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

ABSTRACT:

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

SUBMITTER: Walaszek M 

PROVIDER: S-EPMC12557483 | biostudies-literature | 2025 Oct-Dec

REPOSITORIES: biostudies-literature