Optimizing Cardiovascular Disease Risk Prediction Using Ensemble Learning Techniques - Santos, Abner Gabriel Dias dos; Marcolino, Bruno Eduardo; Lucas, Thiago José; Lopes, Alessandra de Souza; Tojeiro, Carlos Alexandre Carvalho; Costa, Kelton Augusto Pontara da (Anais do Congresso Latino-Americano de Software Livre e Tecnologias Abertas (Latinoware); 2025: Anais do XXII Congresso Latino-Americano de Software Livre e Tecnologias AbertasDO - 10.5753/latinoware.2025.16443, 2025)
The heart is one of the most essential organs, responsible for ensuring blood circulation throughout the human body. A wide range of cardiovascular diseases (CVDs) poses severe risks to human health. According to data from the World Health Organization (WHO), CVDs account for approximately 17.9 million deaths annually, representing 31% of all global fatalities. Machine learning techniques have demonstrated significant potential in effectively predicting the risk of CVD occurrence. In this study, ensemble learning algorithms were employed to optimize predictive analysis for cardiovascular events. The dataset Indicators of Heart Disease (2022 UPDATE), provided by the Centers for Disease Control and Prevention (CDC), served as the basis for model training and evaluation. The best-performing models were Gradient Boosting and Logistic Regression, both achieving an accuracy and precision rate of 94.9%.