Machine Learning-Based Measurement System for Spinal Cord Injuries Rehabilitation Length of Stay

Citation:
Rehab Mahmoud, Nashwa El-Bendary, H. M. A. E. H. H. S. M. O. A., "Machine Learning-Based Measurement System for Spinal Cord Injuries Rehabilitation Length of Stay", Proceedings of the Second Euro-China Conference on Intelligent Data Analysis and Applications, ECC 2015, , Ostrava, Czech Republic, , June 29 - July , 2015.

Date Presented:

June 29 - July

Abstract:

Disabilities, specially Spinal Cord Injuries (SCI), affect people behaviors, their response, and the participation in daily activities. People with SCI need long care, cost, and time to improve their heath status. So, the rehabilitation of people with SCI on different period of times is required. In this paper, we proposed an automated system to estimate the rehabilitation length of stay of patients with SCI. The proposed system is divided into three phases; (1) pre-processing phase, (2) classification phase, and (3) rehabilitation length of stay measurement phase. The proposed system is automating International Classification of Functioning, Disability and Health classification (ICF) coding process, monitoring progress in patient status, and measuring the rehabilitation time based on support vector machines algorithm. The proposed system used linear and radial basis (RBF) kernel functions of support vector machines (SVMs) classification algorithm to classify data. The accuracy obtained was full match on training and testing data for linear kernel function and 93.3 % match for RBF kernel function.

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