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Related Concept Videos

Steps in Outbreak Investigation01:18

Steps in Outbreak Investigation

126
In the ever-evolving field of public health, statistical analysis serves as a cornerstone for understanding and managing disease outbreaks. By leveraging various statistical tools, health professionals can predict potential outbreaks, analyze ongoing situations, and devise effective responses to mitigate impact. For that to happen, there are a few possible stages of the analysis:
126

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Updated: Jun 29, 2025

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DengueFog: A Fog Computing-Enabled Weighted Random Forest-Based Smart Health Monitoring System for Automatic Dengue

Ashima Kukkar1, Yugal Kumar2, Jasminder Kaur Sandhu3

  • 1Chitkara University Institute of Engineering and Technology, Chitkara University, Punjab 140401, India.

Diagnostics (Basel, Switzerland)
|March 27, 2024
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Summary
This summary is machine-generated.

A new DengueFog system using fog computing and a weighted random forest classifier effectively predicts and detects dengue fever. This approach shows superior performance over traditional methods for this life-threatening illness.

Keywords:
IoTcloud computingdenguefog computingrandom forest

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Area of Science:

  • Medical Informatics
  • Computer Science
  • Epidemiology

Background:

  • Dengue fever, transmitted by Aedes aegypti mosquitoes, is a critical global health issue, particularly in developing nations, due to high mortality and low diagnosis rates.
  • Dengue shares symptoms with other febrile illnesses, complicating diagnosis and necessitating advanced monitoring systems.
  • The Internet of Things (IoT), fog, and cloud computing are emerging technologies for developing sophisticated healthcare solutions.

Purpose of the Study:

  • To develop and evaluate a novel DengueFog monitoring system utilizing fog computing for the prediction and detection of dengue fever.
  • To integrate a weighted random forest (WRF) classifier within the DengueFog system for enhanced dengue infection monitoring and prediction.

Main Methods:

  • A fog computing-based system, DengueFog, was designed for real-time dengue fever monitoring and prediction.
  • A weighted random forest (WRF) classifier was employed for analyzing dengue infection data.
  • The system's performance was validated using a dataset collected from Delhi-NCR hospitals between 2016 and 2018.

Main Results:

  • The DengueFog system demonstrated high accuracy, F-value, recall, precision, and specificity, with a low error rate.
  • The proposed system, incorporating the WRF classifier, significantly outperformed traditional classification methods in dengue prediction and detection.
  • Performance metrics confirmed the system's efficacy in monitoring and predicting dengue infections.

Conclusions:

  • The DengueFog monitoring system, powered by fog computing and WRF, offers a robust and effective solution for dengue fever prediction and detection.
  • This advanced system addresses the diagnostic challenges associated with dengue, potentially improving patient outcomes.
  • The study highlights the potential of IoT and fog computing in developing advanced, accurate, and efficient disease surveillance systems.