Intelligent Prediction of Cancer Diseases through Machine Learning

Cancer remains one of the leading causes of mortality worldwide, and timely diagnosis plays a critical role in improving patient survival rates. Traditional diagnostic methods often face challenges such as complexity, cost, and human error, necessitating the development of intelligent computational systems. This study proposes a machine learning–based framework for the intelligent prediction of cancer diseases, aiming to improve accuracy, reduce misdiagnosis, and support clinical decision-making. The proposed approach integrates feature selection, optimized model training, and performance evaluation to construct a scalable predictive model applicable to various types of cancer.

@article{ijter2025212509171966,
  author = {Veeramuthu P and Rajesh D},
  title = {Intelligent Prediction of Cancer Diseases through Machine Learning},
  journal = {International Journal of Technology &  Emerging Research },
  year = {2025},
  volume = {1},
  number = {5},
  pages = {107-111},
  issn = {3068-109X},
  url = {https://www.ijter.org/article/212509171966/intelligent-prediction-of-cancer-diseases-through-machine-learning},
  abstract = {Cancer remains one of the leading causes of mortality worldwide, and timely diagnosis plays a critical role in improving patient survival rates. Traditional diagnostic methods often face challenges such as complexity, cost, and human error, necessitating the development of intelligent computational systems. This study proposes a machine learning–based framework for the intelligent prediction of cancer diseases, aiming to improve accuracy, reduce misdiagnosis, and support clinical decision-making. The proposed approach integrates feature selection, optimized model training, and performance evaluation to construct a scalable predictive model applicable to various types of cancer.},
  keywords = {Cancer Prediction, Machine Learning, Intelligent Systems, Early Diagnosis, Clinical Decision Support},
  month = {Sep},
}

Copyright © 2025 Authors retain the copyright of this article. This article is an open access article distributed under the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0/) which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.