Estimating Breathing Levels of Asthma Patients with Artificial Intelligence Methods

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dc.contributor.authorYüksel A.S.
dc.contributor.authorTan F.G.
dc.date.accessioned2024-07-22T08:06:47Z
dc.date.available2024-07-22T08:06:47Z
dc.date.issued2021
dc.description.abstractPollen contains highly allergic proteins. One of the major causes of allergic diseases is the pollen in the air we breathe. Asthma patients are known to show allergic reaction to pollens. Therefore, they need to be more careful and avoid the factors that trigger asthma. In this study, the first step was taken to develop an artificial-intelligence-based decision support system to improve the quality of life of asthma patients. Finding the ratio of pollen in nature is a long process, and it is measured by using different instruments and hours of calculations in laboratories. In this study, Adaptive Network-Based Fuzzy Extraction System (ANFIS) and normalization methods were applied to meteorological data to estimate the breathing levels of asthma patients according to the amount of pollen in the air. The data were tested via the Artificial Neural Networks method, and it was found that the model produced better results that are very close to real values when compared to the results in ANFIS. © 2021, The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Switzerland AG.
dc.identifier.DOI-ID10.1007/978-3-030-51156-2_23
dc.identifier.issn21945357
dc.identifier.urihttp://akademikarsiv.cbu.edu.tr:4000/handle/123456789/13692
dc.language.isoEnglish
dc.publisherSpringer
dc.subjectDecision support systems
dc.subjectFuzzy inference
dc.subjectFuzzy neural networks
dc.subjectFuzzy systems
dc.subjectMeteorology
dc.subjectAdaptive networks
dc.subjectAllergic disease
dc.subjectAllergic reactions
dc.subjectArtificial intelligence methods
dc.subjectFuzzy extractions
dc.subjectMeteorological data
dc.subjectNormalization methods
dc.subjectQuality of life
dc.subjectDiseases
dc.titleEstimating Breathing Levels of Asthma Patients with Artificial Intelligence Methods
dc.typeConference paper

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