Machine learning-based brief version of the Caregiver-Teacher Report Form for preschoolers

Background: The Caregiver -Teacher Report Form of the Child Behavior Checklist for Ages 11/2-5 (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.

Automated summary

This study aimed to develop a machine learning-based short-form of the C-TRF (C-TRF-ML) by selecting a shortened item set and training a scoring algorithm using joint learning for classification and regression. The results showed that the C-TRF-ML had about 60% fewer items than the C-TRF but yielded comparable scores.