Fiziksel ve Motor Engelli Çocukların Öz Bakım Problemlerinin Derin Sinir Ağları İle Sınıflandırılması

Abdulkadir Karacı

doi:10.2339/politeknik.522641

Research Article

Self-Care Problems Classification of Children with Physical and Motor Disability by Deep Neural Networks

Year 2020, Volume: 23 Issue: 2, 333 - 341, 01.06.2020

Abdulkadir Karacı

https://doi.org/10.2339/politeknik.522641

Cited By: 2

Abstract

Physical
and motor disability is a disorder that greatly limits some of the individual
main life activities. These disorders affect children in many countries of the
world. In addition, it is a difficult process for physically and motorly
disabled individuals to be classified by doctors with appropriate occupational
treatments. Because, there are many variables that must be considered. The aim
of this study is to classify the self-care skill problems of children with
physical and motor disabilities by the minimal error using deep neural networks
(DNN). For this purpose, DNN models with different parameters were created. The
number of hidden layers, the number of neurons in the hidden layers, the
activation function, the optimization algorithm, the loss function and the
epoch value are taken into consideration in the creation of the models. The DSA
models were trained and tested with the SCADI (Self-Care Activities Dataset
based on ICFCY) data set. The classification performance of the models was
demonstrated by using the F-1 score, precision (P), recall (R) and accuracy
(ACC) metrics. Details of the 8 models with the best grading performance are
presented. According to the findings, the best classification performance was
obtained in the DSA-1 model using Adadelta optimization algorithm, Elu
activation function and Categorical crossentropy loss function. The P, R, ACC and
F1 scores of this model are 1. In other words, this model predicts the
self-care skills problems of physical and motor disability children with 100%
accuracy. In addition, in order to increase the validity of the three best
models (DSA-1, DSA-2 and DSA-3), the training and testing process was performed
with 10-fold cross-validation method. Mean cross validation accuracy values
were calculated as 85.71%, 85.71% and 87.14% respectively. Occupational
therapists can be used developed DSA models as a validating tool for diagnosing
self-care problems.

Keywords

Machine learning, deep neural network, physical and motor disability, self-care

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