Relating personality traits and mercury exposure in miners with machine learning methods

Abstract

We use machine learning/data mining methods to analyse scientific data in the area of environmental epidemiology, i.e., the study of the influence of environmental factors on human health. In particular, the aim of this study was an evaluation of the impact of long-term past occupational exposure to elemental mercury vapour (Hg°) on the mental health, i.e., personality traits of ex-mercury miners. Personality traits were defined by the Eysenck Personality Questionnaire (EPQ) and Emotional States Questionnaire (ESQ), which produced scores for traits such as depression and negative self-concept. Statistical analyses were performed to determine if there are significant differences between the values of the scores for ex-miners and controls. For the psychological traits for which significant differences were found between exminers and non-miners, we performed regression analysis. The target variables were the personality trait scores, while the independent/explanatory variables were the indices of previous occupational exposure to Hg°, medical history and lifestyle habits and some biological indices of actual non-occupational exposure. Regression/model trees were used to perform the analyses and revealed many interesting findings, e.g., that alcohol consumption and mercury exposure increase the depression score.