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Covid-19'un Yayılım Tahminine Yönelik Makine Öğrenmesi ve Derin Öğrenme Tabanlı Karşılaştırmalı Bir Analiz: Türkiye İçin Örnek Bir Çalışma

Year 2022, Volume: 34 Issue: 2, 709 - 717, 30.09.2022
https://doi.org/10.35234/fumbd.1125609

Abstract

Salgınlar tarih boyunca çeşitli zaman dilimlerinde ortaya çıkmış ve insan topluluklarına ciddi zararlar vermiştir. Günümüzde ise bu salgınların modern versiyonu Covid-19 milyonlarca insanın hayatını kaybetmesine ve bir o kadarının da sağlık sorunları yaşamasına yol açmıştır. Tüm dünya, altyapı, finans, veri kaynakları, koruyucu donanımlar, hayati risk tedavileri ve diğer birçok kaynak açısından bu ölümcül hastalığın yayılmasına karşı mücadele etmek için inanılmaz çaba sarf etmektedir. Araştırmacılar ülke çapında paylaşılan verileri kullanarak bu salgın durumunu analiz etmek için matematiksel modeller geliştirmektedirler. Ülkeler salgın hızına bağlı olarak bu salgınla mücadele etmeye çalışmaktadırlar. Bu çalışmada Türkiye özelinde vaka ve ölüm sayılarının tahmin edilmesi için LSTM tabanlı bir tahmin modeli oluşturulmuştur. Bu modelin tahmin başarısını ölçmek için RF, SVM, XGBoost, MLP, CNN ve RNN olmak üzere popüler derin öğrenme yöntemleri dâhil altı makine öğrenmesi yöntemi kullanılmıştır. LSTM modeli vaka sayılarının tahmininde, MSE: 16670823,040 RMSE: 4082,991 MAE: 2543,651 R2: 0,975 sonuçlarını ve ölüm sayılarının tahmininde ise MSE: 331,351 RMSE: 18,203 MAE: 14,891 R2: 0,740 sonuçlarını alarak en başarılı model olmuştur.

