AI-Based Intelligent System for Healthcare Application Using Edge-Based Neural Random Back Propagation Technique

Natesh Mahadev; Shankar R; Sowmya V L; Anitha Premkumar; Rajesh Natarajan; Thangarasu N; Shashi Kant Gupta

doi:10.54392/irjmt2532

Authors

Natesh Mahadev Department of Computer Science and Engineering, Vidyavardhaka College of Engineering, Mysore, Karnataka, India. Author
Shankar R Department of Computer Science Engineering, BMS Institute of Technology & Management, Bengaluru - 560119, India. Author https://orcid.org/0000-0001-6389-2198
Sowmya V L Department of Artificial Intelligence & Machine Learning, BMS Institute of Technology & Management, Bengaluru 560119, India Author
Anitha Premkumar Department of Information Technology, Manipal Institute of Technology Bengaluru, Manipal Academy of Higher Education, Manipal, Bengaluru 576104, India Author
Rajesh Natarajan Information Technology Department, College of Computing and Information Sciences, University of Technology and Applied Sciences, Al-Aqr, Shinas 324, Oman. Author https://orcid.org/0000-0003-1255-9621
Thangarasu N Department of Computer Science, Karpagam Academy of Higher Education, Coimbatore 641021, Tamil Nadu, India. Author
Shashi Kant Gupta Centre for Research Impact & Outcome, Chitkara University Institute of Engineering and Technology, Chitkara University, Rajpura, 140401, Punjab, India Author

DOI:

https://doi.org/10.54392/irjmt2532

Keywords:

Intelligent system, Information and communication technology (ICT), World Health Organization (WHO), Diabetes, Artificial Intelligence (AI), Least Absolute Shrinkage and Selection Operator (LASSO), Edge Based neural Random back propagation (EB-NRBP)

Abstract

The rising prevalence of diabetes, driven by dietary changes and reduced physical activity, is a leading cause of mortality worldwide. Early diagnosis is critical to managing this chronic condition. This study proposes an AI-based intelligent system for early diabetes detection using Edge-Based Neural Random Backpropagation (EB-NRBP). The EB-NRBP model leverages feature selection via the Least Absolute Shrinkage and Selection Operator (LASSO) to enhance the regularization of the classification process. This approach optimizes the classifier’s cost function and accelerates its development through Random Forward Gradients in conjunction with Edge-Based neural networks. The model's performance was compared with conventional methods, demonstrating a significant improvement in classification accuracy. The EB-NRBP model achieved a high success rate of 98%, outperforming traditional techniques in terms of efficiency and precision. This AI-based system presents a promising solution for the early diagnosis of diabetes, offering higher accuracy and faster detection compared to existing methods. It holds potential for integration into healthcare applications, enhancing early intervention and improving patient outcomes.

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