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Semi-Supervised Clustering-Based DANA Algorithm for Data Gathering and Disease Detection in Healthcare Wireless

Anurag Sinha1, Turki Aljrees2, Saroj Kumar Pandey3

  • 1Department of Computer Science and Information Technology, IIndira Gandhi National Open University, New Delhi 110068, India.

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|January 11, 2024
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Summary
This summary is machine-generated.

This study introduces an innovative approach for wireless sensor networks (WSNs) in healthcare, improving disease detection through signal processing. The DANA algorithm and semi-supervised clustering enhance data collection efficiency and patient monitoring reliability.

Keywords:
KGS theoryWSNclusteringdata gatheringdata mininghealthcaresignal processing

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Area of Science:

  • Biomedical Engineering
  • Computer Science
  • Signal Processing

Background:

  • Wireless sensor networks (WSNs) are crucial for continuous patient monitoring and early disease detection in healthcare.
  • Existing WSN data collection methods face challenges in precision, energy efficiency, and adaptability for disease diagnosis.

Purpose of the Study:

  • To introduce an innovative data collection approach for healthcare WSNs using signal processing for disease detection.
  • To enhance the precision, effectiveness, and reliability of WSNs in healthcare monitoring and early diagnosis.

Main Methods:

  • Leveraging the DANA (data aggregation using neighborhood analysis) algorithm for optimized energy consumption and dynamic route adjustment.
  • Implementing a semi-supervised clustering-based model utilizing both labeled and unlabeled data for robust clustering.
  • Conducting extensive simulations and practical deployments for experimental validation.

Main Results:

  • Demonstrated significant improvements in data quality, energy efficiency, and disease detection accuracy compared to conventional techniques.
  • The DANA algorithm optimized energy consumption and prolonged sensor node lifetimes.
  • The semi-supervised clustering model provided a more robust and adaptable data clustering technique.

Conclusions:

  • The combined DANA algorithm and semi-supervised clustering model offer a compelling solution for healthcare WSNs.
  • This approach enhances responsiveness and reliability in disease diagnosis through signal processing.
  • The research advances healthcare monitoring systems, promoting early diagnosis and improved patient care.