Journal of International Psychiatry

[Abstract] Objective The aim of this study was to explore the relationship between executive function and suicide in severe major depressive disorder (MDD) and to develop a suicide risk prediction model based on machine learning algorithms using MDD patients' executive function. Methods The study enrolled 51 MDD suicide cases, 55 MDD non-suicide cases, and 100 healthy controls. Executive function was measured using the N-back task, affective shifting task (AST), and Iowa Gambling Task (IGT). Variance analysis was used to compare differences in executive function among the three groups. The extreme gradient boosting (XGBoost) classification algorithm was used to construct the prediction model, which was compared based on predictive performance and area under the curve (AUC), and local interpretation techniques were used to evaluate the relative importance of features. Results There were significant intergroup differences in updating (P<0.001), inhibition (P=0.009), shifting (P<0.001), and decision-making functions (P<0.001). The MDD suicide group showed poorer performance in inhibition (P=0.013) and shifting functions (P=0.023) compared to the MDD non-suicide group. A more effective suicide prediction model was developed using comprehensive data, and executive function improved the specificity, sensitivity, accuracy, F1 score, and AUC of the prediction model. Conclusion The findings suggest that executive function is an important risk factor for suicide. Using comprehensive data can improve the classification performance of the suicide attempt prediction model. An interpretable model can improve the credibility of the prediction results and aid in early intervention.

[Key words] Suicide; Major Depressive Disorder; Executive Function; Machine Learning