| Abstract: |
Digital Twins that can integrate with related technologies such as Artificial intelligence, optimization, mobile communication systems, edge computing, fog computing, cloud computing, etc. are virtual representations of physical objects and reflect the real time status through streaming data. In this study, we provide two Digital Twin frameworks both cloud-based and edge-based and compare them in terms of scalability, flexibility, latency and security. We represented those frameworks by developing a case study to predict cardiac patient, continuously monitor the risks related to heart disease, and reporting the risks to both healthcare professionals and users in real time. We extracted features over electrocardiogram signals and performed popular machine learning algorithms. We employed feature binning and feature selection methods to increase the robustness of the prediction model and, in total, we built 20 models. We presented empirical analysis on a publicly available dataset based on PTB Diagnostic ECG Database and evaluated the results in terms of accuracy, precision, recall and F-score. When predicting cardiac patients, Linear Regression outperformed the other classifiers with accuracy and F-score rates of 86% and 92%, respectively. This model has also the highest recall rate (98%), which is vital in predicting diseases. Meanwhile, Gradient Boosted Tree applied binning, mRMR feature selection method and random oversampling achieve high precision (91%). |