Cardiac Stroke Prediction Framework using Hybrid Optimization Algorithm under DNN

Authors

  • N. K. Al-Shammari Mechanical Engineering Department, University of Hail, Saudi Arabia
  • A. A. Alzamil Electrical Engineering Department, College of Engineering, University of Hail, Saudi Arabia
  • M. Albadarn Electrical Engineering Department, College of Engineering, University of Hail, Saudi Arabia
  • S. A. Ahmed School of Computing and Information Technology, REVA University, India
  • M. B. Syed School of Computing and Information Technology, REVA University, India
  • A. S. Alshammari Department of Electrical Engineering, University of Hail, Saudi Arabia
  • A. M. Gabr Physical Therapy Department, Faculty of Applied Medical Sciences, University of Hail, Saudi Arabia
Volume: 11 | Issue: 4 | Pages: 7436-7441 | August 2021 | https://doi.org/10.48084/etasr.4277

Abstract

Heart weakness and restricted blood flow into the cavities can cause a range of strokes from mild to severe Heart strokes are primary caused due to the fat deposited on artery walls. The process reduces the intake of blood and internally causes a pseudo vacuum of air bubbles leading to a stroke which can be identified with high-end instrumentations. In this article, a detailed evaluation is processed with a Hybrid Optimization Algorithm (HOA). In the proposed technique, data are preprocessed using a label encoder and the missing values of the dataset are filled. Whale Optimization Algorithm (WOA) and Crow Search Algorithm(CSA) extract inter-connected patterns and learning features using a dedicated Deep Neural Networking (DNN) support. The proposed Hybrid Optimization Algorithm extracts features and the resultant values demonstrate a high accuracy range of 97.34%.

Keywords:

cardiac stroke prediction, cardiovascular disease, whale optimization algorithm, crow search algorithm, hybrid optimization algorithm

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[1]
N. K. Al-Shammari, “Cardiac Stroke Prediction Framework using Hybrid Optimization Algorithm under DNN”, Eng. Technol. Appl. Sci. Res., vol. 11, no. 4, pp. 7436–7441, Aug. 2021.

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