Detection of cardiovascular disease cases using advanced tree-based machine learning algorithms.

Cardiovascular disease (CVD) can often lead to serious consequences such as death or disability. This study aims to identify a tree-based machine learning method with the best performance criteria for the detection of CVD. This study analyzed data collected from 9,499 participants, with a focus on 38 different variables.

Identifying Risk Indicators of Cardiovascular Disease in Fasa Cohort Study (FACS): An Application of Generalized Linear Mixed-Model Tree.

Background: Today, cardiovascular disease (CVD) is the most important cause of death around the world. In this study, our main aim was to predict CVD using some of the most important indicators of this disease and present a tree-based statistical framework for detecting CVD patients according to these indicators.

Methods: We used data from the baseline phase of the Fasa Cohort Study (FACS).