Utilizing machine learning for proactive detection of cardiovascular risks: A data-driven approach

Cardiovascular diseases (CVDs) are the leading cause of death worldwide, account for 31% of all deaths and they represent an urgent public health problem. Early detection of cardiovascular risks mitigated with healthcare support is important in order to reduce future impact caused from these diseases. In this study we investigated the application of machine learning (ML) techniques to enhance earlier detection of cardiovascular risks, by better enabling high-risk individuals to become identified prospectively before they experience large adverse health events. We use the rich dataset available from the Framingham Heart Study (FHS), an extensive longitudinal study spanning several decades, which includes comprehensive information regarding demographic, clinical and lifestyle measures on more than 5,000 participants. Using state of the art machine learning models like logistic regression, random forest, support vector machines (SVM), and Neural Networks we explore complex patterns and relationships between various cardiovascular disease risks. The results demonstrated that the use of machine learning models in general, and with the random forest algorithm in specific, could significantly improve cardiovascular risk prediction to be 87% accurate with AUC-ROC score up to 0.92. From this, it was concluded that age in addition to levels of both cholesterol and blood pressure were some of the most important factors regarding risk for cardiovascular events. The study further highlights the ability of machine learning to assist targeted interventions aimed at high-risk individuals that can contribute towards personalized health care strategies. Use of these data-driven models in clinical practice would help in determining the risk and expected indications which will aid for an advance management of cardiovascular health, thus indirectly reduces morbidity and mortality associated with cardiovascular diseases. These models will need to be validated across a range of populations in future research, as well as their integration into real-time monitoring systems so that healthcare can become more predictive and feedback-driven.