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Survival study on deep learning techniques for IoT enabled smart healthcare system.

Ashok Kumar Munnangi1, Satheeshwaran UdhayaKumar2, Vinayakumar Ravi3

  • 1Department of Information Technology, Velagapudi Ramakrishna Siddhartha Engineering College (Autonomous), Vijayawada, Andhra Pradesh India.

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This study introduces a novel deep learning method for smart healthcare, improving health status recognition accuracy by 95% and reducing detection time by 18%. The AI-driven system enhances healthcare efficiency.

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Area of Science:

  • Artificial Intelligence in Healthcare
  • Deep Learning Applications
  • Internet of Things (IoT) in Medicine

Background:

  • Deep learning (DL) offers significant potential for advancing healthcare analytics.
  • Existing methods face challenges in accuracy and efficiency for real-time health monitoring.
  • Smart healthcare systems require robust data analytics for improved patient outcomes.

Purpose of the Study:

  • To review deep learning techniques in healthcare, highlighting strengths and limitations.
  • To lay the foundation for understanding DL-based data analytics in smart healthcare.
  • To provide insights for healthcare professionals and policymakers on current DL trends.

Main Methods:

  • Developed a deep learning-based technique for sensor displacement extraction and abnormality prediction.
  • Introduced the Moran Autocorrelation and Regression-based Elman Recurrent Neural Network (MAR-ERNN).
  • Addressed the vanishing gradient issue in Recurrent Neural Networks (RNNs) for improved temporal and spatial analysis.

Main Results:

  • Experimental results demonstrate the feasibility and effectiveness of the proposed MAR-ERNN method.
  • Achieved a 95% improvement in accuracy for health status activity recognition.
  • Reduced execution time by 18% compared to existing methods.

Conclusions:

  • The MAR-ERNN model performs effectively in recognizing health status through activity recognition.
  • The developed IoT-enabled smart healthcare system enhances accuracy and minimizes detection time.
  • The study contributes to the advancement of efficient and accurate smart healthcare solutions.