Covid-19'da Pozitif Vaka Oranı Tahmini İçin Aşırı Öğrenme Makinesi Algoritmaları: Karşılaştırmalı Bir Çalışma

Year 2023, , 170 - 188, 31.01.2023

Salih Berkan Aydemir , Funda Kutlu Onay

https://doi.org/10.29130/dubited.999953

Abstract

Dünya tarihinde bugüne kadar çeşitli pandemiler meydana gelmiştir. 2019 yılının sonunda ortaya çıkan Covid-19 salgını son zamanlarda literatürde güncel bir konu haline geldi. Bu çalışmada, aşırı öğrenme algoritmaları, en fazla pozitif vakaların görüldüğü ülkeler olan Hindistan, Türkiye, İtalya, ABD ve İngiltere için pozitif oranı tahmin etmeye yönelik karşılaştırmalı bir çalışma olarak sunulmaktadır. F-testi öznitelik seçme yöntemi ile öğrenme aşamasında kullanılacak öznitelikler belirlenir. Her bir aşırı öğrenme yaklaşımı ve her bir ülke için hata ortalama karekökü değerlendirme kriterleri ile sonuçlar elde edilir. Buna göre, radyal tabanlı çekirdek fonksiyonu en iyi tahmin sonuçlarını üretirken, doğrusal çekirdek fonksiyonu en yüksek RMSE'ye sahiptir. Buna göre Hindistan için en düşük RMSE değeri radyal tabanlı çekirdek fonksiyonu tabanlı ELM ile 4.17E-03 olarak elde edilmiştir. Ayrıca Türkiye verileri çok fazla aykırı değer içerdiğinden doğrusal çekirdek yönteminde ülkeler arasında en yüksek RMSE değerine (0.015 - 0.035) sahiptir.

Keywords

Aşırı Öğrenme Makinaları, Covid-19, Tahmin, Öznitelik Seçimi

Extreme Learning Machine Algorithms for Prediction of Positive Rate in Covid-19: A Comparative Study

Abstract

Various pandemics have been recorded in world history until today. The Covid-19 outbreak, which emerged at the end of 2019, has recently been a hot topic in the literature. In this work, extreme learning algorithms are presented as a comparative study for predicting the positive rate for the countries: India, Turkey, Italy, USA and UK. The features to be used in the learning phase are determined with the F-test feature selection method. For each extreme learning approach, results are obtained for each country with the root mean square error evaluation criteria. Accordingly, the radial basis kernel function produces the best estimation results, while the linear kernel function has the highest RMSE. Accordingly, the lowest RMSE value has been obtained for India as 4.17E-03 with the radial basis kernel function based ELM. Also, since Turkey's data contains too many outliers, it has the highest RMSE value (0.015 - 0.035) in linear kernel method among the countries.

Keywords

Extreme Learning Machines, Prediction, Feature Selection, Aşırı Öğrenme Makinaları, Covid-19, Tahmin, Öznitelik Seçimi

