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Türkiye Covid-19 günlük hasta sayısındaki değişimin sınıflandırılmasına yönelik tahmininin destek vektör makineleri ve k-en yakın komşu algoritmaları ile gerçekleştirilmesi

Year 2022, Volume: 12 Issue: 1, 370 - 379, 15.01.2022
https://doi.org/10.17714/gumusfenbil.892253

Abstract

Covid-19 virüsü hayatımıza girdiği Aralık 2019’dan bu yana etkinliğini kaybetmeden tüm dünyayı etkilemeye devam etmektedir. Dünya sağlık örgütünün önerileri, ülkelerin kendi bünyelerinde aldıkları tedbirler ve aşı çalışmaları virüsün üstesinden gelmek için büyük önem arz etmektedir. Bu bağlamda birçok bilimsel çalışma virüsün geleceği için değerli bilgiler ortaya koymuştur. Çalışmada Türkiye Covid-19 günlük hasta sayısındaki değişimin sınıflandırılmasına yönelik tahminler destek vektör makinesi ve k-en yakın komşu algoritmaları ile yapılmıştır. Günlük hasta sayısının sınıflandırılmasının tahmininde etkin rol oynayan öznitelikler ‘pozitif çıkma oranı’, ‘filyasyon oranı’, ‘işyerleri hareketliliği’ ve ‘parklardaki hareketlilik’ olarak belirlenmiştir. Bu etkin öznitelikler yardımıyla yapılan günlük hasta sayısının sınıflandırılması tahmininde de k-en yakın komşu algoritmasının (%84,7) en başarılı algoritma olduğu gözlemlenmiştir.

