Abstract
The article is devoted to the research of personalized medicine methods using Bayesian analysis. In modern medicine, one of the most important ways is to make effective decisions taking into account the genetic, clinical and lifestyle characteristics of each patient. In this regard, probabilistic approaches to data analysis in conditions of uncertainty have a special application. The research examines the possibilities of assessing the likelihood of developing diseases and optimizing treatment strategies by combining various sources of medical data. The Bayesian method combines preliminary knowledge and new clinical information. Thus, it allows you to increase the accuracy of diagnosis and assess risks. The study analyzes effective ways to support the medical decision-making process using probabilistic models. At the same time, the practical significance of a personalized approach in medicine is revealed. In particular, the issues of early diagnosis and individualization of treatment are considered. The presented method allows you to improve the quality of medical data processing. Aimed at developing the rules of personalized medicine.