Machine learning for predicting cardiac events: what does the future hold?
Brijesh Patel1, Partho Sengupta1
1Heart and Vascular Institute, West Virginia University, Morgantown, WV, USA.
Insights
Machine learning (ML) models outperform traditional statistical methods for predicting cardiovascular events (CVEs). ML offers individualized risk assessment, crucial for managing increasing cardiovascular disease burdens.
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.
Abstract:
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.
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