EARLY BREAST CANCER DETECTION AND DIAGNOSIS IN KAZAKHSTAN USING MACHINE LEARNING METHODS

Abstract

Breast cancer is a significant public health concern in Kazakhstan, with limited access to screening and diagnostic services, inadequate public awareness, and other socioeconomic factors posing significant challenges to effective early detection and diagnosis. In recent years, efforts have been made to improve early breast cancer detection through national cancer control programs and international partnerships. However, there is still a need for further research and improvement in this area. Machine learning (ML) methods have shown promise in assisting healthcare professionals in detecting and diagnosing breast cancer at an early stage. This paper proposes a comprehensive understanding of the current state of breast cancer screening and diagnosis in Kazakhstan, highlighting areas for further research and improvement. A complex model for detecting breast cancer signs can be developed using ML algorithms, which have the capability to analyze large datasets of medical images and patient information, leading to accurate and prompt diagnoses. With ML algorithms, healthcare professionals can remotely screen patients for breast cancer, reducing the need for in-person visits, and enabling patients to receive personalized and effective treatments, improving patient outcomes and reducing healthcare costs.

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