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Area of Science:

  • Biomedical Engineering
  • Health Informatics
  • Data Science

Background:

  • The Internet of Medical Things (IoMT) is transforming healthcare by integrating connected devices into clinical workflows.
  • IoMT facilitates the collection of real-time patient data, crucial for advancing medical diagnostics and treatment.
  • Wearable devices, a key component of IoMT, offer continuous monitoring of vital signs and physiological parameters.

Purpose of the Study:

  • To propose an efficient Internet of Medical Things (IoMT)-based model for cancer detection.
  • To leverage real-time data from wearable devices for improved cancer diagnosis.
  • To enhance clinical workflow models through advanced data analytics.

Main Methods:

  • Development of an IoMT-based cancer detection model utilizing data from wearable devices.
  • Integration of real-time physiological data (heart rate, blood pressure, eye movement) into the diagnostic model.
  • Application of big data analytics for processing and interpreting the collected health information.

Main Results:

  • The proposed IoMT model demonstrates higher accuracy compared to existing cancer detection methods.
  • Real-time data collection from wearable devices provides comprehensive patient health insights.
  • The model supports enhanced analysis of patient health reports for medical professionals.

Conclusions:

  • The IoMT-based cancer detection model offers a significant advancement in diagnostic accuracy.
  • IoMT facilitates a 'big data renaissance' in healthcare, improving patient care.
  • This approach empowers doctors with better tools for analyzing patient data and optimizing treatment strategies.