MACHINE LEARNING IN THE DIAGNOSIS OF ENDOMETRIOSIS

Rusinova Anastasia Konstantinovna

This article examines the application of machine learning methods in the diagnosis of endometriosis, highlighting the current state of research, achievements, and potential directions for further work. Endometriosis is a chronic condition that significantly affects women's morbidity and quality of life. Traditional diagnostic methods, including imaging and invasive procedures, have their limitations, emphasizing the need for the integration of modern technologies. The main machine learning algorithms used for analyzing medical data are discussed, including classification, regression, and clustering methods. Factors contributing to the successful diagnosis of endometriosis are identified, such as the volume and quality of data, as well as approaches to data processing and analysis. In conclusion, it is noted that machine learning can significantly improve the accuracy of endometriosis diagnosis; however, more clinical trials and standards are needed to achieve clinical practice.

Keywords:endometriosis, machine learning, diagnostics, noninvasive diagnostics, algorithms, medical data, chronic disease