References

• [1] WHO. (2020, 2021-04-26). World health organization (2020) covid-19 situation reports. Available: https: //www.who.int/emergencies/diseases/novel-coronavirus-2019/ situation-reports.
• [2] Worldometer, "Coronavirus cases:," 2021-04-26.
• [3] Q. Li, W. Feng, and Y.-H. Quan, "Trend and forecasting of the COVID-19 outbreak in China," Journal of Infection, vol. 80, no. 4, pp. 469-496, 2020.
• [4] D. Fanelli and F. Piazza, "Analysis and forecast of COVID-19 spreading in China, Italy and France," Chaos, Solitons & Fractals, vol. 134, p. 109761, 2020.
• [5] W. Wei et al., "Application of a combined model with autoregressive integrated moving average (ARIMA) and generalized regression neural network (GRNN) in forecasting hepatitis incidence in Heng County, China," PloS one, vol. 11, no. 6, p. e0156768, 2016.
• [6] Z. Ceylan, "Estimation of COVID-19 prevalence in Italy, Spain, and France," Science of The Total Environment, vol. 729, p. 138817, 2020.
• [7] A. F. Lukman, R. I. Rauf, O. Abiodun, O. Oludoun, K. Ayinde, and R. O. Ogundokun, "COVID- 19 prevalence estimation: Four most affected African countries," Infectious Disease Modelling, vol. 5, pp. 827-838, 2020.
• [8] A. M. Almeshal, A. I. Almazrouee, M. R. Alenizi, and S. N. Alhajeri, "Forecasting the spread of COVID-19 in Kuwait using compartmental and logistic regression models," Applied Sciences, vol. 10, no. 10, p. 3402, 2020.
• [9] R. O. Ogundokun, A. F. Lukman, G. B. Kibria, J. B. Awotunde, and B. B. Aladeitan, "Predictive modelling of COVID-19 confirmed cases in Nigeria," Infectious Disease Modelling, vol. 5, pp. 543- 548, 2020.
• [10] M. Djeddou, I. A. Hameed, A. Hellal, and A. Nejatian, "Predictive modeling of COVID-19 New Confirmed Cases in Algeria using Artificial Neural Network," medRxiv, 2021.
• [11] M. A. Achterberg, B. Prasse, L. Ma, S. Trajanovski, M. Kitsak, and P. Van Mieghem, "Comparing the accuracy of several network-based COVID-19 prediction algorithms," International journal of forecasting, 2020.
• [12] W. He, G. Y. Yi, and Y. Zhu, "Estimation of the basic reproduction number, average incubation time, asymptomatic infection rate, and case fatality rate for COVID‐19: Meta‐analysis and sensitivity analysis," Journal of medical virology, vol. 92, no. 11, pp. 2543-2550, 2020.
• [13] A. M. Hasan, A. G. Mahmoud, and Z. M. Hasan, "Optimized Extreme Learning Machine for Forecasting Confirmed Cases of COVID-19," International Journal of Intelligent Engineering and Systems, pp. 484-494, 2021.
• [14] G. Pinter, I. Felde, A. Mosavi, P. Ghamisi, and R. Gloaguen, "COVID-19 pandemic prediction for Hungary; a hybrid machine learning approach," Mathematics, vol. 8, no. 6, p. 890, 2020.
• [15] M. Yadav, M. Perumal, and M. Srinivas, "Analysis on novel coronavirus (COVID-19) using machine learning methods," Chaos, Solitons & Fractals, vol. 139, p. 110050, 2020.
• [16] S. Rath, A. Tripathy, and A. R. Tripathy, "Prediction of new active cases of coronavirus disease (COVID-19) pandemic using multiple linear regression model," Diabetes & Metabolic Syndrome: Clinical Research & Reviews, vol. 14, no. 5, pp. 1467-1474, 2020.
• [17] S. Ghosal, S. Sengupta, M. Majumder, and B. Sinha, "Linear Regression Analysis to predict the number of deaths in India due to SARS-CoV-2 at 6 weeks from day 0 (100 cases-March 14th 2020)," Diabetes & Metabolic Syndrome: Clinical Research & Reviews, vol. 14, no. 4, pp. 311-315, 2020.
• [18] R. Sujath, J. M. Chatterjee, and A. E. Hassanien, "A machine learning forecasting model for COVID-19 pandemic in India," Stochastic Environmental Research and Risk Assessment, vol. 34, pp. 959-972, 2020.
• [19] therealcyberlord, "Coronavirus (covid-19) visualization & prediction."
• [20] G.-B. Huang, Q.-Y. Zhu, and C.-K. Siew, "Extreme learning machine: theory and applications," Neurocomputing, vol. 70, no. 1-3, pp. 489-501, 2006.
• [21] G.-B. Huang, H. Zhou, X. Ding, and R. Zhang, "Extreme learning machine for regression and multiclass classification," IEEE Transactions on Systems, Man, and Cybernetics, Part B (Cybernetics), vol. 42, no. 2, pp. 513-529, 2011.
• [22] W. Deng, Q. Zheng, and L. Chen, "Regularized extreme learning machine," in 2009 IEEE symposium on computational intelligence and data mining, 2009, pp. 389-395: IEEE.
• [23] G.-B. Huang and C.-K. Siew, "Extreme learning machine: RBF network case," in ICARCV 2004 8th Control, Automation, Robotics and Vision Conference, 2004., 2004, vol. 2, pp. 1029-1036: IEEE.
• [24] W.-Y. Deng, Q.-H. Zheng, and Z.-M. Wang, "Cross-person activity recognition using reduced kernel extreme learning machine," Neural Networks, vol. 53, pp. 1-7, 2014.
• [25] A. Iosifidis and M. Gabbouj, "On the kernel extreme learning machine speedup," Pattern Recognition Letters, vol. 68, pp. 205-210, 2015.
• [26] Z. Bai, G.-B. Huang, D. Wang, H. Wang, and M. B. Westover, "Sparse extreme learning machine for classification," IEEE transactions on cybernetics, vol. 44, no. 10, pp. 1858-1870, 2014.
• [27] K. Parikh and T. Shah, "Kernel based extreme learning machine in identifying dermatological disorders," International Journal of Innovative Science, Engineering & Technology, vol. 3, no. 10, pp. 370-375, 2016.
• [28] W. Zhu, J. Miao, and L. Qing, "Constrained extreme learning machines: A study on classification cases," arXiv preprint arXiv:1501.06115, 2015.
• [29] N.-Y. Liang, G.-B. Huang, P. Saratchandran, and N. Sundararajan, "A fast and accurate online sequential learning algorithm for feedforward networks," IEEE Transactions on neural networks, vol. 17, no. 6, pp. 1411-1423, 2006.
• [30] J. Wang, S. Lu, S.-H. Wang, and Y.-D. Zhang, "A review on extreme learning machine," Multimedia Tools and Applications, pp. 1-50, 2021.
• [31] J. Hasell et al., "A cross-country database of COVID-19 testing," Scientific data, vol. 7, no. 1, pp. 1-7, 2020.