Innovative Data Science Model for Analysis on Pesticide Poisoning Using Supervised Learning

In a Data Science project, assessing data relevance and identifying patterns that support decision-making based on domain-specific knowledge are critical. Additionally, establishing clear methodologies and comprehensive documentation is essential to guide the project from its initial stages to completion. This study introduces a structured Data Science model, covering the entire process from data collection to model training, aimed at enhancing knowledge discovery. The motivation behind this model stems from limitations in existing Data Science methodologies, particularly the absence of practical, step-by-step guidance for data preparation and deployment. The proposed model, called "Data Refinement Cycle with Supervised Machine Learning (DRC–SML)," was specifically designed to assist healthcare professionals in diagnosing pesticide poisoning among rural workers. The dataset for this project, based on scientific research, included 1027 samples containing toxicity biomarker data and clinical analyses. The model achieved an impressive 99.62% accuracy with only 28 decision rules, significantly improving healthcare practices and quality of life in rural areas. The results validate the effectiveness of the DRC–SML model, demonstrating its potential for enhancing predictive analytics in healthcare and other domains.