Predictive Modeling for Diabetic Foot Ulcer Detection: A Comparative Study of RNNs and ANNs Approaches

• Author(s): Penil Jones Nadar ; Santhosh Singh ; Rimsy Dua
• Paper ID: 1705478
• Page: 73-77
• Published Date: 03-02-2024
• Published In: Iconic Research And Engineering Journals
• Publisher: IRE Journals
• e-ISSN: 2456-8880
• Volume/Issue: Volume 7 Issue 8 February-2024

Abstract

Diabetic foot ulcers (DFUs) pose a significant challenge in healthcare due to their potential complications. Predictive modeling techniques, specifically Recurrent Neural Networks (RNN) and Artificial Neural Networks (ANN), have emerged as promising tools for early detection and prevention of DFUs. This paper presents a comprehensive comparative study of RNN and ANN approaches in the context of diabetic foot ulcer prediction. The study delves into the fundamental concepts of predictive modeling, elucidates the intricacies of RNN and ANN algorithms, and conducts a meticulous comparative analysis of their effectiveness in DFU detection. Key formulas and equations pertinent to these approaches are explored, providing a practical understanding for implementation. Critical thinking questions are addressed, probing the advantages of RNN over ANN, the impact of diverse data sets on model accuracy, and the challenges faced during real-world implementation. Common mistakes encountered in predictive modeling are identified, accompanied by effective solutions to enhance accuracy and reliability. The paper incorporates real-life examples and comparative case studies, shedding light on the practical application of RNN and ANN in healthcare settings. This research not only synthesizes existing knowledge but also anticipates future trends in healthcare predictive modeling. By offering a structured analysis of RNN and ANN methodologies, this study provides valuable insights for healthcare professionals, researchers, and policymakers striving to mitigate the impact of DFUs. The findings contribute to the ongoing discourse on the application of advanced technologies in diabetic care, paving the way for enhanced patient outcomes and reduced healthcare burdens.

Keywords

Revelation of DFU genres, Authentication, Supervised Learning Model, Revelation, Detection, Deep Learning

Citations

IRE Journals:
Penil Jones Nadar , Santhosh Singh , Rimsy Dua "Predictive Modeling for Diabetic Foot Ulcer Detection: A Comparative Study of RNNs and ANNs Approaches" Iconic Research And Engineering Journals Volume 7 Issue 8 2024 Page 73-77

IEEE:
Penil Jones Nadar , Santhosh Singh , Rimsy Dua "Predictive Modeling for Diabetic Foot Ulcer Detection: A Comparative Study of RNNs and ANNs Approaches" Iconic Research And Engineering Journals, 7(8)