Tehran University of Medical Sciences Iranian Journal of Public Health 2251-6085 52 10 2023 09 30 2179 2185 Shahriar Mohammadi Information Technology Group, Department of Industrial Engineering, K.N. Toosi University of Technology, Tehran, Iran Soraya Zarei Information Technology Group, Department of Industrial Engineering, K.N. Toosi University of Technology, Tehran, Iran Hossain Jabbari 1. Neurology Department, Penzing Teaching Hospital, Vienna, Austria 2. Digestive Diseases Research Institute, Tehran University of Medical Sciences, Tehran, Iran 2022 11 30 2023 01 22 Background: One of the negative effects of the COVID-19 illness, which has affected people all across the world, is Alzheimer's disease. Oblivion after COVID-19 has created a variety of issues for many people. Predicting this issue in COVID-19 patients can considerably lessen the severity of the problem. Methods: Alzheimer's disease was predicted in Iranian persons with COVID-19 in using three algorithms: Nave Bayes, Random Forest, and KNN. Data collected by private questioner from hospitals of Tehran Province, Iran, during Oct 2020 to Sep 2021. For ML models, performance is quantified using measures such as Precision, Recall, Accuracy, and F1-score. Results: The Nave Bayes, Random Forest algorithm has a prediction accuracy of higher than 80%. The predicted accuracy of the random forest algorithm was higher than the other two algorithms. Conclusion: The Random Forest algorithm outperformed the other two algorithms in predicting Alzheimer's disease in persons using COVID-19. The findings of this study could help persons with COVID-19 avoid Alzheimer's problems. https://ijph.tums.ac.ir/index.php/ijph/article/view/30519 https://ijph.tums.ac.ir/index.php/ijph/article/download/30519/8066