References

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  • Durusoy, R., Teneler, A. A., Geçim, C., Özbay, N. F., Küçük, E. F., Şimşek, S., & Ersel, M. (2020). Ege Üniversitesi Tıp Fakültesi Hastanesi’nde COVID-19 vakalarının sürveyansı, filyasyonu ve temaslılarının belirlenmesi. Turkish Journal of Public Health, 18(COVID-19 Special), 25-39. https://doi.org/10.20518/tjph.771286
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  • Filiz, E., & Öz, E. (2019). Finding The Best Algorithms And Effective Factors In Classification Of Turkish Science Student Success. Journal of Baltic Science Education, 18(2), 239. https://doi.org/10.33225/jbse/19.18.239
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  • Kemalbay G., & Alkiş B. N. (2020). Borsa endeks hareket yönünün çoklu lojistik regresyon ve k-en yakın komşu algoritması ile tahmini. Pamukkale Üniversitesi Mühendislik Bilimleri Dergisi, 26 (8). https://doi.org/10.5505/pajes.2020.57383
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  • Kononenko, I. (1994, April). Estimating attributes: Analysis and extensions of RELIEF. In European conference on machine learning, (ss. 171-182). Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-57868-4_57
  • Kononenko, I., Šimec, E., & Robnik-Šikonja, M. (1997). Overcoming the myopia of inductive learning algorithms with RELIEFF. Applied Intelligence, 7(1), 39-55. https://doi.org/10.1023/A:1008280620621
  • Kushwaha, S., Bahl, S., Bagha, A. K., Parmar, K. S., Javaid, M., Haleem, A., & Singh, R. P. (2020). Significant applications of machine learning for COVID-19 pandemic. Journal of Industrial Integration and Management, 5(4). https://doi.org/10.1142/S2424862220500268
  • Lalmuanawma, S., Hussain, J., & Chhakchhuak, L. (2020). Applications of machine learning and artificial intelligence for Covid-19 (SARS-CoV-2) pandemic: A review. Chaos, Solitons & Fractals, 110059. https://doi.org/10.1016/j.chaos.2020.110059
  • Li, B., Yu, S., & Lu, Q. (2003). An improved k-nearest neighbor algorithm for text categorization. Proceedings of the 20th International Conference on Computer Processing of Oriental Languages. https://arxiv.org/ftp/cs/papers/0306/0306099.pdf.
  • Malki, Z., Atlam, E. S., Hassanien, A. E., Dagnew, G., Elhosseini, M. A., & Gad, I. (2020). Association between weather data and COVID-19 pandemic predicting mortality rate: Machine learning approaches. Chaos, Solitons & Fractals, 138, 110137. https://doi.org/10.1016/j.chaos.2020.110137
  • Mitchell, T. M. (1997). Machine Learning. Burr Ridge, IL: McGraw Hill, 45(37), 870-877.
  • Punn, N. S., Sonbhadra, S. K., & Agarwal, S. (2020). COVID-19 epidemic analysis using machine learning and deep learning algorithms. MedRxiv. https://doi.org/10.1101/2020.04.08.20057679
  • Shahiri, A.M., & Husain, W. (2015). A review on predicting student's performance using data mining techniques. Procedia Computer Science, 72, 414-422. https://doi.org/10.1016/j.procs.2015.12.157
  • Shawe-Taylor, J., Bartlett, P. L., Williamson, R.C., & Anthony, M. (1998). Structural risk minimization over data-dependent hierarchies. IEEE transactions on Information Theory, 44(5), 1926-1940. https://doi.org/10.1109/18.705570
  • Şimşek, A. Ç., Kara, A., Baran-Aksakal, F. N., Gülüm, M., Ilter, B., Ender, L., & Demirkasimoğlu, M. (2020). Contact tracing management of the COVID-19 pandemic. Türk Hijyen ve Deneysel Biyoloji Dergisi, 269. https://doi.org/10.5505/TurkHijyen.2020.80688
  • Sirkeci, I., Özerim, M. G., & Bilecen, T. (2020). Editörden: kovid-19’un uluslararası hareketlilik ve göçmenliğe ilişkin etkisi üzerine. Göç Dergisi, 7(1), 1-8. https://doi.org/10.33182/gd.v7i1.688
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  • Tuncer, E., & Bolat, E. D. Destek Vektör Makinaları ile EEG Sinyallerinden Epileptik Nöbet Sınıflandırması. Politeknik Dergisi, 1-1..https://doi.org/10.2339/politeknik.672077
  • Ulaş, E. (2021). Prediction of COVID-19 Pandemic Before The Latest Restrictions in Turkey by Using SIR Model. Suleyman Demirel University Journal of Science, 16(1), 77-85. https://doi.org/10.29233/sdufeffd.852222
  • Urbanowicz, R. J., Meeker, M., La Cava, W., Olson, R. S., & Moore, J. H. (2018). Relief-based feature selection: Introduction and review. Journal of biomedical informatics, 85, 189-203. https://doi.org/10.1016/j.jbi.2018.07.014
  • Wang, P., Zheng, X., Li, J., & Zhu, B. (2020). Prediction of epidemic trends in COVID-19 with logistic model and machine learning technics. Chaos, Solitons & Fractals, 139, 110058. https://doi.org/10.1016/j.chaos.2020.110058
  • Willmott, C. J., & Matsuura, K. (2005). Advantages of the mean absolute error (MAE) over the root mean square error (RMSE) in assessing average model performance. Climate research, 30(1), 79-82. https://doi.org/10.3354/cr030079
  • Xia, S., Xiong, Z., Luo, Y., Dong, L., & Zhang, G. (2015). Location difference of multiple distances based k-nearest neighbors algorithm. Knowledge-Based Systems, 90, 99-110. https://doi.org/10.1016/j.knosys.2015.09.028
  • Yadav, M., Perumal, M., & Srinivas, M. (2020). Analysis on novel coronavirus (COVID-19) using machine learning methods. Chaos, Solitons & Fractals, 139, 110050. https://doi.org/10.1016/j.chaos.2020.110050
  • Zhang, S., Li, X., Zong, M., Zhu, X., & Wang, R. (2017). Efficient kNN classification with different numbers of nearest neighbors. IEEE transactions on neural networks and learning systems, 29(5), 1774-1785. https://doi.org/10.1109/TNNLS.2017.2673241

Classification in the change of estimated number of Covid-19 daily cases by using support vector machine and k-nearest neighbor algorithm

