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CLASSIFICATION OF COVID-19 DATASET WITH SOME MACHINE LEARNING METHODS

Year 2020, Volume: 1 Issue: 1, 30 - 37, 02.07.2020

Yavuz Ünal Muhammed Nuri Dudak

Abstract

2019 yılında ortaya çıkan ve tüm dünyayı etkileyen covid-19 hastalığı milyonlarca insanın enfekte olmasına ve yüzbinlerce insanın ölümüne sebep olmuştur. Bu hastalıkla ilgili yapılacak her türlü bilimsel çalışma bu hastalıktan en kısa sürede kurtulmaya yardımcı olacaktır. Bu çalışmada kaggle sitesi üzerinden temin edilen covid 19 datasetine makine öğrenmesi sınıflandırma algoritmalarından naive bayes, k-nearest neighbor, support vector machine, decision tree uygulanmıştır. En iyi sınıflandırma doğruluğu %100 ile destek vektör makinalarından algoritmasından elde edilmiştir.

Keywords

machine learning covid-19 classification naive bayes support vector machine

References

  • [1] C. Huang et al., “Clinical features of patients infected with 2019 novel coronavirus in wuhan, china,” The Lancet, vol. 395, no. 10223, pp. 497–506, 2020 [2] Z. Wu and J. M. McGoogan, “Characteristics of and important lessons from the coronavirus disease 2019 (covid-19) outbreak in china: summary of a report of 72 314 cases from the chinese center for disease control and prevention,” Jama, 2020. [3] Quinlan, J. R. C4.5: Programs for Machine Learning. Morgan Kaufmann Publishers, 1993. [4] L. Jiang, D. Wang, Z. Cai and X. Yan : Survey of Improving Naive Bayes for Classification. In: Lecture Notes in Computer Science, Vol. 4632, pp. 134-145, Springer-Verlag , Berlin Heidelberg , 2007. [5] Hsu C. W., Lin C. J., A comparison of methods for multiclass support vector machines, IEEE Transactions on Neural Networks, 2002, 13 (2): 415-425. [6] R. Agrawal. “K-Nearest Neighborn for Uncertain Data”. International Journal of Computer Applications (0975-8887). 2014. Vol. 105 No. 11 pp 13-16.

CLASSIFICATION OF COVID-19 DATASET WITH SOME MACHINE LEARNING METHODS

Year 2020, Volume: 1 Issue: 1, 30 - 37, 02.07.2020

Yavuz Ünal Muhammed Nuri Dudak

Abstract

2019 yılında ortaya çıkan ve tüm dünyayı etkileyen covid-19 hastalığı milyonlarca insanın enfekte olmasına ve yüzbinlerce insanın ölümüne sebep olmuştur. Bu hastalıkla ilgili yapılacak her türlü bilimsel çalışma bu hastalıktan en kısa sürede kurtulmaya yardımcı olacaktır. Bu çalışmada kaggle sitesi üzerinden temin edilen covid 19 datasetine makine öğrenmesi sınıflandırma algoritmalarından naive bayes, k-nearest neighbor, support vector machine, decision tree uygulanmıştır. En iyi sınıflandırma doğruluğu %100 ile destek vektör makinalarından algoritmasından elde edilmiştir.

Keywords

Classification Machine Learning covid-19 svm naive Bayes

References

  • [1] C. Huang et al., “Clinical features of patients infected with 2019 novel coronavirus in wuhan, china,” The Lancet, vol. 395, no. 10223, pp. 497–506, 2020 [2] Z. Wu and J. M. McGoogan, “Characteristics of and important lessons from the coronavirus disease 2019 (covid-19) outbreak in china: summary of a report of 72 314 cases from the chinese center for disease control and prevention,” Jama, 2020. [3] Quinlan, J. R. C4.5: Programs for Machine Learning. Morgan Kaufmann Publishers, 1993. [4] L. Jiang, D. Wang, Z. Cai and X. Yan : Survey of Improving Naive Bayes for Classification. In: Lecture Notes in Computer Science, Vol. 4632, pp. 134-145, Springer-Verlag , Berlin Heidelberg , 2007. [5] Hsu C. W., Lin C. J., A comparison of methods for multiclass support vector machines, IEEE Transactions on Neural Networks, 2002, 13 (2): 415-425. [6] R. Agrawal. “K-Nearest Neighborn for Uncertain Data”. International Journal of Computer Applications (0975-8887). 2014. Vol. 105 No. 11 pp 13-16.
There are 1 citations in total.

Details

Primary Language Turkish
Subjects Engineering
Journal Section Research & Review Articles
Authors

Yavuz Ünal 0000-0002-3007-679X

Muhammed Nuri Dudak

Publication Date July 2, 2020
Published in Issue Year 2020 Volume: 1 Issue: 1

Cite

APA Ünal, Y., & Dudak, M. N. (2020). CLASSIFICATION OF COVID-19 DATASET WITH SOME MACHINE LEARNING METHODS. Journal of Amasya University the Institute of Sciences and Technology, 1(1), 30-37.
AMA Ünal Y, Dudak MN. CLASSIFICATION OF COVID-19 DATASET WITH SOME MACHINE LEARNING METHODS. J. Amasya Univ. Inst. Sci. Technol. July 2020;1(1):30-37.
Chicago Ünal, Yavuz, and Muhammed Nuri Dudak. “CLASSIFICATION OF COVID-19 DATASET WITH SOME MACHINE LEARNING METHODS”. Journal of Amasya University the Institute of Sciences and Technology 1, no. 1 (July 2020): 30-37.
EndNote Ünal Y, Dudak MN (July 1, 2020) CLASSIFICATION OF COVID-19 DATASET WITH SOME MACHINE LEARNING METHODS. Journal of Amasya University the Institute of Sciences and Technology 1 1 30–37.
IEEE Y. Ünal and M. N. Dudak, “CLASSIFICATION OF COVID-19 DATASET WITH SOME MACHINE LEARNING METHODS”, J. Amasya Univ. Inst. Sci. Technol., vol. 1, no. 1, pp. 30–37, 2020.
ISNAD Ünal, Yavuz - Dudak, Muhammed Nuri. “CLASSIFICATION OF COVID-19 DATASET WITH SOME MACHINE LEARNING METHODS”. Journal of Amasya University the Institute of Sciences and Technology 1/1 (July 2020), 30-37.
JAMA Ünal Y, Dudak MN. CLASSIFICATION OF COVID-19 DATASET WITH SOME MACHINE LEARNING METHODS. J. Amasya Univ. Inst. Sci. Technol. 2020;1:30–37.
MLA Ünal, Yavuz and Muhammed Nuri Dudak. “CLASSIFICATION OF COVID-19 DATASET WITH SOME MACHINE LEARNING METHODS”. Journal of Amasya University the Institute of Sciences and Technology, vol. 1, no. 1, 2020, pp. 30-37.
Vancouver Ünal Y, Dudak MN. CLASSIFICATION OF COVID-19 DATASET WITH SOME MACHINE LEARNING METHODS. J. Amasya Univ. Inst. Sci. Technol. 2020;1(1):30-7.
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