Machine learning-based brief version of the Caregiver-Teacher Report Form for preschoolers. (March 2023) - Lin, Gong-Hong; Lee, Shih-Chieh; Yu, Yen-Ting; Huang, Chien-Yu
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.