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Seven-days-ahead forecasting of childhood asthma admissions using artificial neural networks in Athens, Greece

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Artificial Neural Network (ANN) models were developed and applied in order to predict the total weekly number of Childhood Asthma Admission (CAA) at the greater Athens area (GAA) in Greece. Hourly meteorological data from the National Observatory of Athens and ambient air pollution data from seven different areas within the GAA for the period 2001–2004 were used. Asthma admissions for the same period were obtained from hospital registries of the three main Children's Hospitals of Athens. Three different ANN models were developed and trained in order to forecast the CAA for the subgroups of 0–4, 5–14-year olds, and for the whole study population. The results of this work have shown that ANNs could give an adequate forecast of the total weekly number of CAA in relation to the bioclimatic and air pollution conditions. The forecasted numbers are in very good agreement with the observed real total weekly numbers of CAA.
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Keywords: Athens; Greece; artificial neural networks; childhood asthma; forecasting

Document Type: Research Article

Affiliations: 1: Department of Mechanical Engineering,Technological Education Institute of Piraeus, Athens 2: 3rd Pediatric Department,Medical School, University of Athens, Athens 3: Laboratory of Climatology and Atmospheric Environment, Department of Geology and Geoenvironment,University of Athens, Athens 4: Laboratory of Environmental Technology,Electronic Computer Systems Engineering Department, Technological Education Institute of Piraeus, Athens, Greece 5: Respiratory Unit, Department of Paediatrics,University Hospital of Patras, Patras 6: General Department of Mathematics,Technological Education Institute of Piraeus, Athens, 7: Allergy-Pneumonology Department,Penteli Children's Hospital, Athens, Greece

Publication date: April 1, 2012

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