Supervised Machine Learning Models for Covid-19 Diagnosis using a Combination of Clinical and Laboratory Data

An epidemic caused by a new type of Coronavirus family, called COVID-19, has created a global crisis involving all countries of the world. In this regard, designing an early detection system using heuristic and noninvasive methods can be a good and decisive factor in detecting the disease early and consequently decreasing the prevalence of the virus. In recent years, to rapidly diagnose diseases, machine learning techniques have increasingly grown to predict and diagnose patients, and researchers have used them in various studies. In this regard, since the outbreak of COVID-19, several researchers have tried to use the machine learning approach as a potential tool for identifying and diagnosing this disease. Due to the importance and role of using clinical and laboratory data in the diagnosis of afflicted people with COVID-19, in this paper, the models of K-NN, SVM, Decision Tree, Random Forest, Naive Bayes, Neural Network, and XGBoost as the most common machine learning models were used on a database with 1354 records consisting of clinical and laboratory data of COVID and non-COVID patients to diagnose COVID-19. Evaluation results based on Accuracy, Precision, Recall, and F-Score criteria showed that a XGBoost and K-NN with accuracy of 97% and 96% could be considered a suitable predictive model to diagnose the COVID-19 disease.