Predictive models of suicidal ideation risk in perinatal-stage women based on sociodemographic and clinical data

Suicidal ideation in women during the perinatal period has become a growing public health problem, with a prevalence ranging from 8 to 19%. Its etiology is multifactorial and carries additional consequences for both the newborn and the woman beyond death itself.

This study aims to predict the risk of suicidal ideation in perinatal women by using artificial intelligence models based on sociodemographic and clinical data.

An analytical observational study was conducted with a sample of 908 Spanish women during the perinatal period, collecting relevant data. To predict the risk of suicidal ideation, five machine learning models (OneR, JRIP, FURIA, J48 and Random Forest) were employed, as they provide rules or trees that can be easily followed to gather additional information. The metrics used to evaluate the performance of the models included accuracy, precision, recall and F1-score.

The models show an accuracy of around 60% in most cases. The model that performs the worst is OneR, with an accuracy of less than 50%. The Random Forest model stood out for its higher accuracy. The metrics of this model (Random Forest) were Accuracy (%) 0.639 ± 0.05, Precision: 0.634, Recall: 0.634 F1:0.634 and AUPRC: 0.632. Factors identified as predictors of suicidal ideation risk included low birth weight, history of mental health problems, problems of intimate partner violence, low income and smoking.

In conclusion, predictive models based on sociodemographic data and clinical variables show a moderate ability to predict suicidal ideation risk in perinatal women.