Prediction of COVID-19 Effect on Patients during Six Month After Recovery, by Using AI Algorithm | ||
| AL-Rafidain Journal of Computer Sciences and Mathematics | ||
| Volume 17, Issue 1, June 2023, Pages 53-61 PDF (1.63 M) | ||
| DOI: 10.33899/csmj.2023.179470 | ||
| Authors | ||
| hanan aldabagh* 1; Ghaydaa A.A. Al-Talib2 | ||
| 1Nineveh Education Directorate | ||
| 2College of Computer Sciences and mathematics University of Mosul, Mosul, Iraq | ||
| Abstract | ||
| People all over the world have experienced severe panic as a result of the novel coronavirus (COVID-19). To secure their future, it is crucial to undertake thorough evaluations in their psychological, physical, and social domains, comprehend the potential outcomes of patients recovering from it, and ascertain whether they have any other harmful diseases. This possible outcome for people recovered from Covid 19 was predicted by collecting data from people who had previously been infected with this virus to determine the effects they had, using intelligent techniques. The GSO algorithm was used for feature selection, and for hyper-parameter tuning for the Random Forest (RF) algorithm used in the prediction process in order to make predictions to identify the effects that may accrue on recoveries persons. This model was evaluated using different metrics after performing multiple processing operations on the data and using the GSO algorithm to perform the feature selection process in order to obtain the important features. Good results were obtained for each expected effect, as the highest AUC was obtained when predicting the impact of the gastrointestinal tract of recovered persons, which is 0.91. This will then reveal the effects that Covid-19 has had on people after they have recovered. This will assist in anticipating possible results to provide counseling and psychological support, as well as some recommended guidelines for healing patients and the community to return to a normal life. | ||
| Keywords | ||
| Prediction; COVID-19; physical activity; Artificial Intelligence; Machine Learning; Glowworm Swarm Optimization | ||
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