Bioinformatics Methods for Predicting Emotional State in Women with Breast Cancer

The study focuses on the development and application of bioinformatics methods for predicting the emotional state of women with breast cancer. The study used psychological questionnaires and physiological indicators, including heart rate variability and cortisol levels. Regression models and machine learning algorithms were employed for data analysis. The results confi rmed a signifi cant correlation between physiological and emotional parameters, as well as the high efficiency of the proposed prediction models. The integration of various types of data within decision support systems provides a personalized approach to psychological care, improving the quality of life and the eff ectiveness of patient adaptation.