Tatiana V. Soloveva

this paper evaluates the performance of three data analysis techniques-decision trees, neural networks, and self-organizing Kohonen maps-in identifying cardiovascular disease patterns. Based on a dataset of 1,328 patient records, the models were compared regarding classification accuracy for target variables and demographic attributes. The neural network achieved the lowest error rate for disease prediction (3.61%), while the decision tree showed robust performance (6.17%) with higher interpretability. The findings underscore the necessity of method selection based on data specificity and the...