Machine learning for predicting cardiac events: what does the future hold?

Introduction: With the increase in the number of patients with cardiovascular diseases, better risk-prediction models for cardiovascular events are needed. Statistical-based risk-prediction models for cardiovascular events (CVEs) are available, but they lack the ability to predict individual-level risk. Machine learning (ML) methods are especially equipped to handle complex data and provide accurate risk-prediction models at the individual level.Areas covered: In this review, the authors summarize the literature comparing the performance of machine learning methods to that of traditional, statistical-based models in predicting CVEs. They provide a brief summary of ML methods and then discuss risk-prediction models for CVEs such as major adverse cardiovascular events, heart failure and arrhythmias.Expert opinion: Current evidence supports the superiority of ML methods over statistical-based models in predicting CVEs. Statistical models are applicable at the population level and are subject to overfitting, while ML methods can provide an individualized risk level for CVEs. Further prospective research on ML-guided treatments to prevent CVEs is needed.