Abstract
The concept of life quality is a subjective feeling that only patient is able to define. The absence of disease is one of the determinants of well-being and life quality. Generally, self-perceived health status is measured by specific or generic questionnaires. The health information collected in the questionnaires is usually expressed by numerical values although the indicators evaluated are qualitative and subjective. This contribution proposes a linguistic approach where health information provided by patients is modelled by means of linguistic information in order to manage the uncertainty and subjectivity of such assessments. The contribution introduces a new model for measuring self-perceived health that can manage linguistic information and computes a final linguistic evaluation for each patient, applying an effective aggregation operator. A real case study is also presented to show the usefulness and effectiveness of the proposed model in the case of diabetes disease.
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Notes
- 1.
The 2-tuple OWA operator needs a weighting vector that can be determined by different methods based on weight generating functions. In this step equal weight for each issue are considered for aggregating self-perceptions.
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Acknowledgements
The authors thank Editors of Lecture Notes in Artificial Intelligence, three anonymous referees and Luis Martínez for their valuable comments and suggestions. The authors acknowledge financial support by the Spanish Ministerio de Ciencia e Innovación under Projects Project ECO2012–32178 (R. de Andrés Calle and T. González-Arteaga), CGL2008-06003-C03-03/CLI (R. de Andrés Calle) and ECO2012–31933 (J.C.R. Alcantud).
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de Andrés Calle, R., González-Arteaga, T., Alcantud, J.C.R., Peral, M. (2015). A Linguistic Approach for Self-Perceived Health State: A Real Study for Diabetes Disease. In: Puerta, J., et al. Advances in Artificial Intelligence. CAEPIA 2015. Lecture Notes in Computer Science(), vol 9422. Springer, Cham. https://doi.org/10.1007/978-3-319-24598-0_7
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