Araştırma Makalesi
BibTex RIS Kaynak Göster
Yıl 2021, Cilt: 9 Sayı: 2, 187 - 190, 30.04.2021
https://doi.org/10.17694/bajece.893672

Öz

Kaynakça

  • [1] Ş. ALP and S. ÜNAL, "Yeni Koronavirüs (SARS-CoV-2) Kaynaklı Pandemi: Gelişmeler ve Güncel Durum," Flora Dergisi, vol. 25, 2020.
  • [2] M. Hasöksüz, S. Kiliç, and F. Saraç, "Coronaviruses and sars-cov-2," Turkish journal of medical sciences, vol. 50, pp. 549-556, 2020.
  • [3] W. J. Wiersinga, A. Rhodes, A. C. Cheng, S. J. Peacock, and H. C. Prescott, "Pathophysiology, transmission, diagnosis, and treatment of coronavirus disease 2019 (COVID-19): a review," Jama, vol. 324, pp. 782-793, 2020.
  • [4] H. A. Rothan and S. N. Byrareddy, "The epidemiology and pathogenesis of coronavirus disease (COVID-19) outbreak," Journal of autoimmunity, p. 102433, 2020.
  • [5] P. Hyland, M. Shevlin, O. McBride, J. Murphy, T. Karatzias, R. P. Bentall, et al., "Anxiety and depression in the Republic of Ireland during the COVID‐19 pandemic," Acta Psychiatrica Scandinavica, vol. 142, pp. 249-256, 2020.
  • [6] N. Salari, A. Hosseinian-Far, R. Jalali, A. Vaisi-Raygani, S. Rasoulpoor, M. Mohammadi, et al., "Prevalence of stress, anxiety, depression among the general population during the COVID-19 pandemic: a systematic review and meta-analysis," Globalization and health, vol. 16, pp. 1-11, 2020.
  • [7] Y. Erdoğdu, F. Koçoğlu, and C. Sevim, "COVID-19 pandemisi sürecinde anksiyete ile umutsuzluk düzeylerinin psikososyal ve demografik değişkenlere göre incelenmesi," Klinik Psikiyatri Dergisi, vol. 23, 2020. [8] I. H. Witten and E. Frank, "Data mining: practical machine learning tools and techniques with Java implementations," Acm Sigmod Record, vol. 31, pp. 76-77, 2002.
  • [9] D. J. Hand and N. M. Adams, "Data Mining," Wiley StatsRef: Statistics Reference Online, pp. 1-7, 2014.
  • [10] R. Polikar, "Ensemble learning," in Ensemble machine learning, ed: Springer, 2012, pp. 1-34.
  • [11] M. Atalay and E. Çelik, "Büyük Veri Analizinde Yapay Zekâ Ve Makine Öğrenmesi Uygulamalari-Artificial Intelligence and Machine Learning Applications in Big Data Analysis," Mehmet Akif Ersoy Üniversitesi Sosyal Bilimler Enstitüsü Dergisi, vol. 9, pp. 155-172, 2017.
  • [12] S. K. G, Naveen; Jothi, M, "Data set on Impact of COVID-19 on Mental Health of Internal Migrant Workers in India: Corona Virus Anxiety Scale (CAS) Approach," V2 ed: Mendeley Data, 2021.
  • [13] R. Choudhari, "COVID 19 pandemic: mental health challenges of internal migrant workers of India," Asian journal of psychiatry, vol. 54, p. 102254, 2020.
  • [14] G. Ke, Q. Meng, T. Finley, T. Wang, W. Chen, W. Ma, et al., "Lightgbm: A highly efficient gradient boosting decision tree," Advances in neural information processing systems, vol. 30, pp. 3146-3154, 2017.
  • [15] C. Krauss, X. A. Do, and N. Huck, "Deep neural networks, gradient-boosted trees, random forests: Statistical arbitrage on the S&P 500," European Journal of Operational Research, vol. 259, pp. 689-702, 2017.
  • [16] R. Studio, "Visual workflow designer for the entire analytics team," ed, 2019.
  • [17] A. Niknafs, B. Sun, M. Richter, and G. Ruhe, "Predictions in Time Series with Repeated Patterns, Using Piecewise Linear Regression," Technical Report SEDS-TR-094/2011, University of Calgary2011.
  • [18] J. H. Chan, M. Joshi, R. Tang, and C. Yang, "Trinomial or binomial: Accelerating American put option price on trees," Journal of Futures Markets, vol. 29, pp. 826-839, 2009.

A Proposed Ensemble Model for The Prediction of Coronavirus Anxiety Scale of Migrant Workers

Yıl 2021, Cilt: 9 Sayı: 2, 187 - 190, 30.04.2021
https://doi.org/10.17694/bajece.893672

Öz

Objective: This study aimed to evaluate the potential negative effects of the scattered migrant worker population on the anxiety level by estimating the coronavirus anxiety scale (CAS) of the COVID-19 anxiety scale with Gradient Boosting Tree (GBT).
Material and Methods: In this study, a public data set achieved from a questionnaire [developed using the Coronavirus Anxiety Scale (CAS)] was used to conduct on 1350 people over phone calls. GBT model was constructed for predicting the CAS score of migrant workers based on input variables including demographical data. Hyperparameters of the GBT model were tuned using Optimize Parameters (Evolutionary) operator, which seeks the optimum values of the selected parameters by an evolutionary computation approach. Hyperparameters of the GBT model were 50 for the number of trees, 5 for minimal depth, 0.044 for learning rate, and 1.0E-5 for minimum split improvement.
Results: A total of 1500 people, 758 (56.1%) male, and 592 (43.9%) female, participated in this study. The experimental findings demonstrated that the GBT yielded a root mean square error of 3.547±0.235, the absolute error of 2.943±0.154, relative error lenient of 31.54%±0.82%, squared error of 12.623±1.691 and correlation of 0.577±0.130.
Conclusions: Variable importance values for each input were calculated from the model-based results of the GBT model. The largest importance was achieved for income and the lowest was estimated for Covid-19 Infection. The calculated importances can be evaluated the potential impacts on the CAS score. In future works, different algorithms can be built for detailed predictions about COVID-19-related anxiety levels.

