Türkiye Covid-19 günlük hasta sayısındaki değişimin sınıflandırılmasına yönelik tahmininin destek vektör makineleri ve k-en yakın komşu algoritmaları ile gerçekleştirilmesi
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.
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
Classification in the change of estimated number of Covid-19 daily cases by using support vector machine and k-nearest neighbor algorithm
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.
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
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