Abstract
This article presents an analysis of the application of various machine learning algorithms, including Logistic Regression, K-Nearest Neighbors Classifier, Support Vector Machines, Decision Tree Classifier, Random Forest Classifier, Gradient Boosting, and XGBoost, for predicting fatal outcomes in heart diseases. The research is structured in two key phases: the first phase involves the identification and analysis of hidden parameters that influence the performance of the specified machine learning models; the second phase aims to compare the performance of these models based on objective criteria. The purpose of this work is to identify the optimal algorithmic approach for accurately predicting the risk of fatal outcomes in the context of cardiovascular pathologies.