Abstract: Breast cancer is a significant global health concern, stressing the urgent need for early detection.
Early diagnosis improves access to varied treatments and significantly enhances patient outcomes. This study
explores breast cancer detection over two days, aiming to create a precise and efficient machine learning model.
The research uses a diverse dataset, combining clinical, genetic, and imaging data, including magnetic resonance
imaging (MRI), X-ray, and electromagnetic data. Rigorous data preprocessing, including variable normalization
and feature identification, enhances dataset quality. Predictive models use statistical techniques like
logistic regression, decision trees, and random forest. Key metrics, such as accuracy, precision, recall, and area
under the curve (AUC), assess model efficacy. Results reveal high accuracy and AUC scores, indicating potential
for precise breast cancer detection. The study enhances our understanding of breast cancer dynamics,
showcasing the effectiveness of machine learning for accurate and efficient early diagnosis. The research underscores
diverse datasets and careful statistical modeling as crucial for predictive breast cancer capabilities.