Recurrent Shephard convolutional neural network for heart disease prediction in spark framework Online publication date: Fri, 09-Aug-2024
by Jayasri Kotti; Jamal Mohammed Saira Banu; Ramanathan Lakshmanan
International Journal of Ad Hoc and Ubiquitous Computing (IJAHUC), Vol. 46, No. 4, 2024
Abstract: The heart is the most essential organ in the human body, and so, heart disease should be predicted more accurately, even a minute mistake in prediction leads to death. To deal with the issue, this paper introduced a new technique, named recurrent Shephard convolutional neural network (R_ShCNN) for heart disease prediction. Initially, big data is considered as input and it is passed to data partitioning, which is carried out by utilising deep embedded clustering (DEC). Here, pre-processing is executed by missing data imputation and linear normalisation, whereas feature fusion is performed using the Harmonic mean measure with deep Q network (DQN). Lastly, heart disease is predicted by utilising the proposed R_ShCNN, which is attained by fusing the Shephard convolutional neural network (ShCNN) and deep recurrent neural network (DRNN). The experimental result shows that R_ShCNN accomplished the specificity of 92.3%, accuracy of 89.5%, and sensitivity of 90.9%.
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