IDEAS home Printed from https://ideas.repec.org/a/gam/jsusta/v12y2020i15p6143-d392334.html
   My bibliography  Save this article

Hybrid Deep Learning Algorithm with Open Innovation Perspective: A Prediction Model of Asthmatic Occurrence

Author

Listed:
  • Min-Seung Kim

    (Department of Computer and Telecommunications Engineering, College of Science and Technology, Yonsei University, Wonju 26493, Korea)

  • Jeong-Hee Lee

    (Graduate School of Computer Science, College of Science and Technology, Yonsei University, Wonju 26493, Korea)

  • Yong-Ju Jang

    (Department of Computer and Telecommunications Engineering, College of Science and Technology, Yonsei University, Wonju 26493, Korea)

  • Chan-Ho Lee

    (Department of Computer and Telecommunications Engineering, College of Science and Technology, Yonsei University, Wonju 26493, Korea)

  • Ji-Hye Choi

    (Department of Computer and Telecommunications Engineering, College of Science and Technology, Yonsei University, Wonju 26493, Korea)

  • Tae-Eung Sung

    (Department of Computer and Telecommunications Engineering, College of Science and Technology, Yonsei University, Wonju 26493, Korea)

Abstract

Due to recent advancements in industrialization, climate change and overpopulation, air pollution has become an issue of global concern and air quality is being highlighted as a social issue. Public interest and concern over respiratory health are increasing in terms of a high reliability of a healthy life or the social sustainability of human beings. Air pollution can have various adverse or deleterious effects on human health. Respiratory diseases such as asthma, the subject of this study, are especially regarded as ‘directly affected’ by air pollution. Since such pollution is derived from the combined effects of atmospheric pollutants and meteorological environmental factors, and it is not easy to estimate its influence on feasible respiratory diseases in various atmospheric environments. Previous studies have used clinical and cohort data based on relatively a small number of samples to determine how atmospheric pollutants affect diseases such as asthma. This has significant limitations in that each sample of the collections is likely to produce inconsistent results and it is difficult to attempt the experiments and studies other than by those in the medical profession. This study mainly focuses on predicting the actual asthmatic occurrence while utilizing and analyzing the data on both the atmospheric and meteorological environment officially released by the government. We used one of the advanced analytic models, often referred to as the vector autoregressive model (VAR), which traditionally has an advantage in multivariate time-series analysis to verify that each variable has a significant causal effect on the asthmatic occurrence. Next, the VAR model was applied to a deep learning algorithm to find a prediction model optimized for the prediction of asthmatic occurrence. The average error rate of the hybrid deep neural network (DNN) model was numerically verified to be about 8.17%, indicating better performance than other time-series algorithms. The proposed model can help streamline the national health and medical insurance system and health budget management in South Korea much more effectively. It can also provide efficiency in the deployment and management of the supply and demand of medical personnel in hospitals. In addition, it can contribute to the promotion of national health, enabling advance alerts of the risk of outbreaks by the atmospheric environment for chronic asthma patients. Furthermore, the theoretical methodologies, experimental results and implications of this study will be able to contribute to our current issues of global change and development in that the meteorological and environmental data-driven, deep-learning prediction model proposed hereby would put forward a macroscopic directionality which leads to sustainable public health and sustainability science.

Suggested Citation

  • Min-Seung Kim & Jeong-Hee Lee & Yong-Ju Jang & Chan-Ho Lee & Ji-Hye Choi & Tae-Eung Sung, 2020. "Hybrid Deep Learning Algorithm with Open Innovation Perspective: A Prediction Model of Asthmatic Occurrence," Sustainability, MDPI, vol. 12(15), pages 1-21, July.
  • Handle: RePEc:gam:jsusta:v:12:y:2020:i:15:p:6143-:d:392334
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/2071-1050/12/15/6143/pdf
    Download Restriction: no

    File URL: https://www.mdpi.com/2071-1050/12/15/6143/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Hongze Li & Bingkang Li & Hao Lu, 2017. "Carbon Dioxide Emissions, Economic Growth, and Selected Types of Fossil Energy Consumption in China: Empirical Evidence from 1965 to 2015," Sustainability, MDPI, vol. 9(5), pages 1-14, April.
    2. Sims, Christopher A, 1980. "Macroeconomics and Reality," Econometrica, Econometric Society, vol. 48(1), pages 1-48, January.
    3. Ming-Sun Horng & Yung-Wang Chang & Ting-Yi Wu, 2012. "Does insurance demand or financial development promote economic growth? Evidence from Taiwan," Applied Economics Letters, Taylor & Francis Journals, vol. 19(2), pages 105-111, February.
    4. JinHyo Joseph Yun & Xiaofei Zhao & KwangHo Jung & Tan Yigitcanlar, 2020. "The Culture for Open Innovation Dynamics," Sustainability, MDPI, vol. 12(12), pages 1-21, June.
    5. Young-Hwan Lee & Hyung-Kee Kim, 2019. "Financial Support and University Performance in Korean Universities: A Panel Data Approach," Sustainability, MDPI, vol. 11(20), pages 1-18, October.
    6. JinHyo Joseph Yun & Dooseok Lee & Heungju Ahn & Kyungbae Park & Tan Yigitcanlar, 2016. "Not Deep Learning but Autonomous Learning of Open Innovation for Sustainable Artificial Intelligence," Sustainability, MDPI, vol. 8(8), pages 1-20, August.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Min-Seung Kim & Chan-Ho Lee & Ji-Hye Choi & Yong-Ju Jang & Jeong-Hee Lee & Jaesik Lee & Tae-Eung Sung, 2021. "A Study on Intelligent Technology Valuation System: Introduction of KIBO Patent Appraisal System II," Sustainability, MDPI, vol. 13(22), pages 1-25, November.

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Hansen, Lars Peter, 2013. "Uncertainty Outside and Inside Economic Models," Nobel Prize in Economics documents 2013-7, Nobel Prize Committee.
    2. Alfonso Mendoza-Velázquez & Luis Carlos Ortuño-Barba & Luis David Conde-Cortés, 2022. "Corporate governance and firm performance in hybrid model countries," Review of Accounting and Finance, Emerald Group Publishing Limited, vol. 21(1), pages 32-58, February.
    3. Punzi, Maria Teresa, 2016. "Financial cycles and co-movements between the real economy, finance and asset price dynamics in large-scale crises," FinMaP-Working Papers 61, Collaborative EU Project FinMaP - Financial Distortions and Macroeconomic Performance: Expectations, Constraints and Interaction of Agents.
    4. Antonella Cavallo & Antonio Ribba, 2017. "Measuring the Effects of Oil Price and Euro-area Shocks on CEECs Business Cycles," Department of Economics 0111, University of Modena and Reggio E., Faculty of Economics "Marco Biagi".
    5. Evans, Charles L. & Marshall, David A., 2007. "Economic determinants of the nominal treasury yield curve," Journal of Monetary Economics, Elsevier, vol. 54(7), pages 1986-2003, October.
    6. Nautz, Dieter & Strohsal, Till & Netšunajev, Aleksei, 2019. "The Anchoring Of Inflation Expectations In The Short And In The Long Run," Macroeconomic Dynamics, Cambridge University Press, vol. 23(5), pages 1959-1977, July.
    7. KAMKOUM, Arnaud Cedric, 2023. "The Federal Reserve’s Response to the Global Financial Crisis and its Effects: An Interrupted Time-Series Analysis of the Impact of its Quantitative Easing Programs," Thesis Commons d7pvg, Center for Open Science.
    8. Bierens, H.J. & Broersma, L., 1991. "The relation between unemployment and interest rate : some international evidence," Serie Research Memoranda 0112, VU University Amsterdam, Faculty of Economics, Business Administration and Econometrics.
    9. Xu, Haifeng & Hamori, Shigeyuki, 2012. "Dynamic linkages of stock prices between the BRICs and the United States: Effects of the 2008–09 financial crisis," Journal of Asian Economics, Elsevier, vol. 23(4), pages 344-352.
    10. Viviane Luporini, 1999. "Federal domestic debt in Brazil: 1981-1996," Textos para Discussão Cedeplar-UFMG td128, Cedeplar, Universidade Federal de Minas Gerais.
    11. Zheng, Li & Abbasi, Kashif Raza & Salem, Sultan & Irfan, Muhammad & Alvarado, Rafael & Lv, Kangjuan, 2022. "How technological innovation and institutional quality affect sectoral energy consumption in Pakistan? Fresh policy insights from novel econometric approach," Technological Forecasting and Social Change, Elsevier, vol. 183(C).
    12. Huang, Shupei & An, Haizhong & Gao, Xiangyun & Sun, Xiaoqi, 2017. "Do oil price asymmetric effects on the stock market persist in multiple time horizons?," Applied Energy, Elsevier, vol. 185(P2), pages 1799-1808.
    13. Zia-Ur- Rahman, 2019. "Influence of Excessive Expenditure of the Government in Perspective of Interest Rate and Money Circulation Which in Turn Affects the Growing Process in Pakistan," Asian Journal of Economics and Empirical Research, Asian Online Journal Publishing Group, vol. 6(2), pages 120-129.
    14. Pérez-Forero, Fernando, 2016. "Comparación de la transmisión de choques de política monetaria en América Latina: Un panel VAR jerárquico," Revista Estudios Económicos, Banco Central de Reserva del Perú, issue 32, pages 10-34.
    15. Francisco de Castro, 2006. "The macroeconomic effects of fiscal policy in Spain," Applied Economics, Taylor & Francis Journals, vol. 38(8), pages 913-924.
    16. Sun, Yanpeng & Song, Yuru & Long, Chi & Qin, Meng & Lobonţ, Oana-Ramona, 2023. "How to improve global environmental governance? Lessons learned from climate risk and climate policy uncertainty," Economic Analysis and Policy, Elsevier, vol. 80(C), pages 1666-1676.
    17. David Card, 2022. "Design-Based Research in Empirical Microeconomics," American Economic Review, American Economic Association, vol. 112(6), pages 1773-1781, June.
    18. António Afonso & Yasfir Ibraimo, 2020. "The macroeconomic effects of public debt: an empirical analysis of Mozambique," Applied Economics, Taylor & Francis Journals, vol. 52(2), pages 212-226, January.
    19. Ratti, Ronald A. & Vespignani, Joaquin L., 2013. "Crude oil prices and liquidity, the BRIC and G3 countries," Energy Economics, Elsevier, vol. 39(C), pages 28-38.
    20. Guochang Wang & Wai Keung Li & Ke Zhu, 2018. "New HSIC-based tests for independence between two stationary multivariate time series," Papers 1804.09866, arXiv.org.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:gam:jsusta:v:12:y:2020:i:15:p:6143-:d:392334. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: MDPI Indexing Manager (email available below). General contact details of provider: https://www.mdpi.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.