Accurate classification of self-care problems in children who suffer from physical and motor affliction is an important problem in the healthcare industry. This is a difficult and a time consumming process and it needs the expertise of occupational therapists. In recent years, healthcare professionals have opened up to the idea of using expert systems and artificial intelligence in the diagnosis and classification of self care problems. In this study, we propose a new deep learning based approach named Care2Vec for solving these kind of problems and use a real world self care activities dataset that is based on a conceptual framework designed by the World Health Organization (WHO). Care2Vec is a mix of unsupervised and supervised learning where we use Autoencoders and Deep neural networks as a two step modeling process. We found that Care2Vec has a better prediction accuracy than some of the traditional methods reported in the literature for solving the self care classification problem viz. Decision trees and Artificial neural networks.
@cite_5: Abstract Self-care problems diagnosis and classification is an important challenge in exceptional children health care systems. Since, self-care problems classification is a time-consuming process and requires expert occupational therapists, using an expert system in classifying these problems can decrease cost and time, efficiently. Expert systems refer to the systems that are based on artificial intelligence methods, which have the ability to learn, infer, and predict. In order to configure and train an expert system, a standard dataset is critical for the learning phase. Hence, in this research, a new standard dataset called SCADI (Self-Care Activities Dataset based on ICF-CY) is introduced innovatively. SCADI is based on ICF-CY, which is a conceptual framework, released by the World Health Organization. According to the best of our knowledge, SCADI is the first standard dataset in the self-care activates based on ICF-CY in which 29 self-care activities are considered. In this research, to show the applicability of SCADI in the expert systems, two different types of expert systems are proposed for the self-care problems classification of children with physical and motor disability. In the first expert system, an Artificial Neural Network (ANN) is employed as a classifier. This classifier is trained by using SCADI during the learning process. Since ANNs do not provide any explanation for the inference rules and manners, in the second expert system, to evaluate the applicability of SCADI in the rule-based systems, C4.5, a popular decision tree algorithm is used to extract self-care problems classification rules precisely. The experiment results show that the ANN-based system has high accuracy in self-care problems classification, which is 83.1 and SCADI has the high applicability to be employed in the different classification systems such as neural networks and rule-based systems.
However, in the literature we don't find much work apart from that of @cite_5 that focuses on machine learning algorithms for solving the self-care problem classification. Thus, there is a great opportunity for development and application of new methodologies that outperform the traditional ones in this domain.