Kaynakça

  • [1] Ş. ALP and S. ÜNAL, "Yeni Koronavirüs (SARS-CoV-2) Kaynaklı Pandemi: Gelişmeler ve Güncel Durum," Flora Dergisi, vol. 25, 2020.
  • [2] M. Hasöksüz, S. Kiliç, and F. Saraç, "Coronaviruses and sars-cov-2," Turkish journal of medical sciences, vol. 50, pp. 549-556, 2020.
  • [3] W. J. Wiersinga, A. Rhodes, A. C. Cheng, S. J. Peacock, and H. C. Prescott, "Pathophysiology, transmission, diagnosis, and treatment of coronavirus disease 2019 (COVID-19): a review," Jama, vol. 324, pp. 782-793, 2020.
  • [4] H. A. Rothan and S. N. Byrareddy, "The epidemiology and pathogenesis of coronavirus disease (COVID-19) outbreak," Journal of autoimmunity, p. 102433, 2020.
  • [5] P. Hyland, M. Shevlin, O. McBride, J. Murphy, T. Karatzias, R. P. Bentall, et al., "Anxiety and depression in the Republic of Ireland during the COVID‐19 pandemic," Acta Psychiatrica Scandinavica, vol. 142, pp. 249-256, 2020.
  • [6] N. Salari, A. Hosseinian-Far, R. Jalali, A. Vaisi-Raygani, S. Rasoulpoor, M. Mohammadi, et al., "Prevalence of stress, anxiety, depression among the general population during the COVID-19 pandemic: a systematic review and meta-analysis," Globalization and health, vol. 16, pp. 1-11, 2020.
  • [7] Y. Erdoğdu, F. Koçoğlu, and C. Sevim, "COVID-19 pandemisi sürecinde anksiyete ile umutsuzluk düzeylerinin psikososyal ve demografik değişkenlere göre incelenmesi," Klinik Psikiyatri Dergisi, vol. 23, 2020. [8] I. H. Witten and E. Frank, "Data mining: practical machine learning tools and techniques with Java implementations," Acm Sigmod Record, vol. 31, pp. 76-77, 2002.
  • [9] D. J. Hand and N. M. Adams, "Data Mining," Wiley StatsRef: Statistics Reference Online, pp. 1-7, 2014.
  • [10] R. Polikar, "Ensemble learning," in Ensemble machine learning, ed: Springer, 2012, pp. 1-34.
  • [11] M. Atalay and E. Çelik, "Büyük Veri Analizinde Yapay Zekâ Ve Makine Öğrenmesi Uygulamalari-Artificial Intelligence and Machine Learning Applications in Big Data Analysis," Mehmet Akif Ersoy Üniversitesi Sosyal Bilimler Enstitüsü Dergisi, vol. 9, pp. 155-172, 2017.
  • [12] S. K. G, Naveen; Jothi, M, "Data set on Impact of COVID-19 on Mental Health of Internal Migrant Workers in India: Corona Virus Anxiety Scale (CAS) Approach," V2 ed: Mendeley Data, 2021.
  • [13] R. Choudhari, "COVID 19 pandemic: mental health challenges of internal migrant workers of India," Asian journal of psychiatry, vol. 54, p. 102254, 2020.
  • [14] G. Ke, Q. Meng, T. Finley, T. Wang, W. Chen, W. Ma, et al., "Lightgbm: A highly efficient gradient boosting decision tree," Advances in neural information processing systems, vol. 30, pp. 3146-3154, 2017.
  • [15] C. Krauss, X. A. Do, and N. Huck, "Deep neural networks, gradient-boosted trees, random forests: Statistical arbitrage on the S&P 500," European Journal of Operational Research, vol. 259, pp. 689-702, 2017.
  • [16] R. Studio, "Visual workflow designer for the entire analytics team," ed, 2019.
  • [17] A. Niknafs, B. Sun, M. Richter, and G. Ruhe, "Predictions in Time Series with Repeated Patterns, Using Piecewise Linear Regression," Technical Report SEDS-TR-094/2011, University of Calgary2011.
  • [18] J. H. Chan, M. Joshi, R. Tang, and C. Yang, "Trinomial or binomial: Accelerating American put option price on trees," Journal of Futures Markets, vol. 29, pp. 826-839, 2009.
Toplam 17 adet kaynakça vardır.

Ayrıntılar

Birincil Dil İngilizce
Konular Yapay Zeka
Bölüm Araştırma Makalesi
Yazarlar

Emek Güldoğan 0000-0002-5436-8164

Yayımlanma Tarihi 30 Nisan 2021
Yayımlandığı Sayı Yıl 2021 Cilt: 9 Sayı: 2

Kaynak Göster

APA Güldoğan, E. (2021). A Proposed Ensemble Model for The Prediction of Coronavirus Anxiety Scale of Migrant Workers. Balkan Journal of Electrical and Computer Engineering, 9(2), 187-190. https://doi.org/10.17694/bajece.893672

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