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

Fiziksel ve motor engellilik bazı bireysel ana yaşam aktivitelerini büyük ölçüde sınırlandıran bir bozukluktur. Bu bozukluklar dünyanın birçok ülkesinde çocukları etkilemektedir. Bunun yanı sıra fiziksel ve motor engelli bireylerin doktorlar tarafından uygun mesleki tedavilerle sınıflandırılmaları zor bir süreçtir. Çünkü değerlendirilmesi gereken birçok değişken vardır. Bu çalışmadaki amaç, fiziksel ve motor engelli çocukların öz bakım beceri problemlerini derin sinir ağlarını (DSA) kullanarak en az hata ile sınıflandırmaktır. Bu amaçla farklı parametrelere sahip DSA modelleri oluşturulmuştur. Modellerin oluşturulmasında gizli katman sayısı, gizli katmanlardaki nöron sayısı, aktivasyon fonksiyonu, optimizasyon algoritması, kayıp fonksiyonu ve epoch değeri parametreleri dikkate alınmıştır. Oluşturulan DSA modelleri SCADI (Self-Care Activities Dataset based on ICFCY) veri seti vasıtasıyla eğitilmiş ve test işlemi gerçekleştirilmiştir. Modellerin sınıflandırma performansları F-1 puanı, kesinlik (precision-P), hassasiyet (recall-R) ve doğruluk (accuracy-ACC) metrikleri kullanılarak ortaya konulmuştur. En iyi sınıflandırma performansına sahip 8 modelin ayrıntıları sunulmuştur. Elde edilen bulgulara göre en iyi sınıflandırma performansı Adadelta optimizasyon algoritmasını, Elu aktivasyon fonksiyonunu ve Categorical crossentropy kayıp fonksiyonunu kullanan DSA-1 modelinde elde edilmiştir. Bu modelin P, R, ACC ve F1 puanı değerleri 1’dir. Yani bu model fiziksel ve motor engelli çocukların öz bakım beceri problemlerini %100 doğrulukla tahmin etmektedir. Ayrıca, en iyi üç modelin (DSA-1, DSA-2 ve DSA-3) geçerliliğini artırmak için 10-fold çapraz doğrulama yöntemi ile eğitim ve test işlemi tekrar gerçekleştirilmiştir. Ortalama çapraz doğrulama accuracy değerleri sırasıyla %85.71, % 85.71 ve % 87.14 olarak hesaplanmıştır. Mesleki terapistler, geliştirilen DSA modellerini öz bakım problemlerini teşhis etmede doğrulayıcı bir araç olarak kullanılabilirler.

Anahtar Kelimeler:

makine öğrenmesi, derin sinir ağı, fiziksel ve motor engellilik, öz bakım

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

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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