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Relationship between the number of samples and the accuracy of the prediction model for dressing independence using artificial neural networks in stroke patients

©Kaifukuki Rehabilitation Ward Association 2020 ABSTRACT Fujita T, Ohashi T, Yamane K, Yamamoto Y, Sone T, Ohira Y, Otsuki K, Iokawa K. Relationship between the number of samples and the accuracy of the prediction model for dressing independence using artificial neural networks in stroke patients. Jpn J Compr Rehabil Sci 2020; 11: 28‐34. Objective: To determine the lower limit of the number of samples that is useful for creating a prediction model on dressing independence in stroke patients by using artificial neural networks. Methods: Five datasets consisting of 120, 100, 80, 60, and 40 were created from 121 stroke patients by repeated random sampling. The models for predicting independent dressing one month after admission were created by an artificial neural network and logistic regression in each dataset from the variables upon admission to the convalescent rehabilitation ward. The accuracy of both models was compared. Results: The accuracy of the artificial neural network model was significantly higher than that of the logistic regression model in the 120, 100, and 80 patient datasets, and there were no differences in the accuracy of both models in the 60 and 40 patient datasets. Conclusion: Our results suggested that the lower limit of the number of samples for creating a useful prediction model of dressing independence by using artificial neural networks is approximately 80.