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Design and Development of a New Methodology Based on Expert Systems Applied to the Prevention of Indoor Radon Gas Exposition Risks

Author

Listed:
  • Jorge Cerqueiro-Pequeño

    (Department of Design in Engineering, University of Vigo, 36208 Vigo, Spain)

  • Alberto Comesaña-Campos

    (Department of Design in Engineering, University of Vigo, 36208 Vigo, Spain)

  • Manuel Casal-Guisande

    (Department of Design in Engineering, University of Vigo, 36208 Vigo, Spain)

  • José-Benito Bouza-Rodríguez

    (Department of Design in Engineering, University of Vigo, 36208 Vigo, Spain)

Abstract

Exposure to high concentration levels of radon gas constitutes a major health hazard, being nowadays the second-leading cause of lung cancer after smoking. Facing this situation, the last years have seen a clear trend towards the search for methodologies that allow an efficient prevention of the potential risks derived from the presence of harmful radon gas concentration levels in buildings. With that, it is intended to establish preventive and corrective actions that might help to reduce the impact of radon exposure on people, especially in places where workers and external users must stay for long periods of time, as it may be the case of healthcare buildings. In this paper, a new methodology is developed and applied to the prevention of the risks derived from the exposure to radon gas in indoor spaces. Such methodology is grounded in the concurrent use of expert systems and regression trees that allows producing a diagram with recommendations associated to the exposure risk. The presented methodology has been implemented by means of a software application that supports the definition of the expert systems and the regression algorithm. Finally, after proving its applicability with a case study and discussing its contributions, it may be claimed that the benefits of the new methodology might lead on to an innovation in this field of study.

Suggested Citation

  • Jorge Cerqueiro-Pequeño & Alberto Comesaña-Campos & Manuel Casal-Guisande & José-Benito Bouza-Rodríguez, 2020. "Design and Development of a New Methodology Based on Expert Systems Applied to the Prevention of Indoor Radon Gas Exposition Risks," IJERPH, MDPI, vol. 18(1), pages 1-32, December.
    • Handle: RePEc:gam:jijerp:v:18:y:2020:i:1:p:269-:d:473428
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    References listed on IDEAS

    as
    1. Lantz, P.M. & Mendez, D. & Philbert, M.A., 2013. "Radon, smoking, and lung cancer: The need to refocus radon control policy," American Journal of Public Health, American Public Health Association, vol. 103(3), pages 443-447.
    2. Alberto Comesaña-Campos & Manuel Casal-Guisande & Jorge Cerqueiro-Pequeño & José-Benito Bouza-Rodríguez, 2020. "A Methodology Based on Expert Systems for the Early Detection and Prevention of Hypoxemic Clinical Cases," IJERPH, MDPI, vol. 17(22), pages 1-31, November.
    3. Saaty, Thomas L., 1990. "How to make a decision: The analytic hierarchy process," European Journal of Operational Research, Elsevier, vol. 48(1), pages 9-26, September.
    4. Lin, Chin-Shien & Khan, Haider A. & Chang, Ruei-Yuan & Wang, Ying-Chieh, 2008. "A new approach to modeling early warning systems for currency crises: Can a machine-learning fuzzy expert system predict the currency crises effectively?," Journal of International Money and Finance, Elsevier, vol. 27(7), pages 1098-1121, November.
    5. Bonczek, Robert H. & Holsapple, Clyde W. & Whinston, Andrew B., 1981. "Foundations of Decision Support Systems," Elsevier Monographs, Elsevier, edition 1, number 9780121130503 edited by Schmidt, J. William.
    6. United Nations UN, 2015. "Transforming our World: the 2030 Agenda for Sustainable Development," Working Papers id:7559, eSocialSciences.
    7. Frada Burstein & Clyde Holsapple, 2008. "Handbook on Decision Support Systems 1," International Handbooks on Information Systems, Springer, number 978-3-540-48713-5, December.
    8. Frada Burstein & Clyde Holsapple, 2008. "Handbook on Decision Support Systems 2," International Handbooks on Information Systems, Springer, number 978-3-540-48716-6, December.
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    Citations

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    Cited by:

    1. Basile, Luigi Jesus & Carbonara, Nunzia & Pellegrino, Roberta & Panniello, Umberto, 2023. "Business intelligence in the healthcare industry: The utilization of a data-driven approach to support clinical decision making," Technovation, Elsevier, vol. 120(C).
    2. Manuel Casal-Guisande & Jorge Cerqueiro-Pequeño & José-Benito Bouza-Rodríguez & Alberto Comesaña-Campos, 2023. "Integration of the Wang & Mendel Algorithm into the Application of Fuzzy Expert Systems to Intelligent Clinical Decision Support Systems," Mathematics, MDPI, vol. 11(11), pages 1-33, May.
    3. Simona Mancini & Martins Vilnitis & Nataša Todorović & Jovana Nikolov & Michele Guida, 2022. "Experimental Studies to Test a Predictive Indoor Radon Model," IJERPH, MDPI, vol. 19(10), pages 1-8, May.
    4. Alberto Comesaña-Campos & Manuel Casal-Guisande & Jorge Cerqueiro-Pequeño & José-Benito Bouza-Rodríguez, 2020. "A Methodology Based on Expert Systems for the Early Detection and Prevention of Hypoxemic Clinical Cases," IJERPH, MDPI, vol. 17(22), pages 1-31, November.
    5. Manuel Casal-Guisande & María Torres-Durán & Mar Mosteiro-Añón & Jorge Cerqueiro-Pequeño & José-Benito Bouza-Rodríguez & Alberto Fernández-Villar & Alberto Comesaña-Campos, 2023. "Design and Conceptual Proposal of an Intelligent Clinical Decision Support System for the Diagnosis of Suspicious Obstructive Sleep Apnea Patients from Health Profile," IJERPH, MDPI, vol. 20(4), pages 1-31, February.

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