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Integration of Digital Twins and Artificial Intelligence for Classifying Cardiac Ischemia

Mohamed Ammar1,*, Hamed Al-Raweshidy2,*

1 Department of Mechanical and Aerospace, Brunel University London, Uxbridge, UB83PH, UK
2 Department of Electrical and Electronic, Brunel University London, Uxbridge, UB83PH, UK

* Corresponding Authors: Mohamed Ammar. Email: email; Hamed Al-Raweshidy. Email: email

Journal on Artificial Intelligence 2023, 5, 195-218. https://doi.org/10.32604/jai.2023.045199

Abstract

Despite advances in intelligent medical care, difficulties remain. Due to its complicated governance, designing, planning, improving, and managing the cardiac system remains difficult. Oversight, including intelligent monitoring, feedback systems, and management practises, is unsuccessful. Current platforms cannot deliver lifelong personal health management services. Insufficient accuracy in patient crisis warning programmes. No frequent, direct interaction between healthcare workers and patients is visible. Physical medical systems and intelligent information systems are not integrated. This study introduces the Advanced Cardiac Twin (ACT) model integrated with Artificial Neural Network (ANN) to handle real-time monitoring, decision-making, and crisis prediction. THINGSPEAK is used to create an IoT platform that accepts patient sensor data. Importing these data sets into MATLAB allows display and analysis. A myocardial ischemia research examined Health Condition Tracking’s (HCT’s) potential. In the case study, 75% of the training sets (Xt), 15% of the verified data, and 10% of the test data were used. Training set feature values (Xt) were given with the data. Training, Validation, and Testing accuracy rates were 99.9%, 99.9%, and 99.9%, respectively. General research accuracy was 99.9%. The proposed HCT system and Artificial Neural Network (ANN) model gather historical and real-time data to manage and anticipate cardiac issues.

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Cite This Article

M. Ammar and H. Al-Raweshidy, "Integration of digital twins and artificial intelligence for classifying cardiac ischemia," Journal on Artificial Intelligence, vol. 5, pp. 195–218, 2023. https://doi.org/10.32604/jai.2023.045199



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