A model of clinical and environmental risk based on artificial intelligence

The exposome concept emphasizes the importance of taking into account the cumulative effects on the body during life, including environmental pollutants. The influence of these factors may increase the risk of developing chronic diseases. Objective. Development of a clinical and environmental risk assessment model that allows predicting the level of sensitivity to chemicals based on measured biomarkers and the assessment of the Quick Environmental Exposure and Sensitivity Inventory (QEESI) index. Material and methods. The study included 71 patients (32 men and 39 women) aged 18–65 years. The level of chemical sensitivity exceeded 20 points on the QEESI scale. The data was analyzed using Python and statistical libraries. A machine learning model (Random Forest Regressor) was created to assess clinical and environmental risk. Results. Significant deviations of biomarkers (alanine aminotransferase, aspartate aminotransferase, low-density lipoproteins) from the normal distribution required the use of mathematical transformations. The model showed good predictive abilities, despite the high values of MAE and RMSE. The cubic and quadratic shape of bilirubin and the scale of symptoms were the most significant factors. Conclusions. The developed model based on the Random Forest Regressor algorithm has shown high accuracy in predicting clinical and environmental risk. Optimization of hyperparameters, preprocessing of data using mathematical transformations (logarithmic, square, cubic), the use of the feature_importances_ attribute allowed a deeper understanding of the impact of environmental factors on health.