A study of applying data mining to early intervention for developmentally-delayed children

Abstract

The implementation of early intervention has close relation to the growth development of developmentally-delayed children. The earlier the intervention is involved the more significant effects and results it will bring to the benefits of these young children. However, providing early intervention exclusively without finding out the relationship between the two would eventually leave the problematic point remain unsolved. Since a child’s becoming developmental delay is resulted from many factors, much of the valuable knowledge among all needs to be unveiled. In the process of knowledge discovery, use data mining approach to nugget out potential knowledge from vast amounts of data. The main purpose of this study is to explore the hidden knowledge among medical history data of developmentally-delayed children. Fields of medical history database belongs to set and binary, so decision tree is constructed to classify delay levels of each type according to physical illness, and association rule is applied to locate correlations between cognitive, language, motor, and social emotional developmental delays. The study results indicate that the majority of illnesses will result in delays in cognitive, language, and motor development. Simultaneously, among all types of delay, motor and cognitive delay mostly accompanies with symptoms of language delay. The results of this study enable healthcare professionals to be on top of the developments of young children during the process of evaluation and diagnosis, and to provide early intervention so that developmentally-delayed children can catch up with their normal peers in development and growth.