Vol. 4 No. 4 (2025): October
RESEARCH ARTICLES

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

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  • M Venkata Ramana,
  • Y Mahesh,
  • A Keshava,
  • G Keerthi Priya,
  • R Sai Jyoshna,
  • S Nayeem
M Venkata Ramana
Department of AI and Data Science, Annamacharya Institute of Technology and Sciences, Kadapa, Andhra Pradesh, India.
Y Mahesh
Department of AI and Data Science, Annamacharya Institute of Technology and Sciences, Kadapa, Andhra Pradesh, India.
A Keshava
Department of AI and Data Science, Annamacharya Institute of Technology and Sciences, Kadapa, Andhra Pradesh, India.
G Keerthi Priya
Department of AI and Data Science, Annamacharya Institute of Technology and Sciences, Kadapa, Andhra Pradesh, India.
R Sai Jyoshna
Department of AI and Data Science, Annamacharya Institute of Technology and Sciences, Kadapa, Andhra Pradesh, India.
S Nayeem
Department of AI and Data Science, Annamacharya Institute of Technology and Sciences, Kadapa, Andhra Pradesh, India.
DOI: https://doi.org/10.5281/zenodo.15251412

Published 2025-04-20

Keywords

  • Data science,
  • decision support system,
  • machine learning,
  • pesticide poisoning diagnosis

How to Cite

M Venkata Ramana, Y Mahesh, A Keshava, G Keerthi Priya, R Sai Jyoshna, & S Nayeem. (2025). Innovative Data Science Model for Analysis on Pesticide Poisoning Using Supervised Learning. International Journal of Computational Learning & Intelligence, 4(4), 743–756. https://doi.org/10.5281/zenodo.15251412

Copyright (c) 2025 M Venkata Ramana, Y Mahesh, A Keshava, G Keerthi Priya, R Sai Jyoshna, S Nayeem

Creative Commons License

This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.

Abstract

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.

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