Machine Learning Methods for COVID-19 Prediction Using Human Genomic Data
Accurate identification of COVID-19 is now a critical task since it has seriously damaged daily life, public health, and the economy. It is essential to identify the infected people to prevent the further spread of the pandemic and to treat infected patients quickly. Machine learning techniques have...
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| Format: | Article |
| Language: | English |
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MDPI AG
2021-03-01
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| Series: | Proceedings |
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| Online Access: | https://www.mdpi.com/2504-3900/74/1/20 |
| _version_ | 1827091013411274752 |
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| author | Hilal Arslan |
| author_facet | Hilal Arslan |
| author_sort | Hilal Arslan |
| collection | DOAJ |
| description | Accurate identification of COVID-19 is now a critical task since it has seriously damaged daily life, public health, and the economy. It is essential to identify the infected people to prevent the further spread of the pandemic and to treat infected patients quickly. Machine learning techniques have a significant role in predicting of COVID-19. In this study, we performed binary classification (COVID-19 vs. other types of coronavirus) by extracting features from genome sequences. Support vector machines, naive Bayes, K-nearest neighbor, and random forest methods were used for classification. We used viral gene sequences from the 2019 Novel Coronavirus Resource Database. Experimental results presented show that a decision tree method achieved 93% accuracy. |
| first_indexed | 2024-03-10T13:13:05Z |
| format | Article |
| id | doaj.art-eea97963327a4f6389ad030845ef2eba |
| institution | Directory Open Access Journal |
| issn | 2504-3900 |
| language | English |
| last_indexed | 2025-03-20T05:49:33Z |
| publishDate | 2021-03-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Proceedings |
| spelling | doaj.art-eea97963327a4f6389ad030845ef2eba2024-10-03T03:23:32ZengMDPI AGProceedings2504-39002021-03-017412010.3390/proceedings2021074020Machine Learning Methods for COVID-19 Prediction Using Human Genomic DataHilal Arslan0Department of Computer Engineering, Faculty of Engineering and Architecture, Izmir Bakırçay University, Izmir 35665, TurkeyAccurate identification of COVID-19 is now a critical task since it has seriously damaged daily life, public health, and the economy. It is essential to identify the infected people to prevent the further spread of the pandemic and to treat infected patients quickly. Machine learning techniques have a significant role in predicting of COVID-19. In this study, we performed binary classification (COVID-19 vs. other types of coronavirus) by extracting features from genome sequences. Support vector machines, naive Bayes, K-nearest neighbor, and random forest methods were used for classification. We used viral gene sequences from the 2019 Novel Coronavirus Resource Database. Experimental results presented show that a decision tree method achieved 93% accuracy.https://www.mdpi.com/2504-3900/74/1/20coronavirusCOVID-19machine learningCpG islands |
| spellingShingle | Hilal Arslan Machine Learning Methods for COVID-19 Prediction Using Human Genomic Data Proceedings coronavirus COVID-19 machine learning CpG islands |
| title | Machine Learning Methods for COVID-19 Prediction Using Human Genomic Data |
| title_full | Machine Learning Methods for COVID-19 Prediction Using Human Genomic Data |
| title_fullStr | Machine Learning Methods for COVID-19 Prediction Using Human Genomic Data |
| title_full_unstemmed | Machine Learning Methods for COVID-19 Prediction Using Human Genomic Data |
| title_short | Machine Learning Methods for COVID-19 Prediction Using Human Genomic Data |
| title_sort | machine learning methods for covid 19 prediction using human genomic data |
| topic | coronavirus COVID-19 machine learning CpG islands |
| url | https://www.mdpi.com/2504-3900/74/1/20 |
| work_keys_str_mv | AT hilalarslan machinelearningmethodsforcovid19predictionusinghumangenomicdata |