Machine learning in the prevention of heart failure

Area of Science:

• Cardiology
• Biomedical Engineering
• Artificial Intelligence in Medicine

Background:

• Heart failure (HF) presents a growing global health challenge, particularly in preventing symptomatic disease (stage C).
• Early detection of pre-heart failure (stage B) is difficult with current models, limiting effective intervention.
• Preventing HF with preserved ejection fraction remains a significant unmet need.

Purpose of the Study:

• To review the application of machine learning (ML) in preventing heart failure.
• To explore ML's potential to overcome challenges in pre-HF detection and intervention.
• To discuss the benefits, pitfalls, and future directions of ML in HF prevention.

Main Methods:

• Review of existing literature on ML applications in cardiovascular disease prevention.
• Analysis of ML strategies for risk prediction, diagnostic sign capture (ECG, X-ray, echocardiograms), and biomarker interpretation.
• Discussion of ML's role in identifying individuals at risk (stage A) and those with pre-HF (stage B).

Main Results:

• ML can enhance HF risk prediction models, improving early identification of individuals at risk.
• ML algorithms can detect subtle structural/functional cardiac abnormalities from various diagnostic tests.
• ML facilitates the interpretation of complex biomarker and epigenetic data for HF risk assessment.

Conclusions:

• Machine learning offers powerful tools to expand HF screening and identify pre-HF populations.
• ML enables early intervention to prevent progression to symptomatic heart failure (stage C).
• Despite limitations, ML's benefits in HF prevention outweigh risks, paving the way for future advancements.