Deep VAE AEO: Deep Variational Auto Encoder with Artificial Ecosystem Optimizer Based Cardiovascular Disease Prediction

Authors

  • Venkateswarlu Tata Research Scholler, Department of Computer Science and Engineering, FEAT, Annamalai University, Chidambaram,
  • R. Bhavani Professor& Head, Department of Computer Science and Engineering, FEAT, Annamalai University, Chidambaram
  • S. V. N. Srinivasu Professor, Department of Computer Science and Engineering, Narasaraopeta Engineering College, Narasaraopeta
  • R. Priya Professor, Department of Computer Science and Engineering, FEAT, Annamalai University, Chidambaram

Keywords:

cardiovascular disease, auto encoder, optimization, feature selection, classifier, neural network

Abstract

Cardiovascular complications are a major reason for mortality and morbidity in such patients. As a result, special attention must be paid to the occurrence of cardiovascular complications, particularly in high-risk populations. The underlying cause of cardiac dysfunction is the interaction of biological, autonomic, and iatrogenic factors. To make a diagnosis of heart defects, a system capable of predicting the presence of heart diseases would be required. Our main motivation in this article is to develop a reliable intelligent medical system using machine learning techniques to assist in identifying a patient's heart condition and guiding a physician in giving a precise prognosis of whether or not the patient has cardiovascular disease. Deep Variational Auto Encoder of Artificial Ecosystem Optimizer (Deep VAE AEO) is used in this paper to benefit from multiple non-linear layer upon layer without so much as an information bottleneck while not getting out of hand to the identity. Furthermore, epileptic data feature selection is carried out using the Spiral Optimization method, which utilizes an improved efficiencies rad model where In order to reach the focal point, the search process follows a lognormal spiral path. Deep VAE AEO is compared to existing methodologies in terms of different parameters, and it is discovered that Deep VAE AEO achieves 97percentage accuracy, 98percentage precision, 87 percentage recall, and 82 percentage F1-score..

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Published

25.12.2023

How to Cite

Tata, V. ., Bhavani, R. ., Srinivasu, S. V. N. ., & Priya, R. . (2023). Deep VAE AEO: Deep Variational Auto Encoder with Artificial Ecosystem Optimizer Based Cardiovascular Disease Prediction. International Journal of Intelligent Systems and Applications in Engineering, 12(2), 68–78. Retrieved from https://ijisae.org/index.php/IJISAE/article/view/4222

Issue

Vol. 12 No. 2 (2024)

Section

Research Article

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