Machine learning prediction of suicide attempt counts in Poland: Insights from Google trends and historical data.

Introduction: Suicidality is a complex, multifaceted issue with significant biopsychosocial causes, ranking as a major cause of death in developed nations. This study aims to leverage machine learning (ML) to predict monthly suicide counts in Poland using Google Trends data, contributing to ongoing efforts to improve public health strategies. Methods: Using data from the Polish National Police (2013-2023), monthly suicide attempt counts were analysed alongside relative search volumes (RSVs) of 40 suicide-related and mental health terms. Pearson Correlation Coefficient (PCC) identified the strongest predictors. Four ML models: Linear Regression, Random Forest, Support Vector Regression (SVR), and XGBoost Regression were tested, with PCC and error metrics guiding model selection. Results: Results showed that 16 terms were the best predictors for the general population and 13 for the adult cohort. Random Forest Regression outperformed the other models, achieving a PCC of 0.909 and a mean absolute percentage error (MAPE) of 6.78% for the general population, compared to SVR's PCC of 0.644 and 14.8% MAPE. For the adult cohort, Random Forest yielded a PCC of 0.853 and MAPE of 7.21%, again outperforming SVR. Key predictors included Conclusions: This study presents one of the first ML approaches to predicting suicide attempts at national level, highlighting the utility of Google Trends data. Further research with higher-resolution data is recommended to refine predictive models and enhance suicide prevention strategies.