Knežević Aleksandar, Petković Milena, Mikov Aleksandra, Jeremić-Knežević Milica, Demeši-Drljan Čila, Bošković Ksenija, Tomašević-Todorović Snežana, Jeličić Zoran D
Srp Arh Celok Lek. 2016 Sep-Oct;144(9-10):507-13.
Identification of predictive factors for walking ability with a prosthesis, after lower limb amputation, is very important in order to define patient’s potentials and realistic rehabilitation goals, however challenging they are.
The objective of this study was to investigate whether variables determined at the beginning of rehabilitation process are able to predict walking ability at the end of the treatment using support vector machines (SVMs).
This research was designed as a retrospective clinical case series. The outcome was defined as three-leveled ambulation ability. SVMs were used for predicting model forming.
The study included 263 patients, average age 60.82 ± 9.27 years. In creating SVM models, eleven variables were included: age, gender, cause of amputation, amputation level, period from amputation to prosthetic rehabilitation, Functional Comorbidity Index (FCI), presence of diabetes, presence of a partner, restriction concerning hip or knee extension, residual limb hip extensor strength, and mobility at admission. Six SVM models were created with four, five, six, eight, 10, and 11 variables, respectively. Genetic algorithm was used as an optimization procedure in order to select the best variables for predicting the level of walking ability. The accuracy of these models ranged from 72.5% to 82.5%.
By using SVM model with four variables (age, FCI, level of amputation, and mobility at admission) we are able to predict the level of ambulation with a prosthesis in lower limb amputees with high accuracy.
下肢截肢后，识别假肢行走能力的预测因素对于确定患者的潜力和现实的康复目标非常重要，无论这些目标有多具挑战性。
本研究的目的是调查康复过程开始时确定的变量是否能够使用支持向量机（SVM）预测治疗结束时的行走能力。
本研究设计为回顾性临床病例系列。结果定义为三级步行能力。使用支持向量机进行预测模型构建。
该研究纳入了263名患者，平均年龄60.82±9.27岁。在创建支持向量机模型时，纳入了11个变量：年龄、性别、截肢原因、截肢水平、从截肢到假肢康复的时间、功能合并症指数（FCI）、糖尿病的存在、伴侣的存在、髋关节或膝关节伸展受限、残肢髋部伸肌力量以及入院时的活动能力。分别创建了六个支持向量机模型，包含四个、五个、六个、八个、十个和十一个变量。使用遗传算法作为优化程序，以选择预测行走能力水平的最佳变量。这些模型的准确率在72.5%至82.5%之间。
通过使用包含四个变量（年龄、FCI、截肢水平和入院时的活动能力）的支持向量机模型，我们能够高精度地预测下肢截肢患者使用假肢的行走水平。
