부산대학교 도서관

In this age of digital and smart healthcare, cutting-edge technologies are being used to improve operations, patient well-being, life expectancy, and healthcare costs. Digital Twins (DT) have the potential to significantly change these new technologies. DTs could revolutionise digital healthcare delivery with extraordinary creativity. A digital representation of a physical asset that is always its digital twin due to real-time data processing. This paper proposes and builds a DT-based intelligent healthcare system that is aware of its environment. This approach is a great advance for digital healthcare and could improve service delivery. Our most notable contribution is a machine learning-based electrocardiogram (ECG) classifier model for cardiac diagnostics and early problem detection. Our cardiac models predict some situations with exceptional accuracy when applied to different ways. These findings highlight the potential for Digital Twins in healthcare to create intelligent, comprehensive, and scalable Health-Systems that improve patient-physician communication. Our ECG classifier also sets a precedent for using Artificial Intelligence (AI) and Machine Learning (ML) to continually monitor wide range of human body data and identify outliers. ECG data processing has improved significantly using neural network-based algorithms over classic machine learning methods. In conclusion, our work integrates digital twins with cutting-edge AI and machine learning to revolutionise healthcare. Future healthcare will be predictive and improve lives.