Abstract
The COVID-19 virus had easily affected people worldwide through direct contact. Individualsdiagnosed with positive COVID-19 virus may be affected with many symptoms, such as fever, tiredness, dry cough,difficulty in breathing, sore throat, chest pain, nasal congestion, runny nose, and diarrhea. An individual can alsobe diagnosed with COVID-19 even when he does not have any symptoms or be in contact with an infected person.Data classification was required due to the size of COVID-19 data that will be analyzed for future countermeasuresdetermination. Some problems in data classification occurred due to unorganized data, such as time consumption,human error in complexity of symptom features and the diagnosis process data needed expert knowledge. This studyaimed to use the artificial neural network (ANN) approach, which was multilayer perceptron (MLP) to classify theCOVID-19 data by using patient symptom data. The MLP process involved data collection, data normalization, MLPdesign, MLP training, testing, and MLP verification. From the experiments, the MLP method was able to obtainan accuracy rate of 77.10%. In conclusion, the MLP method could classify the COVID-19 data and achieve a highaccuracy rate.
Recommended Citation
Ismail, Mohd Arfian; Mohd Azam, Nurulain Nusrah; Mohamad, Mohd Saberi; Ibrahim, Ashraf Osman; and Jeba, Shermina
(2023)
"Classification of COVID-19 Symptoms Using MultilayerPerceptron,"
Iraqi Journal for Computer Science and Mathematics: Vol. 4:
Iss.
4, Article 9.
DOI: https://doi.org/10.52866/ijcsm.2023.04.04.009
Available at:
https://ijcsm.researchcommons.org/ijcsm/vol4/iss4/9
