Predicting Suicide Attempt Trends in Youth: A Machine Learning Analysis Using Google Trends and Historical Data.

ABSTRACT: Background: Suicide remains a leading cause of death among youth, yet effective tools to predict suicide attempts (SA) in individuