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ESMAC BEST PAPER 2017: Using machine learning to overcome challenges in GMFCS level assignment.

Michael H Schwartz1, Meghan E Munger2

  • 1Gillette Children's Specialty Healthcare, MN, United States; University of Minnesota, Department of Orthopaedic Surgery, MN, United States.

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Summary
This summary is machine-generated.

A new random forest classifier accurately predicts Gross Motor Function Classification System (GMFCS) levels I-IV using patient-reported abilities from the Gillette Functional Assessment Questionnaire (FAQ), improving upon current methods.

Keywords:
AccuracyAlgorithmCerebral palsyFunctionGMFCSGaitPredictionRandom forestReliability

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Area of Science:

  • Pediatric rehabilitation
  • Machine learning in healthcare
  • Functional assessment

Background:

  • The Gross Motor Function Classification System (GMFCS) is crucial for classifying motor function in cerebral palsy.
  • Current GMFCS assignment methods have limitations, including reliance on clinical observation and age-dependency.
  • Patient-reported functional abilities offer a valuable, yet underutilized, data source for classification.

Purpose of the Study:

  • To develop and validate a machine learning model for predicting GMFCS levels I-IV.
  • To assess the accuracy of a random forest classifier using self-reported functional abilities.
  • To overcome limitations of existing GMFCS assignment protocols.

Main Methods:

  • Utilized a random forest classifier algorithm.
  • Input data comprised patient-reported abilities from the Gillette Functional Assessment Questionnaire (FAQ).
  • Model performance was evaluated using true positive rates (TPR) and receiver operating characteristic (ROC) curves for GMFCS levels I-IV.

Main Results:

  • The random forest classifier achieved high accuracy, with TPR ranging from 83% to 91% for GMFCS levels I-IV.
  • Area under the ROC curve exceeded 0.96 for all GMFCS levels.
  • Misclassification by more than one GMFCS level occurred in only 1.2% of cases.

Conclusions:

  • A random forest classifier accurately predicts GMFCS levels I-IV based on self-reported functional abilities.
  • This approach offers a more comprehensive and less age-dependent method for GMFCS assignment.
  • Future research should investigate potential inter-center variations in classifier performance.