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

Area of Science:

• Cardiology
• Medical Informatics
• Artificial Intelligence in Medicine

Background:

• Cardiovascular diseases (CVDs) represent a growing global health concern, necessitating improved risk prediction.
• Existing statistical models for cardiovascular events (CVEs) often lack precision at the individual patient level.
• Machine learning (ML) offers advanced capabilities for analyzing complex datasets and enhancing individual risk stratification.

Purpose of the Study:

• To review and compare the performance of ML methods against traditional statistical models for predicting CVEs.
• To summarize key ML techniques applicable to cardiovascular risk prediction.
• To discuss the application of ML in predicting specific CVEs, including major adverse cardiovascular events, heart failure, and arrhythmias.

Main Methods:

• Literature review comparing ML models with statistical models for CVE prediction.
• Summary of fundamental ML algorithms relevant to medical risk prediction.
• Analysis of studies focusing on ML applications for major adverse cardiovascular events, heart failure, and arrhythmias.

Main Results:

• Evidence indicates ML methods generally outperform statistical models in predicting CVEs.
• ML models demonstrate superior ability in providing individualized risk predictions compared to population-level statistical models.
• Statistical models are prone to overfitting, whereas ML methods can capture complex patterns for personalized risk assessment.

Conclusions:

• ML methods are superior to traditional statistical models for predicting cardiovascular events.
• ML enables personalized risk assessment, a significant advantage over population-based statistical approaches.
• Future research should focus on prospective studies evaluating ML-guided interventions for CVE prevention.