Abstract
In COVID-19, the inflammatory cytokine storm is a critical factor that increases the severity of the disease. Procalcitonin (PCT) is a costly, time-consuming and important biomarker involved in the cytokine storm that exacerbates the severity of COVID-19. This study aims to develop an algorithm that can predict the PCT value in an explainable and interpretable way using explainable artificial intelligence (XAI) methods. The dataset consists of 1068 COVID-19 patients registered at Erzurum Regional Research Center in Turkey between March 2020 and March 2021 (ethical decision number: 2023/3–17). The Permutation Feature Significance (PFI) method was used to identify essential features and build the model. Among the seven-machine learning (ML) models, RandomForestClassifier performed best. RandomForestClassifier's performance metrics training accuracy: 0.89, test accuracy: 0.88, precision: 0.91, recall: 0.88, F-1 score: 0.88, Brier score: 0.11, AUC (area under the curve): 0.935, confidence intervals: 0.877, 0.883. The importance of the features in the model's predictions was analysed with the Shapley additive annotation (SHap) method integrated into the model. The results showed that LDH U/L, CRP mg/L and lymphocytes are important in predicting PCT. This study showed that PCT plays a vital role in assessing the condition of COVID-19 patients, and XAI methods can be helpful in this assessment.