References

  • John CC, Ponnusamy V, Chandrasekaran SK, Nandakumar R. A survey on mathematical, machine learning and deep learning models for COVID-19 transmission and diagnosis. IEEE Rev. Biomed. Eng 2021; 15: 325-340.
  • Kreutz R ve diğerler. Hypertension, the renin–angiotensin system, and the risk of lower respiratory tract infections and lung injury: implications for COVID-19: European Society of Hypertension COVID-19 Task Force Review of Evidence. Cardiovasc. Res. 2020; 116(10): 1688-99.
  • Aguiar D, Lobrinus JA, Schibler M, Fracasso T, Lardi C. Inside the lungs of COVID-19 disease. Int. J. Legal Med 2020; 134(4): 1271-4.
  • Ebrahim SH, Ahmed QA, Gozzer E, Schlagenhauf P, Memish ZA. Covid-19 and community mitigation strategies in a pandemic. Bmj. 2020; 368.
  • Courtemanche C, Garuccio J, Le A, Pinkston J, Yelowitz A. Strong Social Distancing Measures In The United States Reduced The COVID-19 Growth Rate: Study evaluates the impact of social distancing measures on the growth rate of confirmed COVID-19 cases across the United States. Health Aff 2020; 39(7): 1237-46.
  • Aleta A, Martin-Corral D, Pastore y Piontti A, Ajelli M, Litvinova M, Chinazzi M, Dean NE, Halloran ME, Longini Jr IM, Merler S, Pentland A. Modelling the impact of testing, contact tracing and household quarantine on second waves of COVID-19. Nat. Hum. Behav 2020; 4(9): 964-71.
  • Heath C, Sommerfield A, von Ungern‐Sternberg BS. Resilience strategies to manage psychological distress among healthcare workers during the COVID‐19 pandemic: a narrative review. Anaesthesia. 2020; 75(10): 1364-71.
  • Melnick ER, Ioannidis JP. Should governments continue lockdown to slow the spread of covid-19?. BMJ 2020;369.
  • Punn NS, Sonbhadra SK, Agarwal S. COVID-19 epidemic analysis using machine learning and deep learning algorithms. MedRxiv. 2020 Jan 1.
  • Alazab M, Awajan A, Mesleh A, Abraham A, Jatana V, Alhyari S. COVID-19 prediction and detection using deep learning. Int. J. Comput. Inf. Syst. Ind. Manage. Appl 2020; 12:168-81.
  • Malki Z, Atlam ES, Ewis A, Dagnew G, Ghoneim OA, Mohamed AA, Abdel-Daim MM, Gad I. The COVID-19 pandemic: prediction study based on machine learning models. Environ. Sci. Pollut. Res 2021; 28(30): 40496-506.
  • Pinter G, Felde I, Mosavi A, Ghamisi P, Gloaguen R. COVID-19 pandemic prediction for Hungary; a hybrid machine learning approach. Math 2020; 8(6): 890.
  • Malki Z, Atlam ES, Hassanien AE, Dagnew G, Elhosseini MA, Gad I. Association between weather data and COVID-19 pandemic predicting mortality rate: Machine learning approaches. Chaos, Solitons Fractals. 2020; 138: 110137.
  • Shahid F, Zameer A, Muneeb M. Predictions for COVID-19 with deep learning models of LSTM, GRU and Bi-LSTM. Chaos, Solitons Fractals. 2020; 140: 110212.
  • Kulkarni VY, Sinha PK. Pruning of random forest classifiers: A survey and future directions. In: 2012 International Conference on Data Science & Engineering (ICDSE); 18-20 July 2012; Cochin, India: IEEE. pp. 64-68.
