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Original Article
Cardiovascular Disease Prediction Using Machine Learning
Muskan Begum1
Dr. Khaja Mahabubullah2
1 Student, MCA, Deccan College of Engineering and Technology, Hyderabad, Telangana, India. 2 Professor & HOD, MCA, Deccan College of Engineering and Technology, Hyderabad, Telangana, India.
Published Online: May-August 2025
Pages: 360-364
Cite this article
↗ https://www.doi.org/10.59256/indjcst.20250402049
References
1. R. Detrano, A. Janosi, W. Steinbrunn, et al., “International application of a new probability algorithm for the diagnosis of coronary artery disease,” The American Journal of Cardiology, vol. 64, no. 5, pp. 304–310, 1989.
2. D. Dua and C. Graff, “UCI Machine Learning Repository: Heart Disease Dataset,” 2019. [Online]. Available: http://archive.ics.uci.edu/ml
3. K. Fernandes, J. S. Cardoso, and J. Fernandes, “Transfer learning with CNNs for cardiovascular disease diagnosis from ECG signals,” Journal of Biomedical Informatics, vol. 68, pp. 45–51, 2017.
4. H. Kaur and V. Kumari, “Predictive modelling and analytics for diabetes using a machine learning approach,” Applied Computing and Informatics, vol. 18, no. 1, pp. 90–100, 2020.
5. F. Pedregosa, G. Varoquaux, A. Gramfort, et al., “Scikit-learn: Machine learning in Python,” Journal of Machine Learning Research, vol. 12, pp. 2825–2830, 2011.
6. T. Chen and C. Guestrin, “XGBoost: A scalable tree boosting system,” in Proc. 22nd ACM SIGKDD Int. Conf. Knowledge Discovery and Data Mining, 2016, pp. 785–794.
7. M. T. Ribeiro, S. Singh, and C. Guestrin, “Why Should I Trust You?: Explaining the predictions of any classifier,” in Proc. 22nd ACM SIGKDD Int. Conf. Knowledge Discovery and Data Mining, 2016, pp. 1135–1144.
8. WorldHealth Organization, “Cardiovascular Diseases (CVDs),” 2021. [Online]. Available: https://www.who.int/news-room/fact-sheets/detail/cardiovascular-diseases-(cvds)
9. A. Rajkomar, J. Dean, and I. Kohane, “Machine Learning in Medicine,” New England Journal of Medicine, vol. 380, pp. 1347–1358, 2019.
10. J. Brownlee, Master Machine Learning Algorithms: Discover How They Work and Implement Them from Scratch. Machine Learning Mastery, 2016.
2. D. Dua and C. Graff, “UCI Machine Learning Repository: Heart Disease Dataset,” 2019. [Online]. Available: http://archive.ics.uci.edu/ml
3. K. Fernandes, J. S. Cardoso, and J. Fernandes, “Transfer learning with CNNs for cardiovascular disease diagnosis from ECG signals,” Journal of Biomedical Informatics, vol. 68, pp. 45–51, 2017.
4. H. Kaur and V. Kumari, “Predictive modelling and analytics for diabetes using a machine learning approach,” Applied Computing and Informatics, vol. 18, no. 1, pp. 90–100, 2020.
5. F. Pedregosa, G. Varoquaux, A. Gramfort, et al., “Scikit-learn: Machine learning in Python,” Journal of Machine Learning Research, vol. 12, pp. 2825–2830, 2011.
6. T. Chen and C. Guestrin, “XGBoost: A scalable tree boosting system,” in Proc. 22nd ACM SIGKDD Int. Conf. Knowledge Discovery and Data Mining, 2016, pp. 785–794.
7. M. T. Ribeiro, S. Singh, and C. Guestrin, “Why Should I Trust You?: Explaining the predictions of any classifier,” in Proc. 22nd ACM SIGKDD Int. Conf. Knowledge Discovery and Data Mining, 2016, pp. 1135–1144.
8. WorldHealth Organization, “Cardiovascular Diseases (CVDs),” 2021. [Online]. Available: https://www.who.int/news-room/fact-sheets/detail/cardiovascular-diseases-(cvds)
9. A. Rajkomar, J. Dean, and I. Kohane, “Machine Learning in Medicine,” New England Journal of Medicine, vol. 380, pp. 1347–1358, 2019.
10. J. Brownlee, Master Machine Learning Algorithms: Discover How They Work and Implement Them from Scratch. Machine Learning Mastery, 2016.
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