Scholarlink

The annual mortality rate due to breast cancer is rising significantly. Breast cancer is the most common type of cancer and a major contributor to mortality among women globally. Breast cancer has various subtypes with different behaviours and responses to treatment. Advancements in cancer prediction and diagnosis are crucial for improving outcomes, and achieving high accuracy in cancer prediction is vital for enhancing treatment approaches and patient survival rates. Machine learning techniques play a key role in predicting and diagnosing breast cancer effectively. In this study, we applied four machine learning algorithms: Random Forest, Logistic Regression, K-Nearest Neighbour and Decision Tree, on the Breast Cancer Wisconsin Diagnostic dataset. After obtaining the results, a performance evaluation and comparison were carried out between these different classifiers. Overall, the logistic regression algorithm outperformed all other classifiers and achieved the highest accuracy of 99%. The analysis was conducted in the Google Colab environment using Python programming language.

错误：搜索内容不能为空，请输入英文关键词
错误：关键词超出字数限制，请精简
高级检索

An Investigative Comparison of an Ensemble Model and Selected Machine Learning Algorithms for Breast Cancer Prediction

  • 会议论文

  • 发表日期：

    2026年

查看全文
  • Temitayo Ife Awoniran,
  • Abidemi Emmanuel Adeniyi,
  • Agbotiname Lucky Imoize,
  • Youssef Mejdoub,
  • Busolami Elizabeth Oluwadamilare,
  • Kehinde Amudat Odulana,
  • Joseph Bamidele Awotunde

摘要

The annual mortality rate due to breast cancer is rising significantly. Breast cancer is the most common type of cancer and a major contributor to mortality among women globally. Breast cancer has various subtypes with different behaviours and responses to treatment. Advancements in cancer prediction and diagnosis are crucial for improving outcomes, and achieving high accuracy in cancer prediction is vital for enhancing treatment approaches and patient survival rates. Machine learning techniques play a key role in predicting and diagnosing breast cancer effectively. In this study, we applied four machine learning algorithms: Random Forest, Logistic Regression, K-Nearest Neighbour and Decision Tree, on the Breast Cancer Wisconsin Diagnostic dataset. After obtaining the results, a performance evaluation and comparison were carried out between these different classifiers. Overall, the logistic regression algorithm outperformed all other classifiers and achieved the highest accuracy of 99%. The analysis was conducted in the Google Colab environment using Python programming language.