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Classification of COVID-19 symptoms using multilayer perceptron

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...

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Main Authors: Nurulain Nusrah, Mohd Azam, Mohd Arfian, Ismail, Mohd Saberi, Mohamad, Ashraf Osman, Ibrahim, Jeba, Shermina
Format: Article
Language:English
Published: College of Education, Al-Iraqia University 2023
Subjects:
QA75 Electronic computers. Computer science
RA Public aspects of medicine
Online Access:http://umpir.ump.edu.my/id/eprint/40055/1/Classification%20of%20COVID-19%20Symptoms%20Using%20Multilayer%20Perceptron.pdf
http://umpir.ump.edu.my/id/eprint/40055/
https://doi.org/10.52866/ijcsm.2023.04.04.009
https://doi.org/10.52866/ijcsm.2023.04.04.009
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spelling my.ump.umpir.400552024-01-17T04:20:43Z http://umpir.ump.edu.my/id/eprint/40055/ Classification of COVID-19 symptoms using multilayer perceptron Nurulain Nusrah, Mohd Azam Mohd Arfian, Ismail Mohd Saberi, Mohamad Ashraf Osman, Ibrahim Jeba, Shermina QA75 Electronic computers. Computer science RA Public aspects of medicine 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. College of Education, Al-Iraqia University 2023-10 Article PeerReviewed pdf en cc_by_4 http://umpir.ump.edu.my/id/eprint/40055/1/Classification%20of%20COVID-19%20Symptoms%20Using%20Multilayer%20Perceptron.pdf Nurulain Nusrah, Mohd Azam and Mohd Arfian, Ismail and Mohd Saberi, Mohamad and Ashraf Osman, Ibrahim and Jeba, Shermina (2023) Classification of COVID-19 symptoms using multilayer perceptron. Iraqi Journal for Computer Science and Mathematics (IJCSM), 4 (4). pp. 100-110. ISSN 2788-7421. (Published) https://doi.org/10.52866/ijcsm.2023.04.04.009 https://doi.org/10.52866/ijcsm.2023.04.04.009
institution Universiti Malaysia Pahang Al-Sultan Abdullah
building UMPSA Library
collection Institutional Repository
continent Asia
country Malaysia
content_provider Universiti Malaysia Pahang Al-Sultan Abdullah
content_source UMPSA Institutional Repository
url_provider http://umpir.ump.edu.my/
language English
topic QA75 Electronic computers. Computer science
RA Public aspects of medicine
spellingShingle QA75 Electronic computers. Computer science
RA Public aspects of medicine
Nurulain Nusrah, Mohd Azam
Mohd Arfian, Ismail
Mohd Saberi, Mohamad
Ashraf Osman, Ibrahim
Jeba, Shermina
Classification of COVID-19 symptoms using multilayer perceptron
description 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.
format Article
author Nurulain Nusrah, Mohd Azam
Mohd Arfian, Ismail
Mohd Saberi, Mohamad
Ashraf Osman, Ibrahim
Jeba, Shermina
author_facet Nurulain Nusrah, Mohd Azam
Mohd Arfian, Ismail
Mohd Saberi, Mohamad
Ashraf Osman, Ibrahim
Jeba, Shermina
author_sort Nurulain Nusrah, Mohd Azam
title Classification of COVID-19 symptoms using multilayer perceptron
title_short Classification of COVID-19 symptoms using multilayer perceptron
title_full Classification of COVID-19 symptoms using multilayer perceptron
title_fullStr Classification of COVID-19 symptoms using multilayer perceptron
title_full_unstemmed Classification of COVID-19 symptoms using multilayer perceptron
title_sort classification of covid-19 symptoms using multilayer perceptron
publisher College of Education, Al-Iraqia University
publishDate 2023
url http://umpir.ump.edu.my/id/eprint/40055/1/Classification%20of%20COVID-19%20Symptoms%20Using%20Multilayer%20Perceptron.pdf
http://umpir.ump.edu.my/id/eprint/40055/
https://doi.org/10.52866/ijcsm.2023.04.04.009
https://doi.org/10.52866/ijcsm.2023.04.04.009
_version_ 1822924088126996480
score 13.235362

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