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Enhanced Prediction of Breast Cancer Using Machine Learning Ensemble Models and Techniques

  • 2024
  • OriginalPaper
  • Chapter
Published in:
Authors
E. Chandralekha
S Ravikumar
K Antony Kumar
M. J. Carmel Mary Belinda
Published in
Proceedings of Third International Conference on Computing and Communication Networks
Publisher
Springer Nature Singapore

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Abstract

The chapter delves into the application of machine learning ensemble models to improve breast cancer prediction, highlighting the significance of accurate diagnosis for effective treatment and patient outcomes. It provides an in-depth review of various ensemble models, including Random Forest, Gradient Boosting, AdaBoost, Bagging, and Extra Trees, and evaluates their performance using the Wisconsin Breast Cancer Dataset. The study emphasizes the importance of thorough feature analysis and model selection, with a particular focus on AdaBoost's superior performance in minimizing false positives and maximizing true positives. The research also discusses the limitations of the study and potential avenues for future research, underscoring the critical role of advanced technologies in enhancing medical diagnostics and healthcare quality.
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Title
Enhanced Prediction of Breast Cancer Using Machine Learning Ensemble Models and Techniques
Authors
E. Chandralekha
S Ravikumar
K Antony Kumar
M. J. Carmel Mary Belinda
Copyright Year
2024
Publisher
Springer Nature Singapore
Book
Proceedings of Third International Conference on Computing and Communication Networks

Print ISBN: 978-981-9708-91-8

Electronic ISBN: 978-981-9708-92-5

Copyright Year: 2024

DOI
https://doi.org/10.1007/978-981-97-0892-5_58
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