Author/Editor		Bajd, T; Grobelnik, M; Mladenić, D; Lavrač, N; Prodnik, V; Benko, H; Šavrin, R; Obreza, P
Title		Machine learning for prediction of walking abilities in incomplete spinal cord injured patients
Type		članek
Source		In: Kononenko I, Urbančič T, editors. CADAM-97. Zbornik Računalniška analiza medicinskih podatkov; 1997 nov 12; Bled. Ljubljana: Inštitut Jožef Stefan,
Publication year		1997
Volume		str. 18-25
Language		eng
Abstract		It is difficult to predict the outcome of the functional electrical stimulation (FES) rehabilitation process when patients are admitted to the spinal unit soon after an accident that caused incomplete spinal cord injury (SCI). Similarly, it is not possible to decide what rehabilitation aid the patient wil (need after recovering from a spinal cord injury. The aim of this study is to develop a diagnostic procedure which will soon after the accident predict which incomplete SCI patients are candidates for a permanent use of FES orthotic aid. Based on data about 31 incomplete SCI patients, a classification tree was developed using a machine learning tool CART. The induced classification tree indicates that the candidates for chronic use of FES are patients with weak ankle dorsiflexors and sufflciently strong knee extensors. It was further demonstrated that FES is less functional in the incomplete SCI patients who are bound to wheelchair.
Descriptors		SPINAL CORD INJURIES WALKING ARTIFICIAL INTELLIGENCE ORTHOTIC DEVICES ELECTRIC STIMULATION THERAPY TREATMENT OUTCOME