Hybrid Deep Learning Algorithm for Heart Disease Analysis Based on Diabetes

Authors

  • Baldev Singh Assistant Professor, Department of Computer Science & Engineering, Vivekananda Global University, Jaipur, India
  • Manish Joshi Assistant Professor, College of Computing Science and Information Technology, Teerthanker Mahaveer University, Moradabad, Uttar Pradesh, India
  • Abhijit Kumar Assistant Professor, Department of Data Science (CS), Noida Institute of Engineering and Technology, Greater Noida, Uttar Pradesh, India
  • Senthilkumar S. Professor & HOD, Department of Computer Science and Engineering, Presidency University, Bangalore, India

Keywords:

Heart disease, diabetic, deep learning (DL), Capsule Networks (CapsNets), feedforward propagation Neural Networks (FPNNs)

Abstract

Heart disease is a major cause of mortality, particularly for individuals with diabetes. Effective treatment and successful outcomes for patients require a timely and accurate diagnosis. Deep learning algorithms may evaluate cardiac disease, but developing a diabetes-specific algorithm is difficult. This research introduces a hybrid deep learning (DL) architecture that combines Capsule Networks (CapsNets) and Feedforward Propagation Neural Networks (FPNNs) to enhance heart disease analysis in diabetic patients. The CapsNet captures hierarchical relationships and poses of features in medical images, while FPNN produces effective learning via backpropagation and feedforward connections, making it suitable for periodic and sequential data, such as ECG signals and patient demographics. To evaluate the proposed model we utilize a medical dataset from Cleveland heart disease. Evaluation using standard metrics demonstrates that the hybrid CapsNet-FPNN outperforms the other approaches in diabetes-based heart disease analysis, achieving higher accuracy and improved classification. This hybrid architecture shows great promise in enhancing heart disease analysis for individuals with diabetes, enabling more accurate detection and diagnosis. By leveraging the strengths of CapsNets and FPNNs, this model holds the potential for improving heart disease management in diabetic patients.

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Published

04.11.2023

How to Cite

Singh, B. ., Joshi, M. ., Kumar, A. ., & S., S. . (2023). Hybrid Deep Learning Algorithm for Heart Disease Analysis Based on Diabetes. International Journal of Intelligent Systems and Applications in Engineering, 12(3s), 363–368. Retrieved from https://ijisae.org/index.php/IJISAE/article/view/3715

Issue

Vol. 12 No. 3s (2024)

Section

Research Article

License

Copyright (c) 2023 Baldev Singh, Manish Joshi, Abhijit Kumar, Senthilkumar S.

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