Scholarlink

Diabetic Foot Ulcers (DFUs) pose significant challenges in diabetes management, often leading to severe complications. This study presents a novel approach for early DFU prediction using DeepQ-CNN algorithm. The research integrates clinical, demographic, and lifestyle data to build a comprehensive dataset, which the DeepQ-CNN model analyzes using deep neural networks and reinforcement learning to discern complex patterns. The study’s objectives include dataset development, feature extraction, and iterative optimization of the DeepQ-CNN hybrid model. The model’s predictive performance is assessed through metrics like accuracy, sensitivity, specificity, and AUC-ROC, validated across various dataset subsets. Results show that the DeepQ-CNN model excels in predicting DFUs, with a notable accuracy rate of 97.82%. This underscores its potential as a vital tool for early risk identification, aiding in timely interventions that could improve patient outcomes and reduce healthcare costs. The study highlights the importance of leveraging advanced deep learning techniques for better predictive modeling in diabetic complications, offering significant implications for both academic research and practical diabetes care.

错误：搜索内容不能为空，请输入英文关键词
错误：关键词超出字数限制，请精简
高级检索

Predictive Modeling of Diabetic Foot Ulcers: An Advanced Approach Using Hybrid DeepQ-CNN

  • 会议论文

  • 发表日期：

    2025年

查看全文
  • Rajasree Rajamohanan,
  • S. Arul Dharshini,
  • J. Gokulapriya

摘要

Diabetic Foot Ulcers (DFUs) pose significant challenges in diabetes management, often leading to severe complications. This study presents a novel approach for early DFU prediction using DeepQ-CNN algorithm. The research integrates clinical, demographic, and lifestyle data to build a comprehensive dataset, which the DeepQ-CNN model analyzes using deep neural networks and reinforcement learning to discern complex patterns. The study’s objectives include dataset development, feature extraction, and iterative optimization of the DeepQ-CNN hybrid model. The model’s predictive performance is assessed through metrics like accuracy, sensitivity, specificity, and AUC-ROC, validated across various dataset subsets. Results show that the DeepQ-CNN model excels in predicting DFUs, with a notable accuracy rate of 97.82%. This underscores its potential as a vital tool for early risk identification, aiding in timely interventions that could improve patient outcomes and reduce healthcare costs. The study highlights the importance of leveraging advanced deep learning techniques for better predictive modeling in diabetic complications, offering significant implications for both academic research and practical diabetes care.