12^th International Heart Conference

Heart Attack Prediction

Artificial intelligence has made significant strides in predicting myocardial infarctions by analyzing a combination of clinical, biochemical, and imaging data. Traditional risk assessments often rely on static factors, such as age, cholesterol levels, and family history, but AI leverages dynamic, real-time data to provide more accurate predictions. Machine learning algorithms can detect subtle changes in biomarkers, ECG readings, and heart imaging that precede a heart attack, enabling early intervention. Predictive models also incorporate lifestyle factors, comorbidities, and genetic predispositions, allowing for a comprehensive risk assessment. Early detection facilitates timely preventive measures, including medication adjustments, dietary modifications, and targeted monitoring, ultimately reducing morbidity and mortality. AI-driven prediction systems can be integrated into wearable devices or hospital monitoring platforms, providing continuous assessment and actionable alerts for patients and clinicians. By combining precision analytics with proactive care, artificial intelligence enhances the ability to prevent myocardial infarctions and improve long-term cardiovascular outcomes.