Utilizing machine learning to classify persistent organic pollutants in the serum of pregnant women: a predictive modeling approach

• Published in: Environmental science and pollution research international Vol. 31; no. 40; pp. 52980 - 52995
• Main Authors: Mahfouz, Maya, Mahfouz, Yara, Harmouche-Karaki, Mireille, Matta, Joseph, Younes, Hassan, Helou, Khalil, Finan, Ramzi, Abi-Tayeh, Georges, Meslimani, Mohamad, Moussa, Ghada, Chahrour, Nada, Osseiran, Camille, Skaiki, Farouk, Narbonne, Jean-François
• Format: Journal Article
• Language: English
• Published: Berlin/Heidelberg Springer Berlin Heidelberg 01.08.2024; Springer Nature B.V; Springer Verlag
• Subjects: Adult; Algorithms; Aquatic Pollution; Atmospheric Protection/Air Quality Control/Air Pollution; Biomonitoring; blood serum; Congeners; Dibenzofuran; Earth and Environmental Science; Ecotoxicology; Environment; Environmental Chemistry; Environmental Health; environmental monitoring; Environmental Monitoring - methods; Female; Humans; Hydrocarbons, Chlorinated - blood; Impact analysis; Learning algorithms; Life Sciences; Machine Learning; Neural networks; Organic compounds; Organochlorine pesticides; PCB; Persistent organic pollutants; Persistent Organic Pollutants - blood; Pesticides - blood; Pollutants; Polychlorinated biphenyls; Polychlorinated Biphenyls - blood; polychlorinated dibenzodioxins; Polychlorinated dibenzofurans; prediction; Prediction models; Pregnancy; Pregnant Women; Research Article; Statistical analysis; Statistical models; Subpopulations; Support vector machines; Waste Water Technology; Water Management; Water Pollution Control; Pregnancy; Model performance; Environmental pollution; Prediction model; Discriminatory power; Sociodemographic criteria; Public health
• ISSN: 1614-7499; 0944-1344; 1614-7499
• DOI: 10.1007/s11356-024-34684-x

Polychlorinated biphenyls (PCBs), organochlorine pesticides (OCPs), polychlorinated dibenzo-p-dioxins and polychlorinated dibenzofurans (PCDD/Fs), and per- and poly-fluoroalkyl substances (PFAS) are persistent organic pollutants (POPs) that remain detrimental to critical subpopulations, namely pregnant women. Required tests for biomonitoring are quite expensive. Moreover, statistical models aiming to discover the relationships between pollutants levels and human characteristics have their limitations. Therefore, the objective of this study is to use machine learning predictive models to further examine the pollutants’ predictors, while comparing them. Levels of 33 congeners were measured in the serum of 269 pregnant women, from whom data was collected regarding sociodemographic, dietary, environmental, and anthropometric characteristics. Several machine learning algorithms were compared using “Python” for each pollutant: support vector machine (SVM), random forest, XGBoost, and neural networks. The aforementioned characteristics were included in the model as features. Prediction, accuracy, precision, recall, F1-score, area under the ROC curve (AUC), sensitivity, and specificity were retrieved to compare the models between them and among pollutants. The highest performing model for all pollutants was Random Forest. Results showed a moderate to acceptable performance and discriminative power among all POPs, with OCPs’ model performing slightly better than all other models. Top related features for each model were also presented using SHAP analysis, detailing the predictors’ negative or positive impact on the model. In conclusion, developing such a tool is of major importance in a context of limited financial and research resources. Nevertheless, machine learning models should always be interpreted with caution by exploring all evaluation metrics.