Machine learning-based predictive models for cardiovascular risk assessment in data analysis, model development, and clinical implications
Cardiovascular diseases (CVDs) remain a leading global cause of morbidity and mortality. Timely identification of individuals at risk is paramount for effective interventions and prevention. This study endeavors to develop machine learning approaches for predicting the initial cardiovascular risk level analyzing the dataset encompassing patient demographics, medical history, lifestyle factors, and clinical indicators.