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Breast cancer remains a life-threatening condition affecting women worldwide, where early diagnosis is essential for improving survival rates. Study presents a computer-aided early detection system that distinguishes between benign and malignant tumors. Dataset used in open source from Kaggle, which is then processed though ensemble decision tree algorithm to perform classification. Extensive preprocessing including noise reduction and feature selection makes the model efficient. Results prove the significance of the model for medical usage, thereby assisting healthcare professionals to enhance diagnostic accuracy and patient outcomes.

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Application of Ensemble Decision Tree Classifier for Automated Precision Assessment of Breast Cancer

  • 会议论文

  • 发表日期：

    2026年

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  • Ruhani Bose,
  • Siddhartha Kumar Panda,
  • Nilamadhab Mishra,
  • Tiansheng Yang,
  • Bharati Rathore

摘要

Breast cancer remains a life-threatening condition affecting women worldwide, where early diagnosis is essential for improving survival rates. Study presents a computer-aided early detection system that distinguishes between benign and malignant tumors. Dataset used in open source from Kaggle, which is then processed though ensemble decision tree algorithm to perform classification. Extensive preprocessing including noise reduction and feature selection makes the model efficient. Results prove the significance of the model for medical usage, thereby assisting healthcare professionals to enhance diagnostic accuracy and patient outcomes.