Application of artificial neural network in medical geochemistry

For the evaluation of various adverse health effects of chemical elements occurring in the environment on humans, the comparison and linking of geochemical data (chemical composition of groundwater, soils, and dusts) with data on health status of population (so-called health indicators) play a key role. Geochemical and health data are predominantly nonlinear, and the use of standard statistical methods can lead to wrong conclusions. For linking such data, we find appropriate the use method of artificial neural networks (ANNs) which enable to eliminate data inhomogeneity and also potential data errors. Through method of ANNs, we are able to determine the order of influence of chemical elements on health indicators as well as to define limit values for the influential elements at which the health status of population is the most favourable (i.e. the lowest mortality, the highest life expectancy). For determination of dependence between the groundwater contents of chemical elements and health indicators, we recommend to create 200 ANNs. In further calculations performed for identification of order of influence of chemical elements as well as definition of limit values, we propose to work with median or mean values from calculated 200 ANNs. The ANN represents an appropriate method to be used for environmental and health data analysis in medical geochemistry.