Literature DB >> 33396542

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

Jorge Cerqueiro-Pequeño1, Alberto Comesaña-Campos1, Manuel Casal-Guisande1, José-Benito Bouza-Rodríguez1.   

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.

Entities:  

Keywords:  decision support systems; design science research; expert systems; radon; regression tree; risk

Year:  2020        PMID: 33396542     DOI: 10.3390/ijerph18010269

Source DB:  PubMed          Journal:  Int J Environ Res Public Health        ISSN: 1660-4601            Impact factor:   3.390


  2 in total

1.  Experimental Studies to Test a Predictive Indoor Radon Model.

Authors:  Simona Mancini; Martins Vilnitis; Nataša Todorović; Jovana Nikolov; Michele Guida
Journal:  Int J Environ Res Public Health       Date:  2022-05-16       Impact factor: 4.614

2.  Design and Development of an Intelligent Clinical Decision Support System Applied to the Evaluation of Breast Cancer Risk.

Authors:  Manuel Casal-Guisande; Alberto Comesaña-Campos; Inês Dutra; Jorge Cerqueiro-Pequeño; José-Benito Bouza-Rodríguez
Journal:  J Pers Med       Date:  2022-01-27
  2 in total

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