Enhancing Cardiac Health: Digital Twin Technology for Real- Time Monitoring and Abnormality Detection of Human Heart

As simulation technology is increasingly integrated into medical practice, the idea of diagnosis and treatments to individual patients through Digital Twin technology is gaining prominence in the field of precision medicine. This paper aims to develop a digital twin model of the human heart for simulating its function and predicting abnormalities through the application of machine learning techniques. Our method expands this frame- work to concentrate specifically on digital twin-based monitoring and abnormality detection of cardiac health by utilizing the readily accessible data from smartwatches. This method uses digital twin technology to create personalized real-time virtual heart models, detecting abnormalities and monitoring cardiac health using machine learning. We explore the use of a digital twin model enhanced with machine learning to forecast heart function abnormalities, relying solely on age as the input. We validate these forecasts through a brief two-minute assessment, covering three distinct heart conditions, including scenarios involving resting, walking, and normal heart rates. Furthermore, we examine the connection between normal heart rate and step count, aiming to identify any possible relationships. The findings illustrate its effectiveness in accurate diagnosis and tailored treatment. The incorporation of Digital Twins into healthcare holds the potential to revolutionize medical practice, facilitating precise detection tailored to individual patients.