Machine-learning prediction of self-care activity by grip strengths of both hands in poststroke hemiplegia

To investigate the relationships between grip strengths and self-care activities in stroke patients using a non-linear support vector machine (SVM). Overall, 177 inpatients with poststroke hemiparesis were enrolled. Their grip strengths were measured using the Jamar dynamometer on the first day of rehabilitation training. Self-care activities were assessed by therapists using Functional Independence Measure (FIM), including items for eating, grooming, dressing the upper body, dressing the lower body, and bathing at the time of discharge. When each FIM item score was ≥6 points, the subject was considered independent. One thousand bootstrap grip strength datasets for each independence and dependence in self-care activities were generated from the actual grip strength. Thereafter, we randomly assigned the total bootstrap datasets to 90% training and 10% testing datasets and inputted the bootstrap training data into a non-linear SVM. After training, we used the SVM algorithm to predict a testing dataset for cross-validation. This validation procedure was repeated 10 times. The SVM with grip strengths more accurately predicted independence or dependence in self-care activities than the chance level (mean ± standard deviation of accuracy rate: eating, 0.71 ± 0.04, P < .0001; grooming, 0.77 ± 0.03, P < .0001; upper-body dressing, 0.75 ± 0.03, P < .0001; lower-body dressing, 0.72 ± 0.05, P < .0001; bathing, 0.68 ± 0.03, P < .0001). Non-linear SVM based on grip strengths can prospectively predict self-care activities.