Predicting COVID-19 outcomes: Machine learning predictions across diverse datasets

Abstract

Background The COVID-19 infection has spread rapidly since its emergence and has affected a large part of the global population. With the increasing number of cases, researchers are trying to predict the prognosis of patients by using different data with artificial intelligence methods such as machine learning (ML). In this study, we aimed to predict mortality risk in COVID-19 patients using ML algorithms with different datasets. Methodology In this retrospective study, we evaluated the fever, oxygen saturation, laboratory results, thorax computed tomography (CT) findings, and comorbid diseases at admission to the hospital of 404 patients whose diagnosis was confirmed by the reverse transcription polymerase chain reaction test. Different datasets were created by combining the data. The Synthetic Minority Oversampling Technique was used to reduce the imbalance in the dataset. K-nearest neighbors, support vector machine, stochastic gradient descent, random forest, neural network, naive Bayes, logistic regression, gradient boosting, XGBoost, and AdaBoost models were used to create the ML algorithm, and the accuracy rates of mortality prediction were compared. Results When the dataset was created with CT parenchyma score, pulmonary artery and inferior vena cava diameters, and laboratory results, mortality was predicted with an accuracy of 98.4% with the gradient boosting model. Conclusions The study demonstrates that patient prognosis can be accurately predicted using simple measurements from thorax CT scans and laboratory findings.