title: Artificial neural networks as a useful tool to predict the risk level of Betula pollen in the air.
abstract: An increasing percentage of the European population suffers from allergies to pollen. The study of the evolution of air pollen concentration supplies prior knowledge of the levels of pollen in the air, which can be useful for the prevention and treatment of allergic symptoms, and the management of medical resources. The symptoms of Betula pollinosis can be associated with certain levels of pollen in the air. The aim of this study was to predict the risk of the concentration of pollen exceeding a given level, using previous pollen and meteorological information, by applying neural network techniques. Neural networks are a widespread statistical tool useful for the study of problems associated with complex or poorly understood phenomena. The binary response variable associated with each level requires a careful selection of the neural network and the error function associated with the learning algorithm used during the training phase. The performance of the neural network with the validation set showed that the risk of the pollen level exceeding a certain threshold can be successfully forecasted using artificial neural networks. This prediction tool may be implemented to create an automatic system that forecasts the risk of suffering allergic symptoms.
authors.affiliation: Department of Statistics and Operation Research, Universidade de Santiago de Compostela, Campus Sur. 15782, Santiago de Compostela A Coruña, Spain. mariacmendez@hotmail.com
authors.forename: M
authors.initials: M
authors.lastname: Castellano-Méndez
authors.forename: M J
authors.initials: MJ
authors.lastname: Aira
authors.forename: I
authors.initials: I
authors.lastname: Iglesias
authors.forename: V
authors.initials: V
authors.lastname: Jato
authors.forename: W
authors.initials: W
authors.lastname: González-Manteiga
mesh_terms: D000393:Air Pollutants / Q000032:analysis*; D029662:Betula; D005060:Europe; D005544:Forecasting; D006801:Humans; D006967:Hypersensitivity / Q000209:etiology; D016571:Neural Networks, Computer; D011058:Pollen; D018570:Risk Assessment
chemical_list: D000393:Air Pollutants