PERFORMANCE EVALUATION OF A NEXT GENERATION CLOUD-BASED BIOMEDICAL IOT APPLICATION
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Keywords
Cloud based, Support vector machine, Arrhythmia, data-driven, IoT and Healthcare
Abstract
The healthcare industry often keeps an eye on all physiological markers as With the development of data technology, healthcare across all market segments is becoming more digital, collaborative, patient-cantered, and data-driven. Service infrastructure of cloud Chronic patients need to be continuously monitored since there are less human resources and infrastructures available. Cloud-based architecture may provide the health care sector efficient ways to handle the aforementioned concerns. Our goal in this research project is to create software for a health care monitoring system that combines cloud computing and mobile technologies. 47 participants' 48 hours of data from the MIT-BIH Arrhythmia dataset were used in the study. The findings suggest that the support vector machine has achieved the highest classification accuracy for fractal features. Support vector machine outperforms the other two classifiers, feed forward and feedback neural network models. Also, it should be highlighted that the findings reported by the sensitivity of feed-forward neural networks and support vector machines, 92.08% and 90.36%, are comparable.
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