A Comprehensive Review on Cancer Prediction Using Machine Learning Techniques

Authors

  • Srikanth R SCORE, Vellore Institute of Technology, Vellore, Tamil Nadu, INDIA
  • Tamil Priya D. SCO RE, Vellore Institute of Technology, Vellore, Tamil Nadu, INDIA
  • Jagadeesan S. SCORE, Vellore Institute of Technology, Vellore, Tamil Nadu, INDIA
  • Savita P. Patil Dept. of IT, Rajarambapu Institute of Technology, Shivaji University, Sangli, Maharashtra, INDIA.
  • Anupama K. Ingale Dept. of CSE, Rajarambapu Institute of Technology, Shivaji University, Sangli, Maharashtra, INDIA.
  • Manojkumar Vivekanandan Dept. of CSE, School of Engineering and Applied Sciences (SEAS) SRM University-AP, INDIA
  • Venkadeshan Ramalingam Dept. of IT, University of Technology and Applied Sciences-Shinas Branch, Sultanate of Oman.

Keywords:

Breast cancer, colorectical cancer, Lung cancer, Machine Learning, Prediction

Abstract

This comprehensive study aims to conduct a thorough analysis of machine learning methods and applications in cancer prediction. Breast cancer, lung cancer, and colorectal cancer are the three distinct categories of cancer that impact individuals on a global scale. We focus on machine learning (ML) algorithms to predict cancer which would be influenced by various performance measures. Using the most common ML techniques, including Support Vector Machines (SVM), K-Nearest Neighbors (KNN), Linear Regression, Decision Tree and Naive Bayes we investigate the accuracy of cancer prediction. Our study can serve as an analysis and recommendations regarding the use of machine learning techniques in clinical settings to improve cancer detection and care.

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Published

24.03.2024

How to Cite

R, S. ., Priya D., T. ., S., J. ., Patil, S. P. ., Ingale, A. K. ., Vivekanandan, M. ., & Ramalingam, V. . (2024). A Comprehensive Review on Cancer Prediction Using Machine Learning Techniques. International Journal of Intelligent Systems and Applications in Engineering, 12(3), 115–127. Retrieved from https://ijisae.org/index.php/IJISAE/article/view/5229

Issue

Vol. 12 No. 3 (2024)

Section

Research Article

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