The advent of the 21st century has witnessed an alarming rise in the prevalence of cardiovascular diseases (CVDs), disorders that primarily afflict the heart or blood vessels. These conditions, as per the World Health Organization, have catapulted to the top rank as the leading cause of global mortality, accounting for nearly one-third of all deaths. The high mortality rate of these diseases underscores the pressing need for effective diagnosis and treatment strategies.
One common endpoint of many CVDs is heart failure, a complex clinical syndrome where the heart fails to pump an adequate quantity of blood to meet the body's physiological demands. Several factors, including aging, hypertension, diabetes, and certain lifestyle choices like smoking, can contribute to heart failure. However, the multifaceted nature of this disease makes its prediction and prognosis considerably challenging.
Stats of deaths caused due to heart failure and which could be diagnosed through early detection:
Heart failure is a leading cause of death worldwide, accounting for over 9 million deaths each year. The mortality rate of heart failure patients is high, with approximately 50% of patients dying within five years of diagnosis. Early detection and intervention are critical to improving patient outcomes and reducing mortality rates. Machine learning methodologies could help identify key factors that contribute to mortality in heart failure patients, allowing for earlier and more accurate diagnosis and treatment.
In this context, our study aims to investigate the risk factors associated with heart failure and their impact on patient survival. We leverage a comprehensive dataset of clinical parameters from heart failure patients, employing machine learning methodologies to construct a predictive model. By identifying the key determinants of mortality among these patients, we aim to provide valuable insights that can guide clinical decisions and enhance patient management strategies. We hope that our work will contribute to global efforts in understanding, predicting, and managing heart failure, thereby reducing its global health burden.