Suzuki Makoto, Sugimura Seiichiro, Suzuki Takako, Sasaki Shotaro, Abe Naoto, Tokito Takahide, Hamaguchi Toyohiro
Faculty of Health Sciences, Tokyo Kasei University, Saitama.
Department of Rehabilitation, St. Marianna University Toyoko Hospital, Kanagawa.
Medicine (Baltimore). 2020 Mar;99(11):e19512. doi: 10.1097/MD.0000000000019512.
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.
使用非线性支持向量机(SVM)研究中风患者握力与自我护理活动之间的关系。总体而言,纳入了177例中风后偏瘫住院患者。在康复训练的第一天,使用Jamar测力计测量他们的握力。治疗师使用功能独立性测量(FIM)评估自我护理活动,包括出院时的进食、修饰、上身穿衣、下身穿衣和洗澡项目。当每个FIM项目得分≥6分时,该受试者被认为具有独立性。从实际握力中生成了1000个用于自我护理活动中独立和依赖情况的自助握力数据集。此后,我们将所有自助数据集随机分配为90%的训练数据集和10%的测试数据集,并将自助训练数据输入到非线性支持向量机中。训练后,我们使用支持向量机算法预测测试数据集进行交叉验证。该验证过程重复10次。与随机水平相比,基于握力的支持向量机更准确地预测了自我护理活动中的独立或依赖情况(准确率的平均值±标准差:进食,0.71±0.04,P<.0001;修饰,0.77±0.03,P<.0001;上身穿衣,0.75±0.03,P<.0001;下身穿衣,0.72±0.05,P<.0001;洗澡,0.68±0.03,P<.0001)。基于握力的非线性支持向量机可以前瞻性地预测自我护理活动。