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Breast cancer detected early has high patient survival rates and an efficient treatment process. Progress in many fields including machine learning (ML) and boosting algorithms have helped change the face of predictive modeling in health care. In this paper, we discuss the combination of boosting with machine learning models for the higher reliability of the initial-stage breast cancer detection. From such datasets as Wisconsin Breast Cancer Dataset (WBCD), this research shows how increased techniques outcompete traditional approaches in sensitivity, specificity, and general performance.

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Enhancing Early Stage Breast Cancer Prediction with Boosting and Machine Learning Techniques

  • 会议论文

  • 发表日期：

    2025年

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  • P. Sinthia,
  • N. Vigneshwari,
  • G. Gurumoorthy,
  • S. Rajalakshmi,
  • E. Ramya

摘要

Breast cancer detected early has high patient survival rates and an efficient treatment process. Progress in many fields including machine learning (ML) and boosting algorithms have helped change the face of predictive modeling in health care. In this paper, we discuss the combination of boosting with machine learning models for the higher reliability of the initial-stage breast cancer detection. From such datasets as Wisconsin Breast Cancer Dataset (WBCD), this research shows how increased techniques outcompete traditional approaches in sensitivity, specificity, and general performance.