Machine learning-driven predictions and interventions for cardiovascular occlusions.

Area of Science:

• Cardiovascular medicine
• Biomedical data science
• Machine learning applications

Background:

• Cardiovascular diseases (CVDs) are a leading global cause of death, with heart attacks and strokes posing significant health challenges.
• Cardiovascular occlusions, or blood vessel blockages, are a critical factor contributing to CVD mortality.
• Accurate and early diagnosis and management are crucial for improving patient outcomes in CVDs.

Purpose of the Study:

• To leverage machine learning (ML) for improved prediction and management of cardiovascular occlusions.
• To reduce the incidence of heart attacks, strokes, and other related health issues through advanced ML interventions.
• To develop more accurate and timely diagnostic and management strategies for cardiovascular events.

Main Methods:

• Analysis of diverse datasets using various ML algorithms to predict heart attacks and strokes.
• Comparison of ML model performance to identify the most accurate and reliable predictors.
• Classification of individuals by predicted risk levels and examination of key correlating features.
• Utilized PyCaret's Classification Module and stratified cross-validation for robust model development and evaluation.

Main Results:

• Machine learning significantly improves prediction accuracy for heart attacks and strokes.
• Identified key features correlating with cardiovascular event incidence.
• Demonstrated the potential for earlier and more precise medical interventions through ML predictions.

Conclusions:

• ML-driven risk stratification and identification of modifiable factors enable preemptive cardiovascular care.
• Effective integration of ML models into clinical practice requires addressing challenges and ensuring healthcare professional interpretation.
• The study aims to reduce life-threatening cardiovascular events and improve long-term patient health trajectories.