Department of Computer Engineering, Meybod University, Meybod, Iran.
Department of Software Engineering, Yazd Branch, Islamic Azad University, Yazd, Iran.
Int J Med Inform. 2018 Jun;114:81-87. doi: 10.1016/j.ijmedinf.2018.03.003. Epub 2018 Mar 30.
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.
自理问题的诊断和分类是特殊儿童保健系统中的一个重要挑战。由于自理问题的分类是一个耗时的过程，需要专家作业治疗师的参与，因此使用专家系统对这些问题进行分类可以有效地降低成本和时间。专家系统是指基于人工智能方法的系统，具有学习、推断和预测的能力。为了配置和训练专家系统，标准数据集对于学习阶段至关重要。因此，在这项研究中，创新性地引入了一个名为 SCADI（基于 ICF-CY 的自理活动数据集）的新标准数据集。SCADI 基于 ICF-CY，这是世界卫生组织发布的一个概念框架。据我们所知，SCADI 是第一个基于 ICF-CY 的自理活动标准数据集，其中考虑了 29 种自理活动。在这项研究中，为了展示 SCADI 在专家系统中的适用性，针对身体和运动残疾儿童的自理问题分类，提出了两种不同类型的专家系统。在第一个专家系统中，使用人工神经网络（ANN）作为分类器。这个分类器在学习过程中通过使用 SCADI 进行训练。由于神经网络没有为推理规则和方式提供任何解释，因此在第二个专家系统中，为了评估 SCADI 在基于规则的系统中的适用性，使用了流行的决策树算法 C4.5 来精确提取自理问题分类规则。实验结果表明，基于 ANN 的系统在自理问题分类方面具有很高的准确性，达到了 83.1%，并且 SCADI 具有很高的适用性，可以应用于神经网络和基于规则的系统等不同的分类系统。
