RESUMO
BACKGROUND & AIMS: Considering that no standard therapy has yet been found for the novel coronavirus disease (COVID-19), identifying severe cases as early as possible, and such that treatment procedures can be escalated seems necessary. Hence, the present study aimed to develop a machine learning (ML) approach for automated severity assessment of COVID-19 based on clinical and paraclinical characteristics like serum levels of zinc, calcium, and vitamin D. METHODS: In this analytical cross-sectional study which was conducted from May 2020 to May 2021, clinical and paraclinical data sets of COVID-19-positive patients with known outcomes were investigated by combining statistical comparison and correlation methods with ML algorithms, including Decision Tree (DT), Random Forest (RF), and Support Vector Machine (SVM). RESULTS: Our work revealed that some patients' characteristics including age, gender, cardiovascular diseases as an underlying condition, and anorexia as disease symptoms, and also some parameters which are measurable in blood samples including FBS and serum levels of calcium are factors that can be considered in predicting COVID-19 severity. In this regard, we developed ML predictive models that indicated accuracy and precision scores >90% for disease severity prediction. The SVM algorithm indicated better results than other algorithms by having a precision of 95.5%, recall of 94%, F1 score of 94.8%, the accuracy of 95%, and AUC of 94%. CONCLUSIONS: Our results indicated that clinical and paraclinical features like calcium serum levels can be used for automated severity assessment of COVID-19.
