Deep Featured Adaptive Dense Net Convolutional Neural Network Based Cardiac Risk Prediction in Big Data Healthcare Environment

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Proposed dense net CNN
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Published: Jan 31, 2023
DOI: https://doi.org/10.17762/ijritcc.v11i2s.6065
Keywords:
Cardiac Risk Prediction, Cardiac Deficiency Prediction rate (CDPr), Cross-Over Mutual Scaling Feature Selection Model (CMSFS), Dense Net Convolution Neural Network (DN-CNN), Disease Affection Rate (DAR)

Main Article Content

Visumathi J
Department of Computer Science and Engineering, Veltech Rangarajan Dr. Sagunthala R&D Institute of Science and Technology, Chennai-600062, Tamil Nadu, India
R. Rajalakshmi
Department of Electronics and Communication Engineering, Panimalar Engineering college, Poonamallee, Chennai, Tamil Nadu 600123, India
J. Jeffin Gracewell
Department of Electronics and Communication Engineering, Saveetha Engineering College, Chennai, Tamil Nadu 602105,India
Suganthi
Department of Electronics and Communication Engineering, Panimalar Engineering college, Poonamallee, Chennai-600123, Tamil Nadu, India
R. Kuppuchamy
Department of MCA, PSNA College of Engineering and Technology, Dindigul-624622, Tamil Nadu
S. Sankar Ganesh
Department of Electronics and Communication Engineering, P.S.R Engineering College, Sivakasi, Sevalpatti, Tamil Nadu, India

Abstract

In recent days, cardiac vascular disease has been one of the deadliest health-affecting factors causing sudden death. So, the importance of early risk prediction through feature analysis has become a big problem in data analysis because more nonlinear time series data increase the feature dimension. Irrelevant feature dimension scaling affects the prediction accuracy and leads to classification inaccuracy. To resolve this problem, we propose an Enhanced Healthcare data analysis model for cardiac data prediction using an adaptive Deep Featured Adaptive Convolution Neural Network for early risk identification. Initially, the preprocessing was augmented to formalize the time series data collected from the CVD-DS dataset. Then the feature evaluation was carried out with the Relative Subset Clustering (RSC) approach. The Cardiac Deficiency Prediction rate (CDPr) was estimated to identify the relational feature to subset margins. Based on the CDPr weight the feature is extracted using Cross-Over Mutual Scaling Feature Selection Model (CMSFS). The selected features get with a deep neural classifier based on logical neurons. They are then constructed into a Dense Net Convolution Neural Network (DN-CNN) classifier to feed forward the feature values and predict the Disease Affection Rate (DAR) by class category. The proposed system produces high prediction accuracy in classification, precision, and recall rate to support premature treatment for early cardiac disease risk prediction..

Article Details

How to Cite
J, V. ., Rajalakshmi, R. ., Gracewell, J. J. ., Suganthi, S., Kuppuchamy, R. ., & Ganesh, S. S. . (2023). Deep Featured Adaptive Dense Net Convolutional Neural Network Based Cardiac Risk Prediction in Big Data Healthcare Environment. International Journal on Recent and Innovation Trends in Computing and Communication, 11(2s), 219–229. https://doi.org/10.17762/ijritcc.v11i2s.6065
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Vol. 11 No. 2s (2023)
Section
Articles

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