Srpski arhiv za celokupno lekarstvo 2016 Volume 144, Issue 9-10, Pages: 507-513
https://doi.org/10.2298/SARH1610507K
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Factors that predict walking ability with a prosthesis in lower limb amputees
Knežević Aleksandar (Faculty of Medicine, Novi Sad + Clinical Centre of Vojvodina, Medical Rehabilitation Clinic, Novi Sad)
Petković Milena (Faculty of Technical Sciences, Novi Sad)
Mikov Aleksandra (Faculty of Medicine, Novi Sad + Institute for Children and Youth Health Care of Vojvodina, Novi Sad)
Jeremić-Knežević Milica (Faculty of Medicine, Novi Sad)
Demeši-Drljan Čila (Faculty of Medicine, Novi Sad + Institute for Children and Youth Health Care of Vojvodina, Novi Sad)
Bošković Ksenija (Faculty of Medicine, Novi Sad + Clinical Centre of Vojvodina, Medical Rehabilitation Clinic, Novi Sad)
Tomašević-Todorović Snežana (Faculty of Medicine, Novi Sad + Institute for Children and Youth Health Care of Vojvodina, Novi Sad)
Jeličić Zoran D. (Faculty of Technical Sciences, Novi Sad)
Introduction. 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. Objective. 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). Methods. 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. Results. The study
included 263 patients, average age 60.82 Ѓ} 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%. Conclusion. 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.
Keywords: amputation, rehabilitation, recovery of function, support vector machines