Suicide risk classification with machine learning techniques in a large Brazilian community sample

Even though suicide is a relatively preventable poor outcome, its prediction remains an elusive task. The main goal of this study was to develop machine learning classifiers to identify increased suicide risk in Brazilians with common mental disorders. With the use of clinical and sociodemographic baseline data (n = 4039 adult par-ticipants) from a large Brazilian community sample, we developed several models (Elastic Net, Random Forests, Naive Bayes, and ensemble) for the classification of increased suicide risk among individuals with common mental disorders. 1120 participants (27.7%) presented increased suicide risk. The Random Forests model ach-ieved the best AUC ROC (0.814), followed by Naive Bayes (0.798) and Elastic Net (0.773). Sensitivity varied from 0.922 (Naive Bayes) to 0.630 (Random Forests), while specificity varied from 0.792 (Random Forests) to 0.473 (Naive Bayes). The ensemble model presented an AUC ROC of 0.811, sensitivity of 0.899, and specificity of 0.510. Features representing depression symptoms were the most relevant for the classification of increased suicide risk. Some of our models presented good performance metrics in the classification of increased suicide risk in the investigated sample, which can provide the means to early preventive interventions.

Automated summary

The study aimed to use machine learning classifiers to identify increased suicide risk in Brazilians with common mental disorders. Several models were developed using clinical and sociodemographic baseline data from a large Brazilian community sample. The Random Forests model performed the best, followed by Naive Bayes and Elastic Net. Depression symptoms were found to be the most relevant features for identifying increased suicide risk.