Machine Learning Models for Enhancing Cardiovascular Disease Management: AI Approaches for Predicting Risk, Monitoring Health, and Personalizing Treatment Plans

Abstract

Effective cardiac health care has a dramatic effect on patients’ prognosis, whether they have known cardiovascular disease or whether they are simply at risk for developing it in their future. On a global level, adherence to treatment guidelines and coordinated disease management strategies is becoming an important priority in the quest for improved healthcare outcomes, patient experiences, and the use of operational resources. It is not only one of the most important diseases in many countries but, along with cancer, it is also one of the leading causes of preventable death. Despite the progress that has been achieved in recent decades, it is a multifaceted ailment that also poses formidable health challenges. On the one hand, like many forms of cancer, it may be asymptomatic until presenting abruptly in the form of an acute cardiovascular event like a heart attack or stroke. A silent presentation, on the other hand, is the primary cause of symptoms that may vary substantially from one person to the next and may be brought on by other medical issues or moments of physical or emotional stress.