Year 2022, Volume: 12 Issue: 1, 370 - 379, 15.01.2022
https://doi.org/10.17714/gumusfenbil.892253

Abstract

Since December 2019, the Covid-19 virus afftected our lives and continues to affect the whole world significantly. The investigistion of the indicators of the Covid-19 virus and vaccination studies are of great interest to overcome the Covid-19 pandemic based on the World health organization recommendations. In this context, many scientific studies have revealed valuable information for the future of the virus. In this study, estimation of the cOvid-19 cases and Classification of changes in the daily number of cases in Turkey was carried out by using support vector machine and k-nearest neighbor algorithms. The indicators that play a critical role in the estimation of the daily patient number classification have been determined as "positivity rate", "fillation rate", "workplace mobility" and "mobility in parks". It has been observed that the k-nearest neighbor algorithm (84.7%) is the most successful algorithm in the estimation of the daily number of cases when considering the highlighted features.

References

  • Abakar, K. A. A., & Yu, C. (2014). Performance of SVM based on PUK kernel in comparison to SVM based on RBF kernel in prediction of yarn tenacity. Indian Journal of Fibre and Textile Research, 39, 55-59.
  • Afacan, E., & Avcı, N. (2020). Koronavirüs (Covid-19) Örneği Üzerinden Salgın Hastalıklara Sosyolojik Bir Bakış. Avrasya Sosyal ve Ekonomi Araştırmaları Dergisi, 7(5), 1-14.
  • Alpaydın, E. (2004). Introduction to machine learning.
  • Ardabili, S. F., Mosavi, A., Ghamisi, P., Ferdinand, F., Varkonyi-Koczy, A. R., Reuter, U., & Atkinson, P. M. (2020). Covid-19 outbreak prediction with machine learning. Algorithms, 13(10), 249. https://doi.org/10.3390/a13100249
  • Ayaz, M. (2021). Makine öğrenmesi algoritmaları ile covid-19 hastalarının belirlenmesi (Master's thesis, Pamukkale Üniversitesi Sosyal Bilimler Enstitüsü).
  • Barstugan, M., Ozkaya, U., & Ozturk, S. (2020). Coronavirus (covid-19) classification using ct images by machine learning methods. arXiv preprint arXiv:2003.09424.
  • Bontempi, G., Taieb, S. B., & Le Borgne, Y. A. (2012, July). Machine learning strategies for time series forecasting. In European business intelligence summer school (ss. 62-77). Springer, Berlin, Heidelberg. Cortes, C., & Vapnik, V. (1995). Support-vector networks. Machine learning, 20(3), 273-297. https://doi.org/10.1007/BF00994018
  • De Felice, F., & Polimeni, A. (2020). Coronavirus Disease (COVID-19): A Machine learning bibliometric analysis. In vivo, 34(3 suppl), 1613-1617. https://doi.org/10.21873/invivo.11951
  • Demirtas, T., & Tekiner, H. (2020). Filiation: a historical term the COVID-19 outbreak recalled in Turkey. Erciyes Medical Journal, 42(3), 354-359.
  • Depren, S. K., Aşkın, Ö. E., & Öz, E. (2017). Identifying the classification performances of educational data mining methods: a case study for TIMSS. Educational Sciences: Theory & Practice, 17(5), 1605-1623. https://doi.org/10.12738/estp.2017.5.0634
  • Durusoy, R., Teneler, A. A., Geçim, C., Özbay, N. F., Küçük, E. F., Şimşek, S., & Ersel, M. (2020). Ege Üniversitesi Tıp Fakültesi Hastanesi’nde COVID-19 vakalarının sürveyansı, filyasyonu ve temaslılarının belirlenmesi. Turkish Journal of Public Health, 18(COVID-19 Special), 25-39. https://doi.org/10.20518/tjph.771286
