Abstract

Agricultural intensification has increasingly relied on the widespread application of agrochemicals, which have often dispersed beyond targeted farmlands through atmospheric drift. This environmental exposure has raised concerns regarding respiratory health among rural populations. Previous environmental surveillance systems have rarely integrated meteorological variables with clinical records that describe respiratory disease patterns. Therefore, the lack of an integrated analytical model has limited the ability to understand the association between agrochemical drift and rural respiratory illness. This study has proposed a Multivariate Agrochemical Drift Impact Mining Model (MADIMM) that which integrated weather attributes and rural clinical records to estimate the relationship between agrochemical dispersion and respiratory disease occurrence. The framework has utilized multivariate data mining techniques that which analyzed temperature, humidity, wind speed, precipitation, and seasonal spraying patterns together with hospital respiratory admission data. The preprocessing stage has included normalization, missing value imputation, and feature correlation filtering. Subsequently, the MADIMM classifier has applied ensemble learning that which combined Gradient Boosting and Random Forest models to extract environmental–clinical correlations. The experimental evaluation is showing that the proposed MADIMM framework achieves 96% accuracy, 95% precision, 94% recall, 95% F1 score, and 97% AUC in respiratory disease prediction. The model improves classification accuracy by approximately 10–13% compared with Random Forest, Support Vector Machine, and Gradient Boosting models. The environmental drift exposure modeling captures atmospheric dispersion patterns that which significantly improve the prediction of respiratory disease risk in rural agricultural regions.

Authors

M.K. Jayanthi Kannan¹, Mariam Safar Mohammed Alshahrani², Shree Nee Thirumalai Ramesh³
VIT Bhopal University, India¹, Digital Government Authority of KSA, Riyadh Province, Kingdom of Saudi Arabia ², Manipal University Medical College Malaysia, Malaysia³

Keywords

Agrochemical Drift, Respiratory Disease Prediction, Multivariate Data Mining, Environmental Health Analytics, Rural Healthcare Surveillance