(untitled) (DIGITAL TRANSFORMATION AND ARTIFICIAL INTELLIGENCE, 2025)
The article examines modern approaches to prediction and classification in breast cancer diagnosis based on data mining techniques. It describes the most commonly used datasets and modalities, stages of preprocessing and feature extraction, classical and deep learning models, validation and evaluation methods, issues of interpretability, clinical validation, regulatory considerations, and ethical aspects. Recommendations for practical implementation and directions for further research are provided.