  • Fanelli, D., & Piazza, F. (2020). Analysis and forecast of COVID-19 spreading in China, Italy and France. Chaos, Solitons & Fractals, 134, 109761. https://doi.org/10.1016/j.chaos.2020.109761
  • Filiz, E., & Öz, E. (2019). Finding The Best Algorithms And Effective Factors In Classification Of Turkish Science Student Success. Journal of Baltic Science Education, 18(2), 239. https://doi.org/10.33225/jbse/19.18.239
  • Google Web Sayfası - Google Covid-19 Topluluk Hareketliliği Raporları, https://www.google.com/covid19/mobility/ (Erişim tarihi: 16.02.2021).
  • Gümüşçü, A., AydileK, İ.B., & Taşaltın, R. (2016). Mikro-dizilim Veri Sınıflandırmasında Öznitelik Seçme Algoritmalarının Karşılaştırılması. Harran Üniversitesi Mühendislik Dergisi, 1(1), 1-7.
  • Haykin, S. (1999). Neural Networks: A comprehensive Foundation.
  • Horton, P., & Nakai, K. (1997, June). Better Prediction of Protein Cellular Localization Sites with the it k Nearest Neighbors Classifier. In Ismb, 5, 147-152.
  • Kavzoğlu, T., & Çölkesen, İ. (2010). Destek vektör makineleri ile uydu görüntülerinin sınıflandırılmasında kernel fonksiyonlarının etkilerinin incelenmesi. Harita Dergisi, 144(7), 73-82.
  • Kemalbay G., & Alkiş B. N. (2020). Borsa endeks hareket yönünün çoklu lojistik regresyon ve k-en yakın komşu algoritması ile tahmini. Pamukkale Üniversitesi Mühendislik Bilimleri Dergisi, 26 (8). https://doi.org/10.5505/pajes.2020.57383
  • Kira, K., & Rendell, L. A. (1992). A practical approach to feature selection. In Machine learning proceedings, (ss. 249-256). Morgan Kaufmann. https://doi.org/10.1016/B978-1-55860-247-2.50037-1
  • Kononenko, I. (1994, April). Estimating attributes: Analysis and extensions of RELIEF. In European conference on machine learning, (ss. 171-182). Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-57868-4_57
  • Kononenko, I., Šimec, E., & Robnik-Šikonja, M. (1997). Overcoming the myopia of inductive learning algorithms with RELIEFF. Applied Intelligence, 7(1), 39-55. https://doi.org/10.1023/A:1008280620621
  • Kushwaha, S., Bahl, S., Bagha, A. K., Parmar, K. S., Javaid, M., Haleem, A., & Singh, R. P. (2020). Significant applications of machine learning for COVID-19 pandemic. Journal of Industrial Integration and Management, 5(4). https://doi.org/10.1142/S2424862220500268
  • Lalmuanawma, S., Hussain, J., & Chhakchhuak, L. (2020). Applications of machine learning and artificial intelligence for Covid-19 (SARS-CoV-2) pandemic: A review. Chaos, Solitons & Fractals, 110059. https://doi.org/10.1016/j.chaos.2020.110059
  • Li, B., Yu, S., & Lu, Q. (2003). An improved k-nearest neighbor algorithm for text categorization. Proceedings of the 20th International Conference on Computer Processing of Oriental Languages. https://arxiv.org/ftp/cs/papers/0306/0306099.pdf.
  • Malki, Z., Atlam, E. S., Hassanien, A. E., Dagnew, G., Elhosseini, M. A., & Gad, I. (2020). Association between weather data and COVID-19 pandemic predicting mortality rate: Machine learning approaches. Chaos, Solitons & Fractals, 138, 110137. https://doi.org/10.1016/j.chaos.2020.110137
  • Mitchell, T. M. (1997). Machine Learning. Burr Ridge, IL: McGraw Hill, 45(37), 870-877.
  • Punn, N. S., Sonbhadra, S. K., & Agarwal, S. (2020). COVID-19 epidemic analysis using machine learning and deep learning algorithms. MedRxiv. https://doi.org/10.1101/2020.04.08.20057679
  • Shahiri, A.M., & Husain, W. (2015). A review on predicting student's performance using data mining techniques. Procedia Computer Science, 72, 414-422. https://doi.org/10.1016/j.procs.2015.12.157
