Machine learning-based brief version of the Caregiver-Teacher Report Form for preschoolers. (March 2023)

• Record Type: Journal Article
• Authors: Lin, Gong-Hong; Lee, Shih-Chieh; Yu, Yen-Ting; Huang, Chien-Yu
• Is Part Of: Research in developmental disabilities. Volume 134(2023)
• Publication Date: 2023-03
• Subjects: Artificial intelligence -- Machine learning -- Assessment -- Emotional and behavioral problems; Developmental disabilities -- Periodicals; Developmentally disabled -- Research -- United States -- Periodicals; Developmentally disabled children -- Education -- Research -- United States -- Periodicals; Developmental Disabilities -- Periodicals; Disabled -- Periodicals; Mental Retardation -- rehabilitation -- Periodicals; Personnes atteintes de troubles du développement -- Recherche -- États-Unis -- Périodiques; Enfants atteints de troubles du développement -- Éducation -- Recherche -- États-Unis -- Périodiques; Développement, Troubles du -- Recherche -- États-Unis -- Périodiques; 616.858800
• DOI: 10.1016/j.ridd.2023.104437
• Languages: English
• ISSNs: 0891-4222

Abstract

Abstract: Background: The Caregiver - Teacher Report Form of the Child Behavior Checklist for Ages 1½–5 (C-TRF) is a widely used checklist to identify emotional and behavioral problems in preschoolers. However, the 100-item C-TRF restricts its utility. Aims: This study aimed to develop a machine learning-based short-form of the C-TRF (C-TRF-ML). Methods and procedures: Three steps were executed. First, we split the data into three datasets in a ratio of 3:1:1 for training, validation, and cross-validation, respectively. Second, we selected a shortened item set and trained a scoring algorithm using joint learning for classification and regression using the training dataset. Then, we evaluated the similarity of scores between the C-TRF-ML and the C-TRF by r- squared and weighted kappa values using the validation dataset. Third, we cross-validated the C-TRF-ML by calculating the r- squared and weighted kappa values using the cross-validation dataset. Outcomes and results: Data of 363 children were analyzed. Thirty-six items of the C-TRF were retained. The r- squared values of C-TRF-ML scores were 0.86–0.96 in the cross-validation dataset. Weighted kappa values of the syndrome/problem grading were 0.72–0.94 in the cross-validation dataset. Conclusions and implications: The C-TRF-ML had about 60 % fewer items than the C-TRF but yielded comparable scores with the C-TRF. Highlights: Machine learning algorithms reduce 60 % of the original number of items. Machine learning algorithms reproduce original scores using short-form scores. Machine learning algorithms categorize children's emotional and behavioral problems.