  • Jiang P, Wu H, Wang W, Ma W, Sun X, Lu Z. MiPred: classification of real and pseudo microRNA precursors using random forest prediction model with combined features. Nucleic Acids Res 2007; 35(suppl_2): W339-44.
  • Wang Z, Wang Y, Zeng R, Srinivasan RS, Ahrentzen S. Random Forest based hourly building energy prediction. Energy Build 2018; 171: 11-25.
  • Zakariah M. Classification of large datasets using Random Forest Algorithm in various applications: Survey. Int. J. Eng. Innovative Technol 2014;4(3).
  • Drucker, H., Burges, C. J., Kaufman, L., Smola, A., & Vapnik, V. Support vector regression machines. Adv. Neural İnf. Process. Syst 1997; 9: 155-161.
  • Smola AJ, Schölkopf B. A tutorial on support vector regression. Stat. Comput 2004; 14(3): 199-222.
  • Zhang F, O'Donnell LJ. Support vector regression. Machine Learning, Academic Press, 2020.
  • Chen T, Guestrin C. Xgboost: A scalable tree boosting system. In: Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining; 13-17 August 2016; San Francisco California USA, pp. 785-794.
  • Bentéjac C, Csörgő A, Martínez-Muñoz G. A comparative analysis of gradient boosting algorithms. Artif. Intell. Rev 2021; 54(3): 1937-67
  • Zhang Y, Chen L. A study on forecasting the default risk of bond based on XGBoost algorithm and over-sampling method. Theor. Econ. Lett 202; 11(02): 258.
  • Zhang C, Pan X, Li H, Gardiner A, Sargent I, Hare J, Atkinson PM. A hybrid MLP-CNN classifier for very fine resolution remotely sensed image classification. ISPRS J. Photogramm. Remote Sens 2018; 140: 133-44.
  • Windeatt T. Ensemble MLP classifier design. Computational Intelligence Paradigms, 2008: 133-147, Springer, Berlin, Heidelberg.
  • Car Z, Baressi Šegota S, Anđelić N, Lorencin I, Mrzljak V. Modeling the spread of COVID-19 infection using a multilayer perceptron. Comput. Math. Methods Med 2020.
  • Delashmit WH, Manry MT. Recent developments in multilayer perceptron neural networks. In Proceedings of the seventh Annual Memphis Area Engineering and Science Conference, MAESC 2005 May.
  • Yao G, Lei T, Zhong J. A review of convolutional-neural-network-based action recognition. Pattern Recognit. Lett 2019; 118: 14-22.
  • Dhillon A, Verma GK. Convolutional neural network: a review of models, methodologies and applications to object detection. Prog Artif. Intell 2020; 9(2): 85-112.
  • Krizhevsky A, Sutskever I, Hinton GE. Imagenet classification with deep convolutional neural networks. Adv. Neural Inf. Process. Syst 2012; 25.
  • Bengio Y, Simard P, Frasconi P. Learning long-term dependencies with gradient descent is difficult. IEEE Trans 1994; 5(2): 157-66.
  • Sutskever I, Martens J, Hinton GE. Generating text with recurrent neural networks. InICML 2011 Jan 1.
Year 2022, Volume: 34 Issue: 2, 709 - 717, 30.09.2022
https://doi.org/10.35234/fumbd.1125609

