Identification of central symptoms of children depression and development of two short version of Children's Depression Inventory: Based on network analysis and machine learning

Using network analysis to study the central symptoms is important for understanding the mechanism of depression symptoms and selecting items for the short version depression screening scale. This study aimed to identify the central symptoms of depression and develop the short and effective depression screening tools for Chinese rural children. Firstly, the 2458 individuals (Mage = 10.74; SDage = 1.64; 51.2 % were female) were recruited from the rural children's mental health database. Children's Depression Inventory (CDI) was used to assess depression symptoms. Then, network analysis was used to identify the central symptoms of depression. The accuracy, stability, and gender invariance of the depression symptoms network were tested. Finally, a short version of CDI with central symptoms (CDI-SC) and a new CDI-10 (CDI-10-N) were developed by network analysis and feature selection techniques to optimize the existing CDI-10. Their performances in screening depression symptoms were validated by the cutoff threshold and machine learning. The central symptoms of Chinese rural children's depression were sadness, self-hatred, loneliness and self-deprecation. This result was accurate and stable and depression symptoms network has gender invariance. The AUC values of CDI-10-N and CDI-SC are over 0.9. The CDI-10-N has a higher AUC than CDI-10. The optimal cutoff thresholds for CDI-10-N and CDI-SC are 6 and 1. The performance of machine learning on AUC generally outperforms those of cutoff threshold. The central symptoms identified in this study should be highlighted in screening depression symptoms, and CDI-10-N and CDI-SC are effective tools for screening depression symptoms in Chinese rural children.