CARDIA: cardiovascular risk detection among middle-aged and older adults

Project Summary

Cardiovascular diseases (CVD) in middle-aged and older adults are not studied enough, not recognized early, not diagnosed well and not treated properly. CVD, including heart failure, heart attacks and strokes, is a major global health issue and leading cause of death. The aim of our project is to address gaps in CVD prevention among this high-risk population. We will develop new AI-enabled tool (named CARDIA) for middle-aged and older adults to personalize CVD prevention based on various datasets, including the CLSA dataset. We will use machine learning (ML) methods such as naive Bayes, support vector machines and random forests, among others. The model will classify patients into risk categories. Ensemble models, another ML method, will be developed to improve predictive accuracy. Additionally, explainable methods will be integrated to provide insights into the reasoning behind the algorithms’ predictions. The outcome will be a tool for clinicians to predict and prevent CVD.