MACHINE LEARNING ALGORITHMS FOR PREDICTION OF AMBULATION AND WHEELCHAIR TRANSFER ABILITY IN SPINA BIFIDA.

Abstract

Objective: To determine which statistical techniques enhance our ability to predict ambulation and transfer ability in people with spina bifida (SB).

Design: Retrospective cohort study SETTING: 35 United States outpatient SB clinic sites PARTICIPANTS: individuals (n=4,589) with SB ages 5-73 (median age =13.59) INTERVENTION: not applicable MAIN OUTCOME MEASURE: ambulation ability, which consisted of the following categories: community ambulators, household ambulators, therapeutic ambulators, and non-ambulators SECONDARY OUTCOME: wheelchair transfer ability, as defined by the ability to transfer in and out of a wheelchair unassisted RESULTS: A Recurrent Neural Network (RNN) utilizing a multilayer perceptron discarded 76 cases during case processing, resulting in 4513 that were run through the RNN. The predictions in the resulting testing dataset were 83.22% accurate. Recall was 93.21% for community ambulators, 10.00% for household ambulators, 23.96% for therapeutic ambulators, and 76.70% for non-ambulators. Precision was 85.34% for community ambulators, 16.05% for household ambulators,16.67% for therapeutic ambulators, and 93.47% for non-ambulators. Total predictions included 68.39% for community ambulators, 2.25% for household ambulators, 3.83% for therapeutic ambulators, and 25.53% for non-ambulators. Correspondingly, the model accurately classified 70% of wheelchair transfers, while correctly identifying 97.3% of those able to transfer unassisted.

Conclusions: RNN models hold promise for prediction of functional outcomes such as ambulation and transfer ability in people with SB, particularly for community and non-ambulators. Compared to previous work utilizing traditional logistic regression approaches which misclassified 16% of cases, the RNN resulted in greater prediction accuracy with fewer than 7% of cases misclassified.