Diagnostic Algorithm for Early Detection of Breast Cancer Based on Error Minimization Approach

The relevance of the study lies in the fact that breast cancer is one of the most common oncological diseases among women, millions of women are diagnosed with it every year. Early detection is important in this disease, because if the disease is detected at an early stage, the chances of treatment are much higher. The study examines the use of artificial intelligence algorithms, in particular, ways to automate the process and improve accuracy based on interviewing users using a program created in Python. The practical significance of this scientific work lies in the fact that it proposes algorithmic approaches aimed at improving the early detection of breast cancer and improving the quality of life of patients by reducing errors. This scientific work is devoted to the development of diagnostic algorithms based on minimizing errors in early detection of breast cancer. The importance of diagnosis for early detection of breast cancer is considered and special attention is paid to the development of diagnostic software. This software package collects information about breast cancer and creates an algorithm that supports its diagnosis and treatment.