Predictive Modeling and Machine Learning Approaches in Agrochemical Exposure and Health Risk Assessment
Eric Oppong *
Department of Animal Science, Iowa State University, Iowa, United States.
*Author to whom correspondence should be addressed.
Abstract
The increasing global reliance on agrochemicals, including pesticides, herbicides, and fertilizers, has substantially enhanced agricultural productivity while intensifying concerns regarding environmental contamination and human health risks. Human exposure occurs through occupational, environmental, and dietary pathways and is associated with acute toxicity as well as chronic conditions such as cancer, neurodegenerative disorders, and endocrine disruption. This narrative review systematically synthesized recent peer-reviewed literature on predictive modeling and machine learning (ML) applications in agrochemical exposure and health risk assessment, drawing from studies focused on environmental monitoring, biomonitoring, geospatial analysis, and health outcome prediction. Evidence was selected based on relevance to ML-driven risk assessment frameworks, model applicability, and comparative analytical value. Findings indicate that ML approaches, including Random Forest, Support Vector Machines, Artificial Neural Networks, and Gradient Boosting, consistently outperform many conventional deterministic and probabilistic models in handling nonlinear interactions, integrating high-dimensional datasets, and improving predictive accuracy across spatial–temporal and biomonitoring contexts. Ensemble and deep learning models demonstrated particularly strong performance for exposure estimation and disease risk stratification, although challenges remain regarding data quality, interpretability, overfitting, and ethical governance. The review highlights emerging opportunities in explainable artificial intelligence, wearable sensor integration, and real-time surveillance systems to enhance model transparency and public health applicability. Overall, ML-driven predictive frameworks represent a transformative advancement in agrochemical risk assessment and offer significant potential to strengthen evidence-based regulatory policies, targeted interventions, and sustainable environmental health decision-making.
Keywords: Machine learning, agrochemical exposure, risk assessment, environmental health, predictive modeling, pesticides, artificial intelligence, biomonitoring