A Comparative Study on Cloud-based and Edge-Based Digital Twin Frameworks for Prediction of Cardiovascular Disease

Havvanur Dervisoglu, Burak Ülver, Rabia Arkan Yurtoglu, Rusen Halepmollasi, Mehmet Haklidir

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 base

External IDs:dblp:conf/ict4ageingwell/DervisogluUYHH23