The prevention of respiratory diseases caused by high air pollution rates is an important issue in big cities, where industrialization and overpopulation cause an increase in allergenic particles that aggravate the disease of allergic rhinitis and asthma, especially in childhood. The problem lies in the disinformation of the population about air quality and the preventive measures to be taken in order to avoid deterioration in health. In this paper, data are monitored by a sensor network that registers the most abundant allergen, called PM 10 , for the city of León, Guanajuato. An artificial neural network (ANN) with a supervised Backpropagation training is used to predict future data until a minimum error is reached. The proposed methodology generates efficient results, measured in the error of the solutions and in execution time.
Resumen. Muchas muertes en el mundo suceden a consecuencia de enfermedades cardiovasculares. El método propuesto combina metaheuristícas-Algoritmos Genéticos (AG)-, y los clasificadores KNN y Naive Bayes. Las pruebas se realizaron a través de una base de datos del Massachusetts Institute of Technology-Beth Israel Hospital (MIT-BIH) [1]. Las metaheurísticas se implementan para mejorar el rendimiento de los clasificadores. Los resultados experimentales demuestran que se logra hasta un 94 % de precisión en la clasificación.
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