Prediction of COVID-19 patient using supervised machine learning algorithm
One of the most symptomatic diseases is COVID-19. Early and precise physiological measurement-based prediction of breathing will minimize the risk of COVID-19 by a reasonable distance from anyone; wearing a mask, cleanliness, medication, balanced diet, and if not well stay safe at home. To evaluate...
| Main Authors: | , |
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| Format: | Article |
| Language: | English |
| Published: |
Penerbit Universiti Kebangsaan Malaysia
2021
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| Online Access: | http://journalarticle.ukm.my/17601/ http://journalarticle.ukm.my/17601/1/28.pdf |
| _version_ | 1848814354312462336 |
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| author | M., Buvana K., Muthumayil |
| author_facet | M., Buvana K., Muthumayil |
| author_sort | M., Buvana |
| building | UKM Institutional Repository |
| collection | Online Access |
| description | One of the most symptomatic diseases is COVID-19. Early and precise physiological measurement-based prediction of breathing will minimize the risk of COVID-19 by a reasonable distance from anyone; wearing a mask, cleanliness, medication, balanced diet, and if not well stay safe at home. To evaluate the collected datasets of COVID-19 prediction, five machine learning classifiers were used: Nave Bayes, Support Vector Machine (SVM), Logistic Regression, K-Nearest Neighbour (KNN), and Decision Tree. COVID-19 datasets from the Repository were combined and re-examined to remove incomplete entries, and a total of 2500 cases were utilized in this study. Features of fever, body pain, runny nose, difficulty in breathing, shore throat, and nasal congestion, are considered to be the most important differences between patients who have COVID-19s and those who do not. We exhibit the prediction functionality of five machine learning classifiers. A publicly available data set was used to train and assess the model. With an overall accuracy of 99.88 percent, the ensemble model is performed commendably. When compared to the existing methods and studies, the proposed model is performed better. As a result, the model presented is trustworthy and can be used to screen COVID-19 patients timely, efficiently. |
| first_indexed | 2025-11-15T00:32:45Z |
| format | Article |
| id | oai:generic.eprints.org:17601 |
| institution | Universiti Kebangasaan Malaysia |
| institution_category | Local University |
| language | English |
| last_indexed | 2025-11-15T00:32:45Z |
| publishDate | 2021 |
| publisher | Penerbit Universiti Kebangsaan Malaysia |
| recordtype | eprints |
| repository_type | Digital Repository |
| spelling | oai:generic.eprints.org:176012021-11-19T02:54:37Z http://journalarticle.ukm.my/17601/ Prediction of COVID-19 patient using supervised machine learning algorithm M., Buvana K., Muthumayil One of the most symptomatic diseases is COVID-19. Early and precise physiological measurement-based prediction of breathing will minimize the risk of COVID-19 by a reasonable distance from anyone; wearing a mask, cleanliness, medication, balanced diet, and if not well stay safe at home. To evaluate the collected datasets of COVID-19 prediction, five machine learning classifiers were used: Nave Bayes, Support Vector Machine (SVM), Logistic Regression, K-Nearest Neighbour (KNN), and Decision Tree. COVID-19 datasets from the Repository were combined and re-examined to remove incomplete entries, and a total of 2500 cases were utilized in this study. Features of fever, body pain, runny nose, difficulty in breathing, shore throat, and nasal congestion, are considered to be the most important differences between patients who have COVID-19s and those who do not. We exhibit the prediction functionality of five machine learning classifiers. A publicly available data set was used to train and assess the model. With an overall accuracy of 99.88 percent, the ensemble model is performed commendably. When compared to the existing methods and studies, the proposed model is performed better. As a result, the model presented is trustworthy and can be used to screen COVID-19 patients timely, efficiently. Penerbit Universiti Kebangsaan Malaysia 2021-08 Article PeerReviewed application/pdf en http://journalarticle.ukm.my/17601/1/28.pdf M., Buvana and K., Muthumayil (2021) Prediction of COVID-19 patient using supervised machine learning algorithm. Sains Malaysiana, 50 (8). pp. 2479-2497. ISSN 0126-6039 https://www.ukm.my/jsm/malay_journals/jilid50bil8_2021/KandunganJilid50Bil8_2021.html |
| spellingShingle | M., Buvana K., Muthumayil Prediction of COVID-19 patient using supervised machine learning algorithm |
| title | Prediction of COVID-19 patient using supervised machine learning algorithm |
| title_full | Prediction of COVID-19 patient using supervised machine learning algorithm |
| title_fullStr | Prediction of COVID-19 patient using supervised machine learning algorithm |
| title_full_unstemmed | Prediction of COVID-19 patient using supervised machine learning algorithm |
| title_short | Prediction of COVID-19 patient using supervised machine learning algorithm |
| title_sort | prediction of covid-19 patient using supervised machine learning algorithm |
| url | http://journalarticle.ukm.my/17601/ http://journalarticle.ukm.my/17601/ http://journalarticle.ukm.my/17601/1/28.pdf |