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

Abdulkadir Karacı

doi:10.2339/politeknik.522641

Araştırma Makalesi

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

Yıl 2020, Cilt: 23 Sayı: 2, 333 - 341, 01.06.2020

Abdulkadir Karacı

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

Cited By: 2

Öz

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.

Anahtar Kelimeler

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

Kaynakça

Zarchi M.S., Bushehri S.M.M. F. and Dehghanizadeh M., “SCADI: A standard dataset for self-care problems classification of children with physical and motor disability”, International Journal of Medical Informatics, 114: 81-87, (2018).
Lucas-Carrasco R., Eser E., Hao Y., McPherson K.M., Green A., Kullmann L., Group T.W.D., “The quality of care and support (QOCS) for people with disability scale: development and psychometric properties”, Res. Dev. Disabil., 32(3):1212–1225, (2011).
Brown R. L., Turner R. J., “Physical disability and depression: clarifying racial/ ethnic contrasts”, Journal of Aging and Health, 22 (7): 977–1000, (2010).
Tsai C-F., Guo H-R., Tseng Y-C., Lai D-C., “Sex and geographic differences in the prevalence of reported childhood motor disability and their trends in Taiwan”, BioMed Research International, (2018), Article ID 6754230, 7 pages, 2018. https://doi.org/10.1155/2018/6754230.
Chang Y.-J., Chen S.-F., Huang J.-D., “A Kinect-based system for physical rehabilitation: A pilot study for young adults with motor disabilities”, Research in Developmental Disabilities, 32(6): 2566-2570, (2011).
Yeh Y.L., Hou T.H., Chang W.Y., “An intelligent model for the classification of children's occupational therapy problems”, Expert Syst. Appl., 39(5): 5233–5242, (2012).
Lollar D.J., Simeonsson R.J., “Diagnosis to function: classification for children and youths”, J. Dev. Behav. Pediatrics, 26(4):323-330, (2005).
Keawutan P., Bell K. L., Oftedal S., Davies P. S. W., Ware R. S., Boyd R. N., “Relationship between habitual physical activity, motor capacity, and capability in children with cerebral palsy aged 4–5 years across all functional abilities”, Disability and Health Journal, 11(4): 632-636, (2018). https://doi.org/10.1016/j.dhjo.2018.03.006.
Organization, W.H., International Classification of Functioning, Disability, and Health: Children & Youth Version: ICF-CY, World Health Organization, 2007.
Wu T. K., Huang S.C., Meng Y.R., “Identifying and diagnosing students with learning disabilities using ANN and SVM”, International Joint Conference on Neural Networks, IJCNN’06, IEEE, 4387-4394, (2006).
David J.M., Balakrishnan K., “Prediction of learning disabilities in school-age children using SVM and decision tree”, International Journal of Computer Science and Information Technologies, 2(2):829-835, (2011).
Muangnak N., Pukdee W., Hengsanunkun T., “Classification students with learning disabilities using Naïve Bayes Classifier and Decision Tree”, The 6th International Conference on Networked Computing and Advanced Information Management, Seoul, 189-192, (2010).
Buduma N., Lacascio N., “Fundamentals of Deep Learning”, O’Reilly Media, United States of America, (2017).
Jia F., Lei Y., Lin J., Zhou X., Lu N., “Deep neural networks: A promising tool for fault characteristic mining and intelligent diagnosis of rotating machinery with massive data”, Mechanical Systems and Signal Processing, 72–73:303-315, (2016). doi:10.1016/j.ymssp.2015.10.025.
Wang B., Luo X., Li Z., Zhu W., Shi Z., Osher S.J., “Deep neural nets with interpolating function as output activation”. arXiv preprint arXiv:1802.00168, (2018).
Ding B., Qian H., Zhou J., “Activation functions and their characteristics in deep neural networks”, Chinese Control And Decision Conference (CCDC), Shenyang, 1836-1841, (2018). doi: 10.1109/CCDC.2018.8407425
An W., Wang H., Sun Q., Xu J., Dai Q., Zhang L., “A PID controller approach for stochastic optimization of deep networks”, The IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 8522-8531, (2018).
Schmidhuber J., “Deep learning in neural networks: An overview”, Neural Networks, 61:85-117, (2015).
Kızrak M. A., Bolata B., “Comprehensive survey of deep learning in crowd analysis”, International Journal of Informatics Technologies, 11(3):263-286, (2018).
Prasetijo A. B., Isnanto R. R., Eridani D., Soetrisno Y. A. A., Arfan M., Sofwan A., “Hoax detection system on Indonesian news sites based on text classification using SVM and SGD”, 4th International Conference on Information Technology, Computer, and Electrical Engineering (ICITACEE), Semarang, 45-49, (2017).doi: 10.1109/ICITACEE.2017.8257673
Fawcett T., “An Introduction to ROC analysis”, Pattern Recognition Letters, 27(8): 861–874, (2006). doi:10.1016/j.patrec.2005.10.010.
Paix˜ao W. R., Paix˜ao T. M. , Costa M. C. B., Andrade J. O. , Pereira F. G., Komati K. S., “Texture classification of sea turtle shell based on color features: color histograms and chromaticity moments”, International Journal of Artificial Intelligence and Applications (IJAIA), 9(2): 55-67, (2018). doi: 10.5121/ijaia.2018.9205
Sasaki Y., “The truth of the f-measure”, Teach Tutor mater, 1(5):1-5, 2007.
Yang Y. and Liu X., “A re-examination of text categorization methods”, Proceedings of SIGIR-99, 22nd ACM International Conference of Research and Development in Information Retrieval, New York, 42-49, (1999).
Davis J., Goadrich M., “The relationship between precision-recall and roc curves”, in Proceedings of the 23rd international conference on Machine learning. ACM, 233-240, (2006).
Eschner N., Weiser L., Häfner B., Lanza G., “Development of an acoustic process monitoring system for selective laser melting (SLM)”, Solid Freeform Fabrication 2018: Proceedings of the 29th Annual International Solid Freeform Fabrication Symposium – An Additive Manufacturing Conference Reviewed Paper, 2097-2117, (2018).