Prediction of Cancer Treatment Effectiveness and Patient Outcomes using Machine Learning Classification Approaches - A Review

Authors

  • Maneesh Ragavendra K  B. Tech CSE – AI ML, School of Computer Science and Engineering, Presidency University, Bangalore , Karnataka, India
  • Dr. R. Chinnaiyan  Professor & Head- RC, School of Computer Science and Engineering, Presidency University, Bangalore , Karnataka, India

DOI:

https://doi.org/10.32628/IJSRST523103148

Keywords:

Machine learning, cancer treatment, radiation toxicity, survival rate, tumor response, ANN, DT, SVM, BNs.

Abstract

This study systematically reviews the Machine Learning methods developed to help predict the patient outcome and treatment effectiveness in cancer treatment. This research paper has been drafted from several other similar papers and with the help of a few topics related websites providing information regarding the radiation toxicity, survival rate and tumor response. Which are the main classification criteria for the patients. The use of ANN, DT, SVM and BNs has proved to be very beneficial in the classification of any given dataset, the accuracy of the model will be high with the use of all these ML methods.

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International Journal of Scientific Research in Science and Technology

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Published

2023-06-30

Issue

Volume 10, Issue 3, May-June-2023

Section

Research Articles

License

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Creative Commons License

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

How to Cite

[1]
Maneesh Ragavendra K, Dr. R. Chinnaiyan "Prediction of Cancer Treatment Effectiveness and Patient Outcomes using Machine Learning Classification Approaches - A Review" International Journal of Scientific Research in Science and Technology(IJSRST), Online ISSN : 2395-602X, Print ISSN : 2395-6011,Volume 10, Issue 3, pp.801-807, May-June-2023. Available at doi : https://doi.org/10.32628/IJSRST523103148