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Original Article
Cancer Patient Identification using Machine Learning and Clustering
Amrita Sinha1
Sujit Kumar Chatterjee2
1 2 Department of Computer Science & Engineering, Birla Institute of Technology, Patna Campus, Bihar, India.
Published Online: March-April 2026
Pages: 274-279
Cite this article
↗ https://www.doi.org/10.59256/ijire.20260702033
References
1. Han, J., Kamber, M., & Pei, J. Data Mining: Concepts and Techniques. Morgan Kaufmann, 2011.
2. Géron, A. Hands-On Machine Learning with Scikit-Learn, Keras, and TensorFlow. O’Reilly Media, 2019.
3. [Pedregosa, F., et al. “Scikit-learn: Machine Learning in Python.” Journal of Machine Learning Research, 2011.
4. Chen, T., & Guestrin, C. “XGBoost: A Scalable Tree Boosting System.” Proceedings of the ACM SIGKDD, 2016.
5. Breiman, L. “Random Forests.” Machine Learning Journal, 2001.
6. Cortes, C., & Vapnik, V. “Support-Vector Networks.” Machine Learning Journal, 1995.
7. Bishop, C. M. Pattern Recognition and Machine Learning. Springer, 2006.
8. World Health Organization (WHO). “Cancer Fact Sheet.” Available: https://www.who.int
9. National Cancer Institute. “Cancer Statistics.” Available: https://www.cancer.gov
10. Kaggle Dataset. “Cancer Prediction Dataset.” Available: https://www.kaggle.com
11. Kotsiantis, S. “Supervised Machine Learning: A Review of Classification Techniques.” Informatica, 2007.
12. Mitchell, T. M. Machine Learning. McGraw-Hill, 1997.
13. Dua, D., & Graff, C. “UCI Machine Learning Repository.” University of California, Irvine.
14. Esteva, A., et al. “Dermatologist-level classification of skin cancer with deep neural networks.” Nature, 2017.
15. Topol, E. “High-performance medicine: the convergence of human and artificial intelligence.” Nature Medicine, 2019.
2. Géron, A. Hands-On Machine Learning with Scikit-Learn, Keras, and TensorFlow. O’Reilly Media, 2019.
3. [Pedregosa, F., et al. “Scikit-learn: Machine Learning in Python.” Journal of Machine Learning Research, 2011.
4. Chen, T., & Guestrin, C. “XGBoost: A Scalable Tree Boosting System.” Proceedings of the ACM SIGKDD, 2016.
5. Breiman, L. “Random Forests.” Machine Learning Journal, 2001.
6. Cortes, C., & Vapnik, V. “Support-Vector Networks.” Machine Learning Journal, 1995.
7. Bishop, C. M. Pattern Recognition and Machine Learning. Springer, 2006.
8. World Health Organization (WHO). “Cancer Fact Sheet.” Available: https://www.who.int
9. National Cancer Institute. “Cancer Statistics.” Available: https://www.cancer.gov
10. Kaggle Dataset. “Cancer Prediction Dataset.” Available: https://www.kaggle.com
11. Kotsiantis, S. “Supervised Machine Learning: A Review of Classification Techniques.” Informatica, 2007.
12. Mitchell, T. M. Machine Learning. McGraw-Hill, 1997.
13. Dua, D., & Graff, C. “UCI Machine Learning Repository.” University of California, Irvine.
14. Esteva, A., et al. “Dermatologist-level classification of skin cancer with deep neural networks.” Nature, 2017.
15. Topol, E. “High-performance medicine: the convergence of human and artificial intelligence.” Nature Medicine, 2019.
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