  • Shawe-Taylor, J., Bartlett, P. L., Williamson, R.C., & Anthony, M. (1998). Structural risk minimization over data-dependent hierarchies. IEEE transactions on Information Theory, 44(5), 1926-1940. https://doi.org/10.1109/18.705570
  • Şimşek, A. Ç., Kara, A., Baran-Aksakal, F. N., Gülüm, M., Ilter, B., Ender, L., & Demirkasimoğlu, M. (2020). Contact tracing management of the COVID-19 pandemic. Türk Hijyen ve Deneysel Biyoloji Dergisi, 269. https://doi.org/10.5505/TurkHijyen.2020.80688
  • Sirkeci, I., Özerim, M. G., & Bilecen, T. (2020). Editörden: kovid-19’un uluslararası hareketlilik ve göçmenliğe ilişkin etkisi üzerine. Göç Dergisi, 7(1), 1-8. https://doi.org/10.33182/gd.v7i1.688
  • T.C. Sağlık Bakanlığı Web Sayfası, https://covid19.saglik.gov.tr/ (Erişim tarihi: 15.02.2021).
  • Tuli, S., Tuli, S., Tuli, R., & Gill, S. S. (2020). Predicting the growth and trend of COVID-19 pandemic using machine learning and cloud computing. Internet of Things, 11, 100222. https://doi.org/10.1016/j.iot.2020.100222
  • Tuncer, E., & Bolat, E. D. Destek Vektör Makinaları ile EEG Sinyallerinden Epileptik Nöbet Sınıflandırması. Politeknik Dergisi, 1-1..https://doi.org/10.2339/politeknik.672077
  • Ulaş, E. (2021). Prediction of COVID-19 Pandemic Before The Latest Restrictions in Turkey by Using SIR Model. Suleyman Demirel University Journal of Science, 16(1), 77-85. https://doi.org/10.29233/sdufeffd.852222
  • Urbanowicz, R. J., Meeker, M., La Cava, W., Olson, R. S., & Moore, J. H. (2018). Relief-based feature selection: Introduction and review. Journal of biomedical informatics, 85, 189-203. https://doi.org/10.1016/j.jbi.2018.07.014
  • Wang, P., Zheng, X., Li, J., & Zhu, B. (2020). Prediction of epidemic trends in COVID-19 with logistic model and machine learning technics. Chaos, Solitons & Fractals, 139, 110058. https://doi.org/10.1016/j.chaos.2020.110058
  • Willmott, C. J., & Matsuura, K. (2005). Advantages of the mean absolute error (MAE) over the root mean square error (RMSE) in assessing average model performance. Climate research, 30(1), 79-82. https://doi.org/10.3354/cr030079
  • Xia, S., Xiong, Z., Luo, Y., Dong, L., & Zhang, G. (2015). Location difference of multiple distances based k-nearest neighbors algorithm. Knowledge-Based Systems, 90, 99-110. https://doi.org/10.1016/j.knosys.2015.09.028
  • Yadav, M., Perumal, M., & Srinivas, M. (2020). Analysis on novel coronavirus (COVID-19) using machine learning methods. Chaos, Solitons & Fractals, 139, 110050. https://doi.org/10.1016/j.chaos.2020.110050
  • Zhang, S., Li, X., Zong, M., Zhu, X., & Wang, R. (2017). Efficient kNN classification with different numbers of nearest neighbors. IEEE transactions on neural networks and learning systems, 29(5), 1774-1785. https://doi.org/10.1109/TNNLS.2017.2673241
There are 42 citations in total.

Details

Primary Language Turkish
Journal Section Articles
Authors

Enes Filiz 0000-0002-8006-9467

Publication Date January 15, 2022
Submission Date March 6, 2021
Acceptance Date January 1, 2022
Published in Issue Year 2022 Volume: 12 Issue: 1

Cite

APA Filiz, E. (2022). Türkiye Covid-19 günlük hasta sayısındaki değişimin sınıflandırılmasına yönelik tahmininin destek vektör makineleri ve k-en yakın komşu algoritmaları ile gerçekleştirilmesi. Gümüşhane Üniversitesi Fen Bilimleri Dergisi, 12(1), 370-379. https://doi.org/10.17714/gumusfenbil.892253