Prediction of Post-Covid chronic fatigue syndrome using Data Mining Techniques in Isfahan COVID cohort study

Background: Post-COVID Fatigue (PCF) is one of the most common issues people face after recovering from COVID-19. This study aimed to use data mining Techniques to prediction of PCF. Methods: We analyzed data from 3,850 COVID-19 patients taken from the Isfahan COVID Cohort Study ( ICC) database. Key factors linked to PCF were identified and used to build predictive models. After preparing the data, we applied several models, including logistic regression (LR), support vector machines (SVM), decision trees (DT), and random forest (RF)s, to predict fatigue. We then compared how well each model performed using different evaluation criteria. The analysis was done using R-Studio version 4.1.2. Results: We found 37 factors that were significantly related to fatigue. Among the different models, the RF model performed the best, with an accuracy rate of 85%. According to this model, the top predictors of PCF were, in order: anxiety levels, body mass index (BMI), depression levels, post-COVID increased irritability, memory issues, a history of fatty liver disease, tingling in the hands and feet, and post-COVID excessive sweating. Conclusions: Using data mining Techniques can help healthcare professionals identify key factors contributing to PCF, allowing them to develop better strategies for prevention and treatment.