Cardiac Abnormalities Classification Model Using Improved Deep Learning Approach

Authors

  • Abdulrahman Arishi Computer Department, General Administration of Education in Tabuk.
  • Suma Alex Kanjramnilkunathil Department of nursing, University Hospital, Jazan University, Jazan-45142, Saudi Arabia.
  • Sudha K. Rajan Department of Nursing, Jazan University Hospital, Jazan university, Jazan, Saudi Arabia, P.O box 45142.
  • Afshan Kausar Lecturer, Department of Computer Science, College of Computer Science and Information Technology, Jazan University, Jazan-45142, Saudi Arabia.
  • Arshia Arjumand Department of Computer Science, College of Computer Science and Information Technology, Jazan University, Jazan-45142, Saudi Arabia.
  • Fred Torres-Cruz Universidad Nacional del Altiplano de Puno,

Keywords:

Deep learning, cardiac, Convolutional Neural Network, accuracy, classification

Abstract

Worldwide, deep learning (DL) is applied in the healthcare industry. In the medical data set, DL approaches aid in the prevention of cardiac illnesses and locomotor disorders. The finding of such crucial information gives researchers important new knowledge on how to apply their diagnosis as well as therapy to a specific patient. Researchers analyze vast quantities of intricate healthcare data using a variety of DL techniques, which enable medical experts to forecast disorders. The primary motivation for developing a model that identify cardiac disorders, which will help lots of people around the world. This paper offerings a model for detecting cardiac diseases. The model is made by improving the Convolutional Neural Network (CNN) termed as Custom CNN (C-CNN). The new results depict that the proposed model works better. The proposed measure of heart disease categorization performs better when compared to certain previously published approaches.

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Published

25.12.2023

How to Cite

Arishi , A. ., Kanjramnilkunathil, S. A. ., Rajan, S. K. ., Kausar, A. ., Arjumand, A. ., & Torres-Cruz, F. . (2023). Cardiac Abnormalities Classification Model Using Improved Deep Learning Approach. International Journal of Intelligent Systems and Applications in Engineering, 12(1), 360–367. Retrieved from https://ijisae.org/index.php/IJISAE/article/view/3910

Issue

Vol. 12 No. 1 (2024)

Section

Research Article

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