MODERN APPROACHES TO FORECASTING FOR BREAST CANCER DIAGNOSIS BASED ON DATA MINING

Mukhamedieva Dildora Kabilovna; Doshanova Malika Yuldashovna; Shaazizova Madina Eldarovna

Authors

Mukhamedieva Dildora Kabilovna Tashkent University of Information Technologies named after Muhammad al-Khwarizmi
Doshanova Malika Yuldashovna Tashkent University of Information Technologies named after Muhammad al-Khwarizmi
Shaazizova Madina Eldarovna Muhammad al-Xorazmiy nomidagi Toshkent axborot texnologiyalari universiteti

Keywords:

Машинное зрение, классификация, сортировка, зерно, анализ и обработка изображений, реальное время

Abstract

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.

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