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Related Concept Videos

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Related Experiment Video

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Machine Learning-Based Cough Tone Classification: Diagnostic Exploration of Chronic Obstructive Pulmonary Disease and Respiratory Tract Infections
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Context Relevant Prediction Model for COPD Domain Using Bayesian Belief Network.

Hamid Mcheick1, Lokman Saleh2, Hicham Ajami3

  • 1Computer Science Department, University of Quebec at Chicoutimi, Chicoutimi, QC G7H 2B1, Canada. hamid_mcheick@uqac.ca.

Sensors (Basel, Switzerland)
|June 24, 2017
PubMed
Summary

This study introduces an advanced system for early detection of Chronic Obstructive Pulmonary Disease (COPD) exacerbations. The developed model accurately predicts exacerbations using a novel attribute selection and Bayesian network approach, improving patient care.

Keywords:
Bayesian Belief Networkchronic pulmonary diseasecontext-aware applicationshealth care systemubiquitous and ambient computing

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Area of Science:

  • Medical Informatics
  • Artificial Intelligence in Healthcare
  • Respiratory Medicine

Background:

  • Context-aware systems have been explored to aid patients with incurable diseases.
  • Numerous studies exist on Chronic Obstructive Pulmonary Disease (COPD), yet early detection of exacerbations remains difficult.
  • Deriving relevant clinical attributes for exacerbation prediction is a significant challenge.

Purpose of the Study:

  • To develop an efficient algorithm for selecting relevant attributes for COPD exacerbation prediction.
  • To propose an extension of the Helper Context-Aware Engine System (HCES) for COPD exacerbation prediction.
  • To create an effective prediction model that overcomes limitations of existing methods.

Main Methods:

  • Utilized an efficient algorithm for attribute selection and discretization.
  • Employed a Bayesian network with the TAN algorithm to model symptom dependencies, overcoming the independency hypothesis of Naïve Bayes.
  • Integrated discretization, attribute selection, dependency modeling, and attribute ordering into a comprehensive prediction model.
  • Developed and validated a computer-aided support application (HCES) to implement the model.

Main Results:

  • The proposed HCES system demonstrated high accuracy in predicting COPD exacerbations.
  • The developed model effectively organized pertinent attributes by priority based on their impact.
  • The system achieved a promising Area Under the Receiver Operating Characteristic (AUC) of 81.5%.

Conclusions:

  • The integrated approach using Bayesian networks and attribute selection provides an effective prediction model for COPD exacerbations.
  • The HCES system offers a validated computer-aided tool for supporting clinical decision-making in COPD management.
  • The findings highlight the potential of advanced algorithms in improving early detection and management of COPD exacerbations.