An intelligent model for the classification of children’s occupational therapy problems

ObjectivesIn Taiwan, the classification of real problems of children with appropriate occupational therapy is a difficult job for the therapist. The complexities of 127 attribute values to be evaluated in the assessment, the misleading diagnosis which may be made by the pediatrician and the shortage of manpower cause of high workload for the therapist. The design of an easy to use and effective classification model is therefore an important issue in children’s occupational therapy treatment. This study accordingly applies an artificial neural network (ANN) and classification and regression tree (CART) techniques to skeleton an intelligent classification model in order to provide a comprehensive framework to assist the therapist to raise the accuracy when categorizing children’s problems for occupational therapy. These categories with critical attributes under the guidelines of the American Occupational Therapy Association (AOTA) are discussed, in order to assist the therapist for precise assessment and appropriate treatment. To the best of our knowledge, no research has yet been conducted on the problems’ characteristics in children’s occupational therapy.MethodsBased on the advice and assistance of the therapists and occupational therapy treatment needed, 127 outpatients from a regional hospital in Taiwan between 2007 and 2010 were selected as the data sets for problems in children occupation classification. This study accordingly suggests an intelligent model for the classification which integrates ANN and CART. The major steps in applying the model include: (1) building an ANN higher performance trained model; and (2) adopting CART to the trained model and building in previous steps, to extract the critical attributes of children occupational problems.ResultsThe results showed that artificial neural network had a higher accuracy, up to 84%, with evenly distributed datasets. Then high performance of the trained neural network had been extracted for the rules by using the classification tree approach in the classification and regression trees application. Most important of all, this study indicated that some of the rules can correctly identify up to 67% of the problems of the children with 100% confidence, which is much better than the current evaluations being used. Moreover, the tree with a binary variable of age and 8 predicators were found and listed afterward, such as, gross coordination, upper left muscle tone, interpersonal skill, proprioceptive and vestibular, visual, visual stimulus input for influence of emotional and movement, swallowing, and dressing. Actual implementation showed that the intelligent classification model is capable of integrating ANN and CART techniques to clarify children’s occupational therapy problems with considerable accuracy.ConclusionsThe model could be employed as a supporting system in making decisions regarding children problems with occupational therapy classifications and treatment. The rules extracted from CART were helpful to therapists in classifying what category the real problems of the children belonged to. This study expected that more machine learning techniques will certainly play an essential role in future children occupational therapy applications.