Integrating multiple feature assessment methods to identify key predictors of repeat suicide attempts in Taiwan

Abstract

Background: The high rate of repeat attempts among individuals who have previously attempted suicide presents a critical challenge in public health and suicide prevention. While early and targeted intervention is crucial for this high-risk group, effectively identifying those most likely to re-attempt is a persistent difficulty, especially when public health resources are limited. This creates a pressing need for accurate and practical risk assessment tools. This study aims to address this gap by using machine learning to analyze a nationwide suicide surveillance database to identify key predictors of repeat suicide attempts and develop a robust predictive model to aid in resource allocation and early intervention.

Methods: This study analyzed data from 32,701 individuals, encompassing 31 features, recorded in Taiwan's National Suicide Surveillance System for 2020. We employed binary decision tree regression with multiple feature selection techniques to identify significant predictors of recurrent suicide attempts. A prediction model was then developed without requiring biological samples.

Results: History of mental illness, specific age groups, and supervision status of mentally ill patients emerged as primary influences on repeated suicide attempts. The prediction model achieved 66.3% accuracy in identifying potential repeat attempters, with a 57.9% success rate for predicting re-suicide events. These findings align with recent literature on suicide recurrence risk factors.

Conclusions: This study provides a practical risk assessment tool that enables early intervention for high-risk individuals without invasive biological sampling. The insights offer valuable guidance for government suicide prevention policies, especially when prioritizing cases with limited resources. Future enhancements through broader data integration and interdisciplinary cooperation could establish a more comprehensive prevention system to reduce the societal impact of suicidal behavior.

Trial registration: Not applicable as this was a retrospective database analysis rather than a randomized controlled trial.

Keywords: History of past mental illness; Prevention; Suicide; Suicide behavior; Surveillance.

Fig. 1

Decision Tree Model Interpretability Results. Utilizing a tree structure to examine the top 10 significant levels affecting repeated suicide attempts

Fig. 2

MRMR algorithm

Fig. 3

Chi-Squared test

Fig. 4

ReliefF algorithm

Fig. 5

ANOVA algorithm

Fig. 6

Kruskal–Wallis test

Fig. 7

Confusion matrix for optimizable tree model