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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| Format: | Article |
| Language: | English |
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College of Education, Al-Iraqia University
2023
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| Online Access: | http://umpir.ump.edu.my/id/eprint/40055/ http://umpir.ump.edu.my/id/eprint/40055/1/Classification%20of%20COVID-19%20Symptoms%20Using%20Multilayer%20Perceptron.pdf |
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| 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 |
| building | UMP Institutional Repository |
| collection | Online Access |
| 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. |
| first_indexed | 2025-11-15T03:36:53Z |
| format | Article |
| id | ump-40055 |
| institution | Universiti Malaysia Pahang |
| institution_category | Local University |
| language | English |
| last_indexed | 2025-11-15T03:36:53Z |
| publishDate | 2023 |
| publisher | College of Education, Al-Iraqia University |
| recordtype | eprints |
| repository_type | Digital Repository |
| spelling | ump-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 |
| 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 |
| title | 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_short | Classification of COVID-19 symptoms using multilayer perceptron |
| title_sort | classification of covid-19 symptoms using multilayer perceptron |
| topic | QA75 Electronic computers. Computer science RA Public aspects of medicine |
| url | http://umpir.ump.edu.my/id/eprint/40055/ http://umpir.ump.edu.my/id/eprint/40055/ http://umpir.ump.edu.my/id/eprint/40055/ http://umpir.ump.edu.my/id/eprint/40055/1/Classification%20of%20COVID-19%20Symptoms%20Using%20Multilayer%20Perceptron.pdf |