Classification and action rules in identification and self-care assessment problems

Abstract

BACKGROUND: Disability, especially in children, is a very important and current problem. Lack of proper diagnosis and care increases the difficulty for children to adapt to disabilities. Disabled children have many problems with basic activities of daily living. Therefore, it is very important to support diagnosticians and physiotherapists in recognizing self-care problems in children. OBJECTIVE: The aim of this paper is to extract classification and action rules, useful for those who work with children with disabilities. METHODS: First, features and their impact on the accuracy of classification are determined. Then, two models are built: one with all features and one with selected ones. For these models the classification rules are extracted. Finally, action rules are mined and the next step in treatment process is predicted. RESULTS: Seventeen features with the greatest impact on classifying a child into a particular group of self-care problems were identified. Based on the implemented algorithms, decision and action rules were obtained. CONCLUSIONS: The obtained model, selected attributes and extracted classification and action rules can support the work of therapists and direct their work to those areas of disability where even a minimal reduction of features would be of great benefit to the children.

The development and impact of an app for a smart drug interaction reminder system

“…Furthermore, the QSAR application focused on basic calculation and feedback while using the prediction model of a single algorithm only. Kwon and Zdrodowska found that using a single model did not elicit impressive results when the response characteristics of each subject were collected; however, the most important predictors could be identified by combining multiple models [ 23 , 29 ]. This study’s main algorithm was a basic artificial neural network.…”