Araştırma Makalesi
BibTex RIS Kaynak Göster

A MACHINE-LEARNING-BASED MODEL FOR FORECASTING MEDICAL DEVICE FOREIGN TRADE

Yıl 2020, Cilt: 21 Sayı: 4, 477 - 485, 28.12.2020
https://doi.org/10.18038/estubtda.803546

Öz

Forecasting the medical device foreign trade is a very important issue and a challenging problem due to many external artifacts in the medical device market for making an efficient policy. Many reports, including the simple statistical based methods do not provide sufficient forecasting for foreign trade and this problem may be solved using a machine-learning based approach. The purpose of this study is to introduce an efficient model for forecasting medical device foreign trade. In this respect, export and import data obtained with 54 different commodity codes were performed using some machine-learning algorithms. The best prediction performance was achieved with SVM regression model with the average R2=0.974 and for the last five years. In 2025, total medical device exports and imports are expected to be $1.03 billion and $2.12 billion, respectively. We also performed Market Penetration Index and Product Diversification Index to analyze medical device foreign trade.

Kaynakça

  • [1] Espicom Medical device reports. BMI Research Company. 2018.
  • [2] EvaluateMedTech World Preview 2018 available from https://info.evaluate.com/WPMT2018-CS.html (Accessed 10 October 2019)
  • [3] ITC Trade Map. Available at: https://www.trademap.org/Index.aspx. Retrieved April 20. 2020.
  • [4] Charles L, Daudin G. Eighteenth-Century International Trade Statistics. Sources and Methods. Revue de l’OFCE 2015; 4: 7-36.
  • [5] Sun J, Suo Y, Park S, Xu T, Liu Y, Wang, W. Analysis of bilateral trade flow and machine learning algorithms for GDP forecasting. Engineering. Technology & Applied Science Research 2018; 8(5): 3432-3438.
  • [6] Yu L, Wang S, Lai K K. Forecasting China’s foreign trade volume with a kernel-based hybrid econometric-AI ensemble learning approach. Journal of Systems Science and Complexity 2008; 21(1): 1-19.
  • [7] Council. E. COUNCIL DIRECTIVE 93/42/EEC concerning medical devices. Official Journal of The European Communities. Luxembourg, 1993.
  • [8] Council. E. COUNCIL DIRECTIVE 98/79/EC concerning in vitro diagnostic medical devices. Official Journal of The European Communities. Luxembourg, 1998.
  • [9] Council. E. COUNCIL DIRECTIVE 90/385/EEC concerning active implantable medical devices. Official Journal of The European Communities. Luxembourg, 1990.
  • [10] Erkan B. Product and market diversification in Turkey’s foreign trade. IJAME, 2018.
  • [11] Lévay PZ, Drossinos Y, Thiel C. The effect of fiscal incentives on market penetration of electric vehicles: A pairwise comparison of total cost of ownership. Energy Policy 2017; 105: 524-533.
  • [12] Fronczek, M. Import penetration rate in view of a new concept of measuring foreign trade. Argumenta Oeconomica 2017; 38: 285-297.
  • [13] Brakman S, Van Marrewijk C. A closer look at revealed comparative advantage: Gross‐versus value‐added trade flows. Papers in Regional Science 2017; 96(1): 61-92.
  • [14] Gozgor G, Can M. Effects of the product diversification of exports on income at different stages of economic development. Eurasian Business Review 2016; 6(2): 215-235.
  • [15] Bangdiwala SI. Regression: simple linear. International journal of injury control and safety promotion 2018; 25(1): 113-115.
  • [16] Malegori C, Marques EJN, de Freitas ST, Pimentel MF, Pasquini C, Casiraghi E. Comparing the analytical performances of Micro-NIR and FT-NIR spectrometers in the evaluation of acerola fruit quality, using PLS and SVM regression algorithms. Talanta 2017; 165: 112-116.
  • [17] Dong X, Greven MC, Kundaje A, et al. Modeling gene expression using chromatin features in various cellular contexts. Genome biology 2012; 13(9): R53.
  • [18] Chai T, Draxler RR. Root mean square error (RMSE) or mean absolute error (MAE)?–Arguments against avoiding RMSE in the literature. Geoscientific model development. 2014; 7(3): 1247-1250.
  • [19] Gelman A, Goodrich B, Gabry J, Vehtari A. R-squared for Bayesian regression models. The American Statistician 2019; 73(3): 307-309.
Yıl 2020, Cilt: 21 Sayı: 4, 477 - 485, 28.12.2020
https://doi.org/10.18038/estubtda.803546

