Digital Twin For A Human Heart Using Deep Learning and Stream Processing Platforms

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Abstract Cardiovascular diseases (CVDs) constitute a substantial global health challenge, with heart diseases ranking among the leading causes of mortality worldwide. This paper addresses this urgent concern by proposing innovative approaches. The fusion of Digital Twin technology with artificial intelligence offers a unique framework for personalized diagnosis, therapy selection, remote monitoring, and real-time treatment adjustments. By combining virtual patient replicas with medical history, real-time data, and machine learning algorithms, the potential for early detection and prevention of heart diseases becomes a reality. This paper presents a comprehensive exploration of leveraging Digital Twin technology for precise and real-time heart disease prediction, focusing on data management, security, and preprocessing. The research aims to lay a robust foundation for the development of a medical decision support system capable of precise predictions and interventions within the realm of heart disease. By combining virtual patient replicas with medical history, real-time data, and advanced machine learning algorithms, our paper explores the potential for early detection and prevention of heart diseases, centering on the development and detailed analysis of an ECG model. This ECG model leverages Digital Twin technology to enable precise and real-time heart disease prediction.

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