Fitness Dependent Optimizer with Neural Networks for COVID-19 patients
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Abstract (arXiv)
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
Key facts. Cited by 1 paper in the map (data through 14 Oct 2026). Builds on 11 of its 58 references that are in the map. First posted on arXiv 6 Jan 2023.
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11 of 58 references.
References · 11
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- 2019A Multi Hidden Recurrent Neural Network with a Modified Grey Wolf Optimizercited by 2
- 2020Improved Fitness-Dependent Optimizer Algorithmcited by 1
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