Abstract

Pesticide poisoning remains a significant health concern among rural workers worldwide due to their frequent exposure during agricultural activities. Traditional diagnostic methods often lack precision and timeliness, leading to delayed treatment and compromised health outcomes. This study proposes a novel data science model utilizing supervised learning techniques for the early detection and diagnosis of pesticide poisoning in rural workers. The model integrates machine learning algorithms trained on comprehensive datasets containing symptoms, exposure histories, and clinical outcomes associated with pesticide toxicity. By leveraging these datasets, the model aims to enhance diagnostic accuracy and provide timely interventions, thereby improving health management strategies in agricultural communities. This research contributes to advancing public health efforts by harnessing data-driven approaches to mitigate the impact of pesticide poisoning among vulnerable rural populations.