Practical Machine Learning Model to Predict the Recovery of Motor Function in Patients with Stroke

Background: Machine learning (ML) is an artificial intelligence technique in which a system learns patterns and rules from a given data. Objectives: The objective of the study was to investigate the potential of ML for predicting motor recovery in stroke patients. Methods: We analyzed data from 833 consecutive stroke patients using 3 ML algorithms: deep neural network (DNN), random forest, and logistic regression. We created a practical ML model using the most common data measured in almost all rehabilitation hospitals as input data. Demographic and clinical data, including modified Brunnstrom classification (MBC) and functional ambulation classification (FAC), were collected when patients were transferred to the rehabilitation unit (8-30 days) and 6 months after stroke onset and were used as input data. Motor outcomes at 6 months after stroke onset of the affected upper and lower extremities were classified according to MBC and FAC, respectively. Patients with an MBC of <5 and an FAC of <4 at 6 months after stroke onset were considered to have a "poor" outcome, whereas those with MBC >= 5 and FAC >= 4 were considered to have a "good" outcome. Results: The area under the curve (AUC) for the DNN model for predicting motor function was 0.836 for the upper and lower limb motor functions. For the random forest and logistic regression models, the AUCs were 0.736 and 0.790 for the upper and lower limb motor functions, respectively. The AUCs for the random forest and logistic regression models were 0.741 and 0.795 for the upper and lower limb motor functions, respectively. Conclusion: Although we used simple and common data that can be obtained in clinical practice as variables, our DNN algorithm was useful for predicting motor recovery of the upper and lower extremities in stroke patients during the recovery phase.