فهرست نشریات علمی دسترسی آزاد ایران

Background: Oxygen saturation is a vital parameter for evaluating the severity of COVID-19 in hospitalized patients, with levels below 90% indicating respiratory distress and a potential need for intensive care. Objectives: This study develops machine learning (ML) models that integrate computed tomography (CT)-based features with clinical and laboratory data to predict binary oxygen saturation outcomes in COVID-19 patients. 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 (AUC = 0.82) identified white blood cell (WBC) count as key. Random forest in the CT-Based Model (AUC = 0.87) highlighted mean lesion volume. The Integrated Model's top classifier, SVM with RBF kernel (AUC = 0.89), found WBC and mean non-lesion lung volume (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 ML approaches to different data types in COVID-19 patient care.