MACHINE LEARNING METHODS FOR BREAST CANCER CLASSIFICATION BY USING DATA SCIENCE TECHNIQUES

This study explores the sensitivity analysis of various machine learning methods applied to the problem of breast cancer classification. By examining the robustness and performance of different algorithms, we aim to identify the most reliable techniques for accurate diagnosis. We assess the impact of key parameters and data variations on model outcomes to provide a comprehensive understanding of each method's strengths and limitations. Our findings offer valuable insights into the selection and optimization of machine learning models for breast cancer detection, ultimately contributing to improved diagnostic accuracy and patient care.

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