Abstract :

This work presents an intelligent health-based application designed for the early detection of silent heart attacks by monitoring vital physiological parameters in real time. Leveraging wearable sensors and advanced machine learning models, the system identifies subtle anomalies indicative of cardiac ischemia without overt symptoms. Data collected from diverse user profiles are processed to generate predictive alerts, aiming to reduce the risk of undiagnosed heart events. The application interfaces with clinicians to enable timely interventions, improving patient outcomes. Initial validation shows promising sensitivity and specificity rates. The proposed solution addresses gaps in traditional detection by providing continuous, personalized monitoring. Results indicate feasibility for large-scale public health deployment.