Digital Twin-Supportive Predictive Analytics for Personalized Cardiac Care by Edge-Cloud Integration

Abstract

It describes a predictive analytics framework for personalized cardiac care based on a digital twin that exploits the edge-cloud integration to provide real-time, patient-specific health information. A dynamic digital replica of a patient's cardiac profile is thus generated continuously from data collected by wearable sensors and IoT-enabled medical devices of the proposed system. Edge computing nodes perform preliminary data preprocessing and anomaly detection at the origin, guaranteeing low-latency reactions for urgent cardiac cases, while the cloud infrastructure employs machine learning to perform deep predictive modelling. The system makes it possible to diagnose cardiac-based disorders, tracks the disease progression of the disorder stages, and generates personalized treatment plans depending on an individual's physiological information. The hybrid edge-cloud schema on the proposed framework ensured the suitability of its strengths in making the computational cost efficacy with predictive precision simultaneously, giving continuous patient monitoring and an adaptive manner of digital twin models.

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