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. 2020 Dec 31;18(1):269.
doi: 10.3390/ijerph18010269.

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

Jorge Cerqueiro-Pequeño  1 , Alberto Comesaña-Campos  1 , Manuel Casal-Guisande  1 , José-Benito Bouza-Rodríguez  1
Affiliations

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

Jorge Cerqueiro-Pequeño et al. Int J Environ Res Public Health. .

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.

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

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Conflict of interest statement

The authors declare no conflict of interest.

Figures

Figure 1
Figure 1
Flowchart of the initial methodology.
Figure 2
Figure 2
Example of a recommendation diagram.
Figure 3
Figure 3
Inference system used for the Correction Factor. The steps where the correlation factor is calculated in order to modify the rules of the inference system are indicated with a *.
Figure 4
Figure 4
Inference System for the Radon Risk.
Figure 5
Figure 5
Data header.
Figure 6
Figure 6
Dashboard for the visualization of the variables.
Figure 7
Figure 7
Dashboard for the calculation of the correlation coefficients.
Figure 8
Figure 8
Dashboard for the definition of the sigmoidal function.
Figure 9
Figure 9
Dashboard for inference systems 1 and 2.
Figure 10
Figure 10
Spider-web diagram.
See this image and copyright information in PMC

References

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