Integration of the Wang & Mendel algorithm into the application of Fuzzy expert systems to intelligent clinical decision support systems
| Autor: | Manuel Casal-Guisande, Jorge Cerqueiro-Pequeño, José-Benito Bouza-Rodríguez, Alberto Comesaña-Campos |
|---|---|
| Jazyk: | angličtina |
| Rok vydání: | 2023 |
| Předmět: |
design
machine learning expert systems fuzzy logic automatic rule generation information fusion intelligent system decision-making Wang–Mendel General Mathematics 1203.04 Inteligencia Artificial Computer Science (miscellaneous) 1203.20 Sistemas de Control Medico Engineering (miscellaneous) 3212 Salud Publica |
| Zdroj: | Mathematics; Volume 11; Issue 11; Pages: 2469 |
| Popis: | Intelligent systems are increasingly present in various areas of society, gaining special relevance in healthcare environments. In this work, grounded on previous developments proposed by the authors, the evolution and improvement of a novel intelligent system focused on the diagnosis of obstructive sleep apnea (OSA) is addressed. To do this, starting from two packages of information of a heterogeneous nature, and deploying a set of Machine Learning classification algorithms, focused each of them on different levels of the apnea-hypopnea index (AHI), as well as a set of cascaded expert systems, it is possible to obtain a series of risk metric pairs (Statistical Risk, Symbolic Risk) for each AHI level, understood as a warning forecast associated with the early detection of the pathology. The risk indicators of each pair are subsequently aggregated by means of a symbolic inference system, whose knowledge base was determined using the automatic rule generation approach proposed by Wang and Mendel. At the output of each inference system, a risk indicator is obtained for each AHI level, the Apnea Risk, whose interpretation makes it possible to discriminate between patients who could suffer OSA and those who do not, as well as to generate the appropriate recommendations. For the initial tests of the system, the design and development of a specific software artifact was carried out using data from 4,400 patients from the Álvaro Cunqueiro Hospital in Vigo, obtaining when applied to the test set AUC values within the range 0.74–0.88, which invites optimism and points to the benefits expected from the architecture of the proposed intelligent system. |
| Databáze: | OpenAIRE |
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