The Application of Artificial Neural Network in Medical Meteorology

Abstract

At present, artificial neural network is widely used in many fields, but almost not used in medical meteorology. In this paper, firstly, on the basis of statistical analysis, selection of main meteorological factors remarkably affecting hypertension is conducted for Yinchuan area .The main objective is to discuss the meteorological factors affecting the incidence of a disease and set up the weekly forecasting model. The factors, including average humidity, temperature swing of 48hous, daily temperature range and air pressure, as input variables, are used for studying and training of multilevel feed-forward neural network BP algorithm and an ANN hypertension model is developed for forecasting this disease. Results are follows: The ANN model structure is 4-14-1, that is, 4 input notes, 14 hidden notes and 1 output note. The training precision is 0.005 and the final error is 0.0048992 after 46 training steps. The simulative rate of ANN model and statistical model of same level are 62.4% and 47.7%, respectively. The forecasting rate of ANN model and statistical model of same level are 58.2% and 50.5%, respectively. The MAPE, MSE and MAE of ANN model are 25.2%, 21.0% and 16.2%, respectively, which are much smaller than statistical model. The method is easy to be finished by smaller error and higher ability on historical simulation and independent prediction, which provides a new method for forecasting the incidence of a disease.

This work was supported by the Project of National Natural Science Foundation of China under Grant No.40905064, the Key Projects in the National Science & Technology Program (2008BAC40B04) and Interdisciplinary Innovation Research Fund For Young Scholars, Lanzhou University (LZUJC2007014).