Machine learning approaches for cardiovascular disease prediction: A review

Area of Science:

• Artificial Intelligence in Medicine
• Cardiovascular Disease Diagnostics
• Machine Learning Applications

Background:

• Cardiovascular disease (CVD) remains a primary global cause of mortality and morbidity.
• Conventional diagnostic methods have limitations in early disease detection.
• Artificial intelligence (AI), specifically machine learning (ML), offers advanced diagnostic capabilities.

Purpose of the Study:

• To conduct a systematic review of ML algorithms for early CVD diagnosis.
• To analyze operational frameworks for integrating ML into clinical practice.
• To critically evaluate regulatory compliance and ethical implications of AI in cardiology.

Main Methods:

• Systematic review of recent literature on ML for cardiovascular disease prediction.
• Analysis of comparative performance of different ML algorithms.
• Examination of clinical implementation workflows and regulatory frameworks.

Main Results:

• ML algorithms demonstrate enhanced accuracy in early CVD diagnosis compared to traditional methods.
• Identified key operational frameworks for successful clinical integration of ML tools.
• Highlighted significant regulatory and ethical challenges requiring careful consideration.

Conclusions:

• ML holds transformative potential for early cardiovascular disease detection and management.
• Successful clinical implementation requires robust frameworks addressing operational, regulatory, and ethical aspects.
• Future research should focus on refining algorithms and addressing implementation barriers.