Cardiovascular Risk Estimation in Colombia Using Artificial Intelligence Techniques

Introduction: There is no information on the potential of machine learning (ML)-based techniques to improve cardiovascular risk estimation in the Colombian population. This article presents innovative models using five artificial intelligence techniques: neural networks, decision trees, support vector machines, random forests, and Gaussian Bayesian networks.Methods: The research is based on a cohort of 847 patients free of cardiovascular disease at baseline and followed for cardiovascular disease events over 10 years at the Central Military Hospital in Bogot & aacute;, Colombia. To enhance the robustness and reduce the risk of overfitting, model evaluation was conducted using a 5-fold cross-validation on the entire dataset. Discriminatory ability was evaluated with the area under a ROC curve (AUC-ROC) for each ML-based model and the Framingham model.Results: Experimental results showed that the neural network technique had the best discriminative ability to predict cardiovascular events, with an AUC-ROC of 0.69 (CI 95% 0.622-0.759) for unbalanced data and 0.67 (CI 95% 0.601-0.754) for balanced data. Other ML techniques also showed good discriminatory ability with AUC-ROC values between 0.56 and 0.65, superior to that observed for the Framingham model (0.53; CI 95% 0.468-0.607).Conclusion: Our study supports the flexible ML approaches to cardiovascular risk prediction as a way forward for cardiovascular risk assessment in Colombia. Our data even suggest that risk prediction using these techniques could be even more discriminative than widely used risk-stimulation models such as Framingham, adapted to the Colombian population. However, new prospective studies need to validate our data before general implementation.