Vol. 5 No. 2 (2025): July−December 2025
Articles

Comparative Study of Machine Learning Approaches for Predicting Cardiotoxicity of Anticancer Drugs

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  • Guifan Weng,
  • Le Yu,
  • Lingfeng Guo
Guifan Weng
Computer Science, University of Southern California, CA, USA
Le Yu
Electronics and Communication Engineering, Peking University, Beijing, China
Lingfeng Guo
Business Analytics, Trine University, AZ, USA

Published 2025-09-26

Keywords

  • cardiotoxicity prediction,
  • machine learning,
  • anticancer drugs,
  • computational toxicology

How to Cite

Weng, G., Yu, L., & Guo, L. (2025). Comparative Study of Machine Learning Approaches for Predicting Cardiotoxicity of Anticancer Drugs. Spectrum of Research, 5(2). Retrieved from https://spectrumofresearch.com/index.php/sr/article/view/39

Copyright (c) 2025 Spectrum of Research

Creative Commons License

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

Abstract

Cardiotoxicity represents one of the most severe adverse effects associated with anticancer therapies, significantly impacting patient outcomes and treatment decisions. This study presents a comprehensive comparative analysis of multiple machine learning approaches for predicting anticancer drug-induced cardiotoxicity. We evaluated five distinct algorithms including Random Forest, Support Vector Machine, Gradient Boosting, Neural Networks, and XGBoost using a comprehensive dataset encompassing molecular descriptors, pharmacokinetic properties, and clinical parameters. Our methodology incorporated advanced feature selection techniques, cross-validation strategies, and statistical significance testing to ensure robust model performance evaluation. The experimental results demonstrated that ensemble methods, particularly XGBoost, achieved superior predictive accuracy with an AUC of 0.923, sensitivity of 87.5%, and specificity of 91.2%. Feature importance analysis revealed that molecular weight, lipophilicity, and dose-response parameters were the most significant predictors of cardiotoxicity. The study provides valuable insights for clinical decision-making in anticancer therapy selection and establishes a framework for future research in computational cardio-oncology. These findings contribute to the development of precision medicine approaches in cancer treatment while minimizing cardiovascular risks.

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