An expert model for self-care problems classification using probabilistic neural network and feature selection approach

• Published in: Applied soft computing 2019-09, Vol.82, p.105545, Article 105545
• Main Authors: Fatemi Bushehri, S.M.M., Zarchi, Mohsen Sardari
• Format: Article
• Language: English
• Subjects: Feature selection; Genetic algorithm; ICF-CY; PNN classifier; Rule extraction; Self-care classification; Soft computing
• ISSN: 1568-4946; 1872-9681
• DOI: 10.1016/j.asoc.2019.105545

Self-care problems classification is one of the important challenges for occupational therapists. Extent and variety of disorders make the self-care problems classification process complex and time-consuming. To overcome this challenge, an expert model is proposed innovatively in this research. The proposed model is based on Probabilistic Neural Network (PNN) and Genetic Algorithm (GA) for classifying self-care problems of children with physical and motor disability. In this model, PNN is employed as a classifier and GA is applied for feature selection. The PNN is trained by using a standard ICF-CY dataset. Based on ICF-CY, occupational therapists must evaluate many features to diagnose self-care problems. According to the experiences of occupational therapists, these features have different effects on classification. Hence, GA is employed to select relevant and important features in self-care problems classification. Since the classification rules are important for occupational therapists, the self-care problems classification rules are extracted additionally by using the CART algorithm. The experimental results show that by using the feature selection algorithm, the accuracy and time complexity of classification are improved in comparison to other models. The proposed model can classify self-care problems of children with 94.28% accuracy by using only 16.5% of all features. •Proposing an expert model for self-care problem classification.•Using ICF-CY framework to standardize the proposed model standard.•Providing a feature selection approach to increase efficiency and accuracy.•Employing Probability Neural Network as a robust and accurate classifier.•Extracting self-care classification rules by a data mining approach.