Al-Obadi, Mohamed GhanimFarhan, Hameed MutlagNaseri, Raghda Awad ShabanTürkben, Ayça KurnazMustafa, Ahmed KhalidAl-Aloosi, Ahmed Raad2023-06-102023-06-102022Al-Obadi, M. G., Farhan, H. M., Naseri, R. A. S., Turkben, A. K., Mustafa, A. K., & Al-Aloosi, A. R. (2022). Data mining techniques for extraction and analysis of covid-19 data. In 2022 International Conference on Artificial Intelligence of Things (ICAIoT). IEEE.9798350396768https://hdl.handle.net/20.500.12939/3539Artificial intelligence has played a crucial role in medical disease diagnosis. In this research, data mining techniques that included deep learning with different scenarios are presented for extraction and analysis of covid-19 data. The energy of the features is implemented and calculated from the CT scan images. A modified meta-heuristic algorithm is introduced and then used in the suggested way to determine the best and most useful features, which are based on how ants behave. Different patients with different problems are investigated and analyzed. Also, the results are compared with other studies. The results of the proposed method show that the proposed method has higher accuracy than other methods. It is concluded from the results that the most crucial features can be concentrated on during feature selection, which lowers the error rate when separating sick from healthy individuals.eninfo:eu-repo/semantics/closedAccessCOVID-19Data MiningFeature AnalysisFeature ExtractionArticle2-s2.0-85160557195N/A