[Purpose] This study aimed to assess the accuracy of a prediction model for dressing
independence created with a multilayer perceptron in a small sample at a single facility.
[Participants and Methods] This retrospective observational study included 82 first-stroke
patients. The prediction models for dressing independence at hospital discharge were
created using a multilayer perceptron, logistic regression, and a decision tree, and
compared for predictive accuracy. Age, dressing performance, trunk function, visuospatial
perception, balance, and cognitive function at admission were used as variables. [Results]
The area under the receiver operating characteristic curve, classification accuracy,
sensitivity, specificity, positive-predictive value, and negative-predictive value for
training data were highest with the multilayer perceptron model. Cochran’s Q and multiple
comparison tests revealed a significant difference between logistic regression and
multilayer perceptron models. Testing of data in 10-fold cross-validation yielded the same
results, except for sensitivity. [Conclusion] The present study suggested that higher
accuracy could be expected with a multilayer perceptron than with logistic regression and
a decision tree when creating a prediction model for independence of activities of daily
living in a small sample of stroke patients.