The usefulness of artificial neural networks in the evaluation of pulmonary efficiency and antioxidant capacity of welders

Abstract

Objective The aim of the study was to determine if artificial neural networks (ANNs) may be useful to analyse a complex and large set of data derived from smoking welders for the purpose of finding relationships between parameters describing respiratory system efficiency and antioxidant defence. Methods A group of 94 welders employed in a big metallurgic enterprise in Krakow, Poland (men only, aged 29–57 years, all active smokers) occupationally exposed to O 3 and NO x , were the subjects of this study. They underwent biochemical measurements including total antioxidant status (TAS) and the anti-oxidative defence enzymes superoxide dismutase (SOD) and catalase (CT); biominerals: Fe, Cu, Zn, Mg in blood serum and in hair; the concentrations of albumin, bilirubin, uric acid in blood. The determination of respiratory efficiency was based on a “flow-volume” curve and spirometry. The dependant variables for ANNs were: TAS, SOD, CT. Thirty-one subjects with normal values of all spirometric parameters were selected for the final analysis. Results Statistically valid relationship between TAS and albumin, Zn and Cu in blood and the two pulmonary parameters forced expiratory volume after 1 s (FEV 1) and middle expiratory flow of 25–75% of vital capacity (MEF 25/75) were found. Zn concentration almost linearly influenced TAS. For Cu a sigmoid curve was obtained. For albumin concentration as well as for FEV 1 a two-stage curve was observed. Conclusions ANNs are useful for the reduction of dimensionality and are suited to analyse a complex and relatively large set of parameters when it is unknown which of these are related. ANNs were useful for finding the relationship between the antioxidant defence and the respiratory system capacity in welders who smoke.