Risk calculator for heart attack patients

Abstract

This study aims to enhance the accuracy of predicting the 30-day mortality rate among patients following their first heart attack by leveraging machine learning and deep learning techniques. The current cardiac risk stratification tool, the Revised Cardiac Risk Index, while widely utilized, exhibits limitations, particularly in its discriminative ability. To address this, we propose the development and validation of a novel predictive cardiac risk calculator. In contrast to existing risk calculators available online that estimate long-term cardiovascular disease (CVD) risk, our focus is on predicting short-term mortality specifically within the crucial 30-day post-myocardial infarction period. Utilizing machine learning algorithms and deep learning neural networks, we aim to create a more precise and personalized predictive model. This model takes into account various clinical parameters and potential risk factors, providing a nuanced understanding of the patient’s prognosis. Through the integration of machine learning and deep learning methodologies, this research aims to contribute to more effective risk assessment tools in the context of short-term mortality post-first heart attack, ultimately aiding clinicians and patients in making informed decisions about management strategies. This study underscores the importance of advancing predictive modeling techniques in the realm of cardiovascular care and serves as a stepping stone towards a more refined and patient-centric approach to risk assessment.