Machine learning-driven simplification of the hypomania checklist-32 for adolescent: a feature selection approach.

ABSTRACT:

Background

The Hypomania Checklist-32 is widely used to screen for bipolar disorder, but its length can be challenging for adolescents with manic symptoms. This study aimed to develop a shortened version of the HCL-32 tailored for adolescents using machine learning techniques.

Methods

Data from 2,850 adolescents (mean age 15.50 years, 68.81% female) who completed the HCL-32 were analyzed. Random forest (RF) and gradient boosting machine (GBM) algorithms were employed for feature selection. The area under the curve (AUC) was used to evaluate model performance. Receiver operating characteristic (ROC) analysis was conducted to determine optimal cutoff points for the shortened scale.

Results

An 8-item version of the HCL-32 was derived, maintaining high predictive accuracy

SUBMITTER: Shen G 

PROVIDER: S-EPMC11655905 | biostudies-literature | 2024 Dec

REPOSITORIES: biostudies-literature