EfficientNet in Digital Twin-based Cardiac Arrest Prediction and Analysis
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| Main Authors: | , , , , , |
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| Format: | Preprint |
| Published: |
2025
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| _version_ | 1866915486063656960 |
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| author | Zia, Qasim Jan, Avais Iqbal, Zafar Ali, Muhammad Mumtaz Ali, Mukarram Patterson, Murray |
| author_facet | Zia, Qasim Jan, Avais Iqbal, Zafar Ali, Muhammad Mumtaz Ali, Mukarram Patterson, Murray |
| contents | Cardiac arrest is one of the biggest global health problems, and early identification and management are key to enhancing the patient's prognosis. In this paper, we propose a novel framework that combines an EfficientNet-based deep learning model with a digital twin system to improve the early detection and analysis of cardiac arrest. We use compound scaling and EfficientNet to learn the features of cardiovascular images. In parallel, the digital twin creates a realistic and individualized cardiovascular system model of the patient based on data received from the Internet of Things (IoT) devices attached to the patient, which can help in the constant assessment of the patient and the impact of possible treatment plans. As shown by our experiments, the proposed system is highly accurate in its prediction abilities and, at the same time, efficient. Combining highly advanced techniques such as deep learning and digital twin (DT) technology presents the possibility of using an active and individual approach to predicting cardiac disease. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2509_07388 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | EfficientNet in Digital Twin-based Cardiac Arrest Prediction and Analysis Zia, Qasim Jan, Avais Iqbal, Zafar Ali, Muhammad Mumtaz Ali, Mukarram Patterson, Murray Machine Learning Computer Vision and Pattern Recognition Cardiac arrest is one of the biggest global health problems, and early identification and management are key to enhancing the patient's prognosis. In this paper, we propose a novel framework that combines an EfficientNet-based deep learning model with a digital twin system to improve the early detection and analysis of cardiac arrest. We use compound scaling and EfficientNet to learn the features of cardiovascular images. In parallel, the digital twin creates a realistic and individualized cardiovascular system model of the patient based on data received from the Internet of Things (IoT) devices attached to the patient, which can help in the constant assessment of the patient and the impact of possible treatment plans. As shown by our experiments, the proposed system is highly accurate in its prediction abilities and, at the same time, efficient. Combining highly advanced techniques such as deep learning and digital twin (DT) technology presents the possibility of using an active and individual approach to predicting cardiac disease. |
| title | EfficientNet in Digital Twin-based Cardiac Arrest Prediction and Analysis |
| topic | Machine Learning Computer Vision and Pattern Recognition |
| url | https://arxiv.org/abs/2509.07388 |