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

Abstract

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 (AUC = 0.97). The selected items captured core symptoms of adolescent mania, including increased energy, risk-taking, and irritability. Two cutoff points were identified: a score of 3 offered high specificity (0.98) and positive predictive value (0.98), while a score of 4 provided balanced sensitivity (0.87) and specificity (0.94) with the highest overall accuracy (0.91). Conclusions The machine learning-driven 8-item version of the HCL-32 demonstrates strong diagnostic utility for adolescent bipolar disorder, offering a more efficient screening tool without sacrificing clinical sensitivity. This shortened scale may improve assessment feasibility and accuracy in clinical settings, addressing the unique challenges of diagnosing bipolar disorder in adolescents.

Keywords

• Feature selection
• Random forest
• Gradient boosting machine
• Scale reduction
• Manic symptoms