Comparison of traditional model-based statistical methods with machine learning for the prediction of suicide behaviour

Background: Despite considerable research efforts during the last five decades, the prediction of suicidal behaviour (SB) using traditional model-based statistical has been weak. This marks the need to explore new statistical methods. Objective: To compare the performance of Cox regression models versus Random Survival Forest (RSF) to predict SB. Methods: Using a data set of more than 300 high-risk suicidal patients from a multicenter prospective cohort study, we compare Cox regression models with RSF to address predictors of time to suicide reattempt. Cross validation was used to assess model prediction performance, including the area under the receiver operator curve (AUC), precision, Integrated Brier Score (IBS), sensitivity, and specificity. Results: A variant of the RSF denominated the RSFElimin, in which irrelevant predictor variables were eliminated from the model, presented the best accuracy, sensitivity, AUC and IBS. At the same time, the sensitivity of this method was slightly lower than that obtained with the Cox regression model with all predictor variables (CoxComp). Conclusion: The RSF, a machine learning model, seems more sensitive and precise than the traditional Cox regression model in predicting suicidal behaviour.

Automated summary

A comparison study between Cox regression models and Random Survival Forest (RSF) was conducted to predict suicidal behavior, and a variant of RSF (RSFElimin) showed better accuracy, sensitivity gAUC, and IBS compared to other methods.