Öz

Kaynakça

  • [1] Espicom Medical device reports. BMI Research Company. 2018.
  • [2] EvaluateMedTech World Preview 2018 available from https://info.evaluate.com/WPMT2018-CS.html (Accessed 10 October 2019)
  • [3] ITC Trade Map. Available at: https://www.trademap.org/Index.aspx. Retrieved April 20. 2020.
  • [4] Charles L, Daudin G. Eighteenth-Century International Trade Statistics. Sources and Methods. Revue de l’OFCE 2015; 4: 7-36.
  • [5] Sun J, Suo Y, Park S, Xu T, Liu Y, Wang, W. Analysis of bilateral trade flow and machine learning algorithms for GDP forecasting. Engineering. Technology & Applied Science Research 2018; 8(5): 3432-3438.
  • [6] Yu L, Wang S, Lai K K. Forecasting China’s foreign trade volume with a kernel-based hybrid econometric-AI ensemble learning approach. Journal of Systems Science and Complexity 2008; 21(1): 1-19.
  • [7] Council. E. COUNCIL DIRECTIVE 93/42/EEC concerning medical devices. Official Journal of The European Communities. Luxembourg, 1993.
  • [8] Council. E. COUNCIL DIRECTIVE 98/79/EC concerning in vitro diagnostic medical devices. Official Journal of The European Communities. Luxembourg, 1998.
  • [9] Council. E. COUNCIL DIRECTIVE 90/385/EEC concerning active implantable medical devices. Official Journal of The European Communities. Luxembourg, 1990.
  • [10] Erkan B. Product and market diversification in Turkey’s foreign trade. IJAME, 2018.
  • [11] Lévay PZ, Drossinos Y, Thiel C. The effect of fiscal incentives on market penetration of electric vehicles: A pairwise comparison of total cost of ownership. Energy Policy 2017; 105: 524-533.
  • [12] Fronczek, M. Import penetration rate in view of a new concept of measuring foreign trade. Argumenta Oeconomica 2017; 38: 285-297.
  • [13] Brakman S, Van Marrewijk C. A closer look at revealed comparative advantage: Gross‐versus value‐added trade flows. Papers in Regional Science 2017; 96(1): 61-92.
  • [14] Gozgor G, Can M. Effects of the product diversification of exports on income at different stages of economic development. Eurasian Business Review 2016; 6(2): 215-235.
  • [15] Bangdiwala SI. Regression: simple linear. International journal of injury control and safety promotion 2018; 25(1): 113-115.
  • [16] Malegori C, Marques EJN, de Freitas ST, Pimentel MF, Pasquini C, Casiraghi E. Comparing the analytical performances of Micro-NIR and FT-NIR spectrometers in the evaluation of acerola fruit quality, using PLS and SVM regression algorithms. Talanta 2017; 165: 112-116.
  • [17] Dong X, Greven MC, Kundaje A, et al. Modeling gene expression using chromatin features in various cellular contexts. Genome biology 2012; 13(9): R53.
  • [18] Chai T, Draxler RR. Root mean square error (RMSE) or mean absolute error (MAE)?–Arguments against avoiding RMSE in the literature. Geoscientific model development. 2014; 7(3): 1247-1250.
  • [19] Gelman A, Goodrich B, Gabry J, Vehtari A. R-squared for Bayesian regression models. The American Statistician 2019; 73(3): 307-309.
Toplam 19 adet kaynakça vardır.

Ayrıntılar

Birincil Dil İngilizce
Konular Mühendislik
Bölüm Makaleler
Yazarlar

Tuncay Bayrak 0000-0001-6826-4350

Yayımlanma Tarihi 28 Aralık 2020
Yayımlandığı Sayı Yıl 2020 Cilt: 21 Sayı: 4

Kaynak Göster

AMA Bayrak T. A MACHINE-LEARNING-BASED MODEL FOR FORECASTING MEDICAL DEVICE FOREIGN TRADE. Eskişehir Technical University Journal of Science and Technology A - Applied Sciences and Engineering. Aralık 2020;21(4):477-485. doi:10.18038/estubtda.803546