Abstract
The escalating global prevalence of heart diseases, particularly heart attacks, underscores a critical health concern that necessitates innovative solutions. Despite strides in healthcare, the current diagnostic methods exhibit limitations in accuracy and efficiency, while the vast reservoir of healthcare data remains underutilized. Traditional diagnostic methods lack precision, necessitating innovative solutions. This research introduces "CardioEdge - AI-Powered Exercise Companion," which addresses these challenges by harnessing the power of Computer Vision, Machine Learning, and Django. The platform made using Django predicts heart attack risk with an 85% accuracy rate and offers personalized exercise recommendations with real-time monitoring for proactive cardiovascular health management using computer vision. This solution addresses the crucial need for early, precise prediction, utilizing untapped healthcare data, and represents a significant step toward data driven, user-friendly heart health management.
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