A New Data Science Model With Supervised Learning and its Application on Pesticide Poisoning Diagnosis in Rural Workers

Abstract

In a Data Science project, it is essential to determine the relevance of the data and identify patterns that contribute to decision-making based on domain-specific knowledge. Furthermore, a clear definition of methodologies and creation of documentation to guide a project's development from inception to completion are essential elements. This study presents a Data Science model designed to guide the process, covering data collection through training with the aim of facilitating knowledge discovery. Motivated by deficiencies in existing Data Science methodologies, particularly the lack of practical step-by-step guidance on how to prepare data to reach the production phase. Named ''Data Refinement Cycle with Supervised Machine Learning (DRC-SML)'', the proposed model was developed based on the emerging needs of a Data Sciense project aimed at assisting healthcare professionals in diagnosing pesticide poisoning among rural workers. The dataset used in this project resulted from scientific research in which 1027 samples were collected, containing data related to toxicity biomarkers and clinical analyses. We achieved an accuracy of 99.61% with only 27 rules for determining the diagnosis. The results optimized healthcare practices and improved quality of life in rural areas. The project outcomes demonstrated the success of the proposed model.