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Instance Selection Algorithms for Predictive Modelling in Telehealth Applications.

Fabian Wiesmüller1,2,3, Dieter Hayn1,3, Florian Hoffmann4

  • 1AIT Austrian Institute of Technology, Graz, Austria.

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|January 25, 2024
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Summary
This summary is machine-generated.

Instance selection significantly enhances machine learning accuracy in diabetes telehealth. Applying optimal algorithms to time-series data improves patient dropout prediction and data management for health professionals.

Keywords:
Instance selectionpredictive modellingtelehealthtraining data selection

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Area of Science:

  • Health Informatics
  • Machine Learning
  • Data Science

Background:

  • Telehealth services generate vast amounts of patient data.
  • Effective data management is crucial for healthcare professionals.
  • Machine learning offers potential solutions for analyzing telehealth data.

Purpose of the Study:

  • To implement and validate instance selection algorithms for diabetes telehealth time-series data.
  • To assess the impact of instance selection on machine learning model accuracy for dropout prediction.
  • To identify optimal instance selection methods for telehealth data.

Main Methods:

  • Analysis of intrinsic, supervised, and unsupervised instance selection algorithms.
  • Application of selected algorithms to time-series data from a diabetes telehealth service.
  • Validation using a random forest model for dropout prediction.

Main Results:

  • Instance selection significantly impacted the accuracy of the random forest dropout prediction model.
  • A One Class Support Vector Machine achieved the best results.
  • The area under the receiver operating curve improved from 69.91% to 75.88%.

Conclusions:

  • Instance selection is a valuable technique for improving machine learning accuracy in telehealth.
  • The potential of instance selection in telehealth has been underexplored.
  • Optimized instance selection can enhance the management and analysis of telehealth data.