Comparative analysis and application of algorithms for clustering multidimensional medical data

The article presents the advantages and disadvantages of using clustering methods using the Kohonen neural network and the DBSCAN algorithm in solving problems of analyzing multidimensional medical data. An example of using clustering methods to identify the relationship between the development of sensorineural hearing loss in newborns and the disease caused by the COVID-19 virus in the children themselves or in their mothers during pregnancy is analyzed. The considered methods are also used to prepare recommendations for monitoring newborns depending on the results of clustering data on their examination and anamnesis using various methods. The use of clustering methods in medicine expands the arsenal of tools for researchers and practicing doctors who use them for the purpose of diagnosing, predicting the course of diseases, the development of pathologies and forming a treatment plan.