Similarity Measures based Recommender System for Rehabilitation of People with Disabilities

Citation:
Mahmoud, R., N. El-Bendary, H. M. O. Mokhtar, and A. E. Hassanien, "Similarity Measures based Recommender System for Rehabilitation of People with Disabilities", the 1st International Conference on Advanced Intelligent Systems and Informatics (AISI’15) Springer, Beni Suef University, Beni Suef, Eg, Nov. 28-30, 2015.

Date Presented:

Nov. 28-30

Abstract:

This paper proposes a recommender system to predict and suggest a
set of rehabilitation methods for patients with spinal cord injuries (SCI). The proposed
system automates, stores and monitors the heath conditions of SCI patients.
The International Classification of Functioning, Disability and Health classification
(ICF) is used to stores and monitors the progress in health status. A set of
similarity measures are utilized in order to get the similarity between patients and
predict the rehabilitation recommendations. Experimental results showed that the
proposed recommender system has obtained an accuracy of 98% via implementing
the cosine similarity measure.

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