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

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.
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