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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 ORCID iD icon (Faculty of Technical Sciences, Novi Sad)
Mikov Aleksandra ORCID iD icon (Faculty of Medicine, Novi Sad + Institute for Children and Youth Health Care of Vojvodina, Novi Sad)
Jeremić-Knežević Milica ORCID iD icon (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 ORCID iD icon (Faculty of Medicine, Novi Sad + Clinical Centre of Vojvodina, Medical Rehabilitation Clinic, Novi Sad)
Tomašević-Todorović Snežana ORCID iD icon (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