Use of a multilayer perceptron to create a prediction model for dressing independence in a small sample at a single facility

Abstract

[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.