-validation AUC range. Significant features identified by the top validation AUC classifier, prioritizing the top three with importance and stability scores over 0.7. Results Linear ML classifiers performed well in Clinical and Laboratory Models, while non-linear classifiers excelled in CT-Based and Integrated Models. Logistic Regression in the Clinical Model achieved an AUC of 0.82, with Age, Gender, and Fever as significant features. In the Laboratory Model, Linear SVM (0.82) identified White Blood Cell count as key. Random Forest in the CT-Based Model (0.87) highlighted Mean Lesion Volume. The Integrated Model's top classifier, SVM with RBF Kernel (0.89), found WBC and Mean NLLV critical. Conclusion Linear classifiers effectively predict oxygen saturation using clinical and laboratory data, while non-linear classifiers excel with CT-based and integrated models, highlighting the need for tailored machine learning approaches to different data types in COVID-19 patient care.