Abstract

References

  • John CC, Ponnusamy V, Chandrasekaran SK, Nandakumar R. A survey on mathematical, machine learning and deep learning models for COVID-19 transmission and diagnosis. IEEE Rev. Biomed. Eng 2021; 15: 325-340.
  • Kreutz R ve diğerler. Hypertension, the renin–angiotensin system, and the risk of lower respiratory tract infections and lung injury: implications for COVID-19: European Society of Hypertension COVID-19 Task Force Review of Evidence. Cardiovasc. Res. 2020; 116(10): 1688-99.
  • Aguiar D, Lobrinus JA, Schibler M, Fracasso T, Lardi C. Inside the lungs of COVID-19 disease. Int. J. Legal Med 2020; 134(4): 1271-4.
  • Ebrahim SH, Ahmed QA, Gozzer E, Schlagenhauf P, Memish ZA. Covid-19 and community mitigation strategies in a pandemic. Bmj. 2020; 368.
  • Courtemanche C, Garuccio J, Le A, Pinkston J, Yelowitz A. Strong Social Distancing Measures In The United States Reduced The COVID-19 Growth Rate: Study evaluates the impact of social distancing measures on the growth rate of confirmed COVID-19 cases across the United States. Health Aff 2020; 39(7): 1237-46.
  • Aleta A, Martin-Corral D, Pastore y Piontti A, Ajelli M, Litvinova M, Chinazzi M, Dean NE, Halloran ME, Longini Jr IM, Merler S, Pentland A. Modelling the impact of testing, contact tracing and household quarantine on second waves of COVID-19. Nat. Hum. Behav 2020; 4(9): 964-71.
  • Heath C, Sommerfield A, von Ungern‐Sternberg BS. Resilience strategies to manage psychological distress among healthcare workers during the COVID‐19 pandemic: a narrative review. Anaesthesia. 2020; 75(10): 1364-71.
  • Melnick ER, Ioannidis JP. Should governments continue lockdown to slow the spread of covid-19?. BMJ 2020;369.
  • Punn NS, Sonbhadra SK, Agarwal S. COVID-19 epidemic analysis using machine learning and deep learning algorithms. MedRxiv. 2020 Jan 1.
  • Alazab M, Awajan A, Mesleh A, Abraham A, Jatana V, Alhyari S. COVID-19 prediction and detection using deep learning. Int. J. Comput. Inf. Syst. Ind. Manage. Appl 2020; 12:168-81.
  • Malki Z, Atlam ES, Ewis A, Dagnew G, Ghoneim OA, Mohamed AA, Abdel-Daim MM, Gad I. The COVID-19 pandemic: prediction study based on machine learning models. Environ. Sci. Pollut. Res 2021; 28(30): 40496-506.
  • Pinter G, Felde I, Mosavi A, Ghamisi P, Gloaguen R. COVID-19 pandemic prediction for Hungary; a hybrid machine learning approach. Math 2020; 8(6): 890.
  • Malki Z, Atlam ES, Hassanien AE, Dagnew G, Elhosseini MA, Gad I. Association between weather data and COVID-19 pandemic predicting mortality rate: Machine learning approaches. Chaos, Solitons Fractals. 2020; 138: 110137.
  • Shahid F, Zameer A, Muneeb M. Predictions for COVID-19 with deep learning models of LSTM, GRU and Bi-LSTM. Chaos, Solitons Fractals. 2020; 140: 110212.
  • Kulkarni VY, Sinha PK. Pruning of random forest classifiers: A survey and future directions. In: 2012 International Conference on Data Science & Engineering (ICDSE); 18-20 July 2012; Cochin, India: IEEE. pp. 64-68.
  • Jiang P, Wu H, Wang W, Ma W, Sun X, Lu Z. MiPred: classification of real and pseudo microRNA precursors using random forest prediction model with combined features. Nucleic Acids Res 2007; 35(suppl_2): W339-44.
  • Wang Z, Wang Y, Zeng R, Srinivasan RS, Ahrentzen S. Random Forest based hourly building energy prediction. Energy Build 2018; 171: 11-25.
  • Zakariah M. Classification of large datasets using Random Forest Algorithm in various applications: Survey. Int. J. Eng. Innovative Technol 2014;4(3).
  • Drucker, H., Burges, C. J., Kaufman, L., Smola, A., & Vapnik, V. Support vector regression machines. Adv. Neural İnf. Process. Syst 1997; 9: 155-161.
  • Smola AJ, Schölkopf B. A tutorial on support vector regression. Stat. Comput 2004; 14(3): 199-222.
  • Zhang F, O'Donnell LJ. Support vector regression. Machine Learning, Academic Press, 2020.
  • Chen T, Guestrin C. Xgboost: A scalable tree boosting system. In: Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining; 13-17 August 2016; San Francisco California USA, pp. 785-794.
  • Bentéjac C, Csörgő A, Martínez-Muñoz G. A comparative analysis of gradient boosting algorithms. Artif. Intell. Rev 2021; 54(3): 1937-67
  • Zhang Y, Chen L. A study on forecasting the default risk of bond based on XGBoost algorithm and over-sampling method. Theor. Econ. Lett 202; 11(02): 258.
  • Zhang C, Pan X, Li H, Gardiner A, Sargent I, Hare J, Atkinson PM. A hybrid MLP-CNN classifier for very fine resolution remotely sensed image classification. ISPRS J. Photogramm. Remote Sens 2018; 140: 133-44.
  • Windeatt T. Ensemble MLP classifier design. Computational Intelligence Paradigms, 2008: 133-147, Springer, Berlin, Heidelberg.
  • Car Z, Baressi Šegota S, Anđelić N, Lorencin I, Mrzljak V. Modeling the spread of COVID-19 infection using a multilayer perceptron. Comput. Math. Methods Med 2020.
  • Delashmit WH, Manry MT. Recent developments in multilayer perceptron neural networks. In Proceedings of the seventh Annual Memphis Area Engineering and Science Conference, MAESC 2005 May.
  • Yao G, Lei T, Zhong J. A review of convolutional-neural-network-based action recognition. Pattern Recognit. Lett 2019; 118: 14-22.
  • Dhillon A, Verma GK. Convolutional neural network: a review of models, methodologies and applications to object detection. Prog Artif. Intell 2020; 9(2): 85-112.
  • Krizhevsky A, Sutskever I, Hinton GE. Imagenet classification with deep convolutional neural networks. Adv. Neural Inf. Process. Syst 2012; 25.
  • Bengio Y, Simard P, Frasconi P. Learning long-term dependencies with gradient descent is difficult. IEEE Trans 1994; 5(2): 157-66.
  • Sutskever I, Martens J, Hinton GE. Generating text with recurrent neural networks. InICML 2011 Jan 1.
There are 33 citations in total.

Details

Primary Language Turkish
Subjects Engineering
Journal Section MBD
Authors

Anıl Utku 0000-0002-7240-8713

Ümit Can 0000-0002-8832-6317

Publication Date September 30, 2022
Submission Date June 3, 2022
Published in Issue Year 2022 Volume: 34 Issue: 2

Cite

APA Utku, A., & Can, Ü. (2022). Covid-19’un Yayılım Tahminine Yönelik Makine Öğrenmesi ve Derin Öğrenme Tabanlı Karşılaştırmalı Bir Analiz: Türkiye İçin Örnek Bir Çalışma. Fırat Üniversitesi Mühendislik Bilimleri Dergisi, 34(2), 709-717. https://doi.org/10.35234/fumbd.1125609
AMA Utku A, Can Ü. Covid-19’un Yayılım Tahminine Yönelik Makine Öğrenmesi ve Derin Öğrenme Tabanlı Karşılaştırmalı Bir Analiz: Türkiye İçin Örnek Bir Çalışma. Fırat Üniversitesi Mühendislik Bilimleri Dergisi. September 2022;34(2):709-717. doi:10.35234/fumbd.1125609
Chicago Utku, Anıl, and Ümit Can. “Covid-19’un Yayılım Tahminine Yönelik Makine Öğrenmesi Ve Derin Öğrenme Tabanlı Karşılaştırmalı Bir Analiz: Türkiye İçin Örnek Bir Çalışma”. Fırat Üniversitesi Mühendislik Bilimleri Dergisi 34, no. 2 (September 2022): 709-17. https://doi.org/10.35234/fumbd.1125609.
EndNote Utku A, Can Ü (September 1, 2022) Covid-19’un Yayılım Tahminine Yönelik Makine Öğrenmesi ve Derin Öğrenme Tabanlı Karşılaştırmalı Bir Analiz: Türkiye İçin Örnek Bir Çalışma. Fırat Üniversitesi Mühendislik Bilimleri Dergisi 34 2 709–717.
IEEE A. Utku and Ü. Can, “Covid-19’un Yayılım Tahminine Yönelik Makine Öğrenmesi ve Derin Öğrenme Tabanlı Karşılaştırmalı Bir Analiz: Türkiye İçin Örnek Bir Çalışma”, Fırat Üniversitesi Mühendislik Bilimleri Dergisi, vol. 34, no. 2, pp. 709–717, 2022, doi: 10.35234/fumbd.1125609.
ISNAD Utku, Anıl - Can, Ümit. “Covid-19’un Yayılım Tahminine Yönelik Makine Öğrenmesi Ve Derin Öğrenme Tabanlı Karşılaştırmalı Bir Analiz: Türkiye İçin Örnek Bir Çalışma”. Fırat Üniversitesi Mühendislik Bilimleri Dergisi 34/2 (September 2022), 709-717. https://doi.org/10.35234/fumbd.1125609.
JAMA Utku A, Can Ü. Covid-19’un Yayılım Tahminine Yönelik Makine Öğrenmesi ve Derin Öğrenme Tabanlı Karşılaştırmalı Bir Analiz: Türkiye İçin Örnek Bir Çalışma. Fırat Üniversitesi Mühendislik Bilimleri Dergisi. 2022;34:709–717.
MLA Utku, Anıl and Ümit Can. “Covid-19’un Yayılım Tahminine Yönelik Makine Öğrenmesi Ve Derin Öğrenme Tabanlı Karşılaştırmalı Bir Analiz: Türkiye İçin Örnek Bir Çalışma”. Fırat Üniversitesi Mühendislik Bilimleri Dergisi, vol. 34, no. 2, 2022, pp. 709-17, doi:10.35234/fumbd.1125609.
Vancouver Utku A, Can Ü. Covid-19’un Yayılım Tahminine Yönelik Makine Öğrenmesi ve Derin Öğrenme Tabanlı Karşılaştırmalı Bir Analiz: Türkiye İçin Örnek Bir Çalışma. Fırat Üniversitesi Mühendislik Bilimleri Dergisi. 2022;34(2):709-17.