Fitness Dependent Optimizer with Neural Networks for COVID-19 patients

TL;DR

This study applies a novel fitness-dependent optimizer combined with neural networks to classify COVID-19 patients using clinical textual data, achieving high accuracy but with longer runtimes.

Contribution

It introduces the FDO algorithm for COVID-19 diagnosis, demonstrating superior accuracy over other machine learning models in this context.

Findings

FDO_MLP and FDO_CMLP achieved 100% accuracy.

Models with FDO had higher accuracy than other methods.

FDO has longer runtime but better performance.

Abstract

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