Predecting Adverse Drug Reactions with XGBoost a Pharmacovigilance Application

K. Jnana Sadhana, Veera Raghavan J., Bellamgubba Anoch, P. Kiran Sree, Raja Rao P. B. V., B. Satyanarayana Murthy

2025

Abstract

Adverse Drug Reactions (ADRs) pose a significant challenge to modern pharmacovigilance, leading to severe health implications, increased healthcare costs, and regulatory concerns. Traditional ADR detection methods rely heavily on manual pharmacovigilance and rule-based expert systems, which are slow, subjective, and limited in scalability.However, existing ML models either suffer from overfitting, lack of generalizability, or black-box limitations.This study proposes a novel XGBoost-based ADR prediction model that achieves 91% accuracy, outperforming traditional classifiers (Naïve Bayes, SVM, Random Forest) and deep learning models (Graph Neural Networks, Neural Collaborative Filtering, Deep Ensembles) found in literature. The proposed model uses feature engineering with SHAP explainability, class balancing techniques, and hyperparameter tuning to improve predictive performance. By leveraging Therapeutic Class, Action Class, and Chemical Properties, the model not only enhances accuracy but also ensures interpretability, making it suitable for real-world clinical decision systems.Experimental results demonstrate that the optimized XGBoost model achieves 91% accuracy, 92% precision, and 91% recall, making it a competitive alternative to deep learning-based ADR detection models.

Paper Citation

in Harvard Style

Sadhana K., J. V., Anoch B., Sree P., P. B. V. R. and Murthy B. (2025). Predecting Adverse Drug Reactions with XGBoost a Pharmacovigilance Application. In Proceedings of the 1st International Conference on Research and Development in Information, Communication, and Computing Technologies - Volume 1: ICRDICCT`25; ISBN 978-989-758-777-1, SciTePress, pages 713-720. DOI: 10.5220/0013871700004919

in Bibtex Style

@conference{icrdicct`2525,
author={K. Sadhana and Veera J. and Bellamgubba Anoch and P. Sree and Raja P. B. V. and B. Murthy},
title={Predecting Adverse Drug Reactions with XGBoost a Pharmacovigilance Application},
booktitle={Proceedings of the 1st International Conference on Research and Development in Information, Communication, and Computing Technologies - Volume 1: ICRDICCT`25},
year={2025},
pages={713-720},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0013871700004919},
isbn={978-989-758-777-1},
}

in EndNote Style

TY - CONF

JO - Proceedings of the 1st International Conference on Research and Development in Information, Communication, and Computing Technologies - Volume 1: ICRDICCT`25
TI - Predecting Adverse Drug Reactions with XGBoost a Pharmacovigilance Application
SN - 978-989-758-777-1
AU - Sadhana K.
AU - J. V.
AU - Anoch B.
AU - Sree P.
AU - P. B. V. R.
AU - Murthy B.
PY - 2025
SP - 713
EP - 720
DO - 10.5220/0013871700004919
PB - SciTePress