Abstract
Due to advancement of new edge medical technologies, many methods have been applied to solve medical issues including machine learning approach. Cardiac Arrhythmia is one of the common diseases which can be solved using various machine learning approaches. There are many approaches which have already been introduced to classify arrhythmia and abnormality detection. This paper has a solution, introduces supervised and unsupervised models in which supervised models generate a good classification result. However, in this paper, we have also introduced a deep neural network classifier and used for prediction of arrhythmia if present based on some predefined value. In this paper, we have also connected it to the user interface to which the native users can check the level of arrhythmia.
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Acknowledgments
Authors wish to communicate a profound feeling of appreciation and gratitude to the Internal Guide, Prof. Hema Raut for her direction, help, and useful suggestions, which helped in completing the project work successfully. This experience of work led them to self-development and exposure to the field knowledge.
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Gupta, A., Banerjee, A., Babaria, D., Lotlikar, K., Raut, H. (2022). Prediction and Classification of Cardiac Arrhythmia. In: Shakya, S., Balas, V.E., Kamolphiwong, S., Du, KL. (eds) Sentimental Analysis and Deep Learning. Advances in Intelligent Systems and Computing, vol 1408. Springer, Singapore. https://doi.org/10.1007/978-981-16-5157-1_41
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DOI: https://doi.org/10.1007/978-981-16-5157-1_41
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Online ISBN: 978-981-16-5157-1
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