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

Michael H. Schwartz, Meghan E. Munger

Research output: Contribution to journalArticlepeer-review

2 Scopus citations

Abstract

We used the random forest classifier to predict Gross Motor Function Classification System (GMFCS) levels I–IV from patient reported abilities recorded on the Gillette Functional Assessment Questionnaire (FAQ). The classifier exhibited outstanding accuracy across GMFCS levels I–IV, with 83%–91% true positive rate (TPR), area under the receiver operation characteristic (ROC) curve greater than 0.96 for all levels, and misclassification by more than one level only occurring 1.2% of the time. This new approach to GMFCS level assignment overcomes several difficulties with the current method: (i) it is based on a broad spectrum of functional abilities, (ii) it resolves functional ability profiles that conflict with existing GMFCS level definitions, (iii) it is based entirely on self-reported abilities, and (iv) it removes complex age dependence. Further work is needed to examine inter-center differences in classifier performance—which would most likely reflect interpretive differences in GMFCS level definitions between centers.

Original languageEnglish (US)
Pages (from-to)290-295
Number of pages6
JournalGait and Posture
Volume63
DOIs
StatePublished - Jun 2018

Keywords

  • Accuracy
  • Algorithm
  • Cerebral palsy
  • Function
  • GMFCS
  • Gait
  • Prediction
  • Random forest
  • Reliability

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