Classification of COVID-19 Symptoms Using Multilayer Perceptron

Nurulain Nusrah Mohd Azam, Mohd Arfian Ismail, Mohd Saberi Mohamad, Ashraf Osman Ibrahim, Shermina Jeba

Research output: Contribution to journalArticlepeer-review

1 Citation (Scopus)


The COVID-19 virus had easily affected people worldwide through direct contact. Individuals diagnosed 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 also be 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 countermeasures determination. 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 study aimed to use the artificial neural network (ANN) approach, which was multilayer perceptron (MLP) to classify the COVID-19 data by using patient symptom data. The MLP process involved data collection, data normalization, MLP design, MLP training, testing, and MLP verification. From the experiments, the MLP method was able to obtain an accuracy rate of 77.10%. In conclusion, the MLP method could classify the COVID-19 data and achieve a high accuracy rate.

Original languageEnglish
Pages (from-to)99-110
Number of pages12
JournalIraqi Journal for Computer Science and Mathematics
Issue number4
Publication statusPublished - 2023


  • Artificial Neural Network
  • Covid19
  • Machine Learning
  • Multilayer Perceptron

ASJC Scopus subject areas

  • Computer Networks and Communications
  • Computer Graphics and Computer-Aided Design
  • Computational Theory and Mathematics
  • Artificial Intelligence


Dive into the research topics of 'Classification of COVID-19 Symptoms Using Multilayer Perceptron'. Together they form a unique fingerprint.

Cite this