Performance Evaluation of Supervised Machine Learning Classifiers for Predicting Cancer Diseases

Authors

  • Vibha Sahu  PG Scholar, Department of Computer Science and Engineering, TRUBA College, Bhopal, Madhya Pradesh, India
  • Prof. Ritu Chaturvedi  Professor, Department of Computer Science and Engineering, TRUBA College, Bhopal, Madhya Pradesh, India
  • Dr. Kaptan Singh  Professor, Department of Computer Science and Engineering, TRUBA College, Bhopal, Madhya Pradesh, India

Keywords:

Machine Learning Classifiers, Healthcare Decision Making, K Nearest Neighbor, Random Forest

Abstract

This paper talks about a healthcare operational decision-making system that uses machine learning classifiers to predict decisions based on the actual decisions made by the doctor during healthcare operations. In this type of system for making decisions, most of the supervised machine learning classification and optimization techniques are used. This system can help the doctor decide what to do in the best way. We testify to this system on the caesarian section, which is the most common obstetric operation in the world to help save both mother and baby. This system helps us figure out when surgery is a good idea. This study shows how machine learning algorithms can be used to figure out how to do medical procedures. For this case study, the results show that both k nearest neighbours and Random Forest have an accuracy of 95.00%.

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IInternational Journal of Scientific Research in Computer Science, Engineering and Information Technology

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Published

2023-02-28

Issue

Volume 9, Issue 1, January-February-2023

Section

Research Articles

License

Copyright (c) IJSRCSEIT

Creative Commons License

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

How to Cite

[1]
Vibha Sahu, Prof. Ritu Chaturvedi, Dr. Kaptan Singh, " Performance Evaluation of Supervised Machine Learning Classifiers for Predicting Cancer Diseases " International Journal of Scientific Research in Computer Science, Engineering and Information Technology(IJSRCSEIT), ISSN : 2456-3307, Volume 9, Issue 1, pp.32-37, January-February-2023.