Disability Classification Using Deep Learning on Functional Assessment Data

Abouelezz, Mohamed; M.Fouad, Khaled; Abdelbaky, Ibrahim

doi:10.21608/ijaici.2025.354921.1008

Disability Classification Using Deep Learning on Functional Assessment Data

Document Type : Original Article

Authors

¹ Department of Artificial Intelligence , Faculty of Computer and Artificial Intelligence, Benha University, Benha, Egypt

² Department of Artificial Intelligence , Faculty of Computer and Artificial Intelligence, Benha University., Benha, Egypt.

10.21608/ijaici.2025.354921.1008

Abstract

Disability has been defined through various conceptual models, with the medical model categorizing individuals with impairments as disabled regardless of their daily life limitations. The World Health Organization estimates that 1.3 billion people experience significant disabilities, a number projected to exceed two billion by 2050 due to aging populations and increasing non-communicable diseases. Traditional disability evaluation processes often focus on symptoms or impairments, leading to challenges in accurately assessing work disability, which is linked to public health issues such as poverty and limited access to healthcare.

This study explores the application of Deep learning techniques in conjunction with a functional assessment tool used to classify disability types. Our objective is to develop a Deep learning model that automatically classifies disability types based on functional assessment results.

The results were obtained using one-dimensional convolutional neural network (1D CNN) with an accuracy of 95.04%. This study demonstrates the potential of AI to enhance disability classification, improving accuracy and reducing human error in medical assessments.

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