  The Coronavirus, known as COVID-19, which appeared in 2019 in China, has
significantly affected global health and become a huge burden on health
institutions all over the world. These effects are continuing today. One
strategy for limiting the virus's transmission is to have an early diagnosis of
suspected cases and take appropriate measures before the disease spreads
further. This work aims to diagnose and show the probability of getting
infected by the disease according to textual clinical data. In this work, we
used five machine learning techniques (GWO_MLP, GWO_CMLP, MGWO_MLP, FDO_MLP,
FDO_CMLP) all of which aim to classify Covid-19 patients into two categories
(Positive and Negative). Experiments showed promising results for all used
models. The applied methods showed very similar performance, typically in terms
of accuracy. However, in each tested dataset, FDO_MLP and FDO_CMLP produced the
best results with 100% accuracy. The other models' results varied from one
experiment to the other. It is concluded that the models on which the FDO
algorithm was used as a learning algorithm had the possibility of obtaining
higher accuracy. However, it is found that FDO has the longest runtime compared
to the other algorithms. The link to the covid 19 models is found here:
https://github.com/Tarik4Rashid4/covid19models
