Predictive Heart Guard: Using Support Vector Machines for Early Warning Systems

In this study, the predictive modeling employed is the support vector machine (SVM) method, which efficiently handles highly complex health data. Despite the prevalence of stressful lifestyles today, it seems unlikely that people in the past had significantly weaker hearts. Heart failure occurs when the heart fails to pump effectively. SVM helps predict who might be at risk of serious vascular issues by identifying potential problems before they arise. With an impressive 79% accuracy in classifying various clinical conditions and risk factors, SVM demonstrates its effectiveness. If used properly, such predictions could revolutionize healthcare by detecting heart issues early and potentially reducing costs. This research highlights the critical role of advanced computer methods in improving early detection and management of heart failure, leading to more effective health monitoring strategies.