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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

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          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.

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                Author and article information

                Journal
                Int J Environ Res Public Health
                Int J Environ Res Public Health
                ijerph
                International Journal of Environmental Research and Public Health
                MDPI
                1661-7827
                1660-4601
                31 December 2020
                January 2021
                : 18
                : 1
                : 269
                Affiliations
                Department of Design in Engineering, University of Vigo, 36208 Vigo, Spain; jcerquei@ 123456uvigo.es (J.C.-P.); jbouza@ 123456uvigo.es (J.-B.B.-R.)
                Author notes
                Author information
                https://orcid.org/0000-0001-8617-595X
                https://orcid.org/0000-0002-3409-8996
                https://orcid.org/0000-0003-1494-8145
                https://orcid.org/0000-0002-1235-7264
                Article
                ijerph-18-00269
                10.3390/ijerph18010269
                7795946
                33396542
                52e5b29d-2d58-43ba-8fd2-31f3972028bd
                © 2020 by the authors.

                Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license ( http://creativecommons.org/licenses/by/4.0/).

                History
                : 16 November 2020
                : 29 December 2020
                Categories
                Article

                Public health
                radon,expert systems,decision support systems,regression tree,risk,design science research

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