Abstract
Recently IoT, i.e., Internet of Things, is gaining importance for various fields; e-Healthcare is one of the IoT applications that can be used in developing different kinds of services like storage of data, managing resources, and power, creating and computing services, etc. Fog Computing and Cloud Computing play vital roles in critical data and IoT implementations. The extension of Cloud Computing i.e., termed Fog Computing; is suitable in IoT implementations in the real world in recent days. Fog Computing has been established to overcome the limitations relating to Cloud Computing and its implementations. In this paper, HeartFog, an intelligent real-time decision support system framework, based on appropriate analysis and IoT for the remote detection of cardiovascular disease, is proposed to enhance the accuracy in diagnosis with unexplained data. This proposed work is evaluated in terms of training accuracy, test accuracy, arbitration time, latency, execution time, and power consumption. This proposed work can be employed in the development of next-generation of IoT models and services. From the experiments, it is found to be a suitable framework for the instantaneous diagnosis of heart patients remotely.
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Pati, A., Parhi, M., Pattanayak, B.K. (2022). HeartFog: Fog Computing Enabled Ensemble Deep Learning Framework for Automatic Heart Disease Diagnosis. In: Mishra, D., Buyya, R., Mohapatra, P., Patnaik, S. (eds) Intelligent and Cloud Computing. Smart Innovation, Systems and Technologies, vol 286. Springer, Singapore. https://doi.org/10.1007/978-981-16-9873-6_4
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DOI: https://doi.org/10.1007/978-981-16-9873-6_4
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