ABSTRACT
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.
ABSTRACT
The COVID-19 pandemic brought about many critical issues in all aspects such as economy, environment, health, and lifestyle, but people always try to find some response to crisis in different conditions. E-learning is defined as an elaborate response aiming at continuing education during the COVID-19 pandemic. It seems that developed countries have established and adjusted their technological infrastructures for the transition from a face-to-face education to a digital one. In contrast, developing countries were not completely prepared for this transition. Improper and deficient technological and practical fundamentals have been causing problems for all students, instructors, and other involved individuals. Therefore, we reviewed the challenges that have arisen from e-learning during the COVID-19 outbreak in different parts of tertiary education focusing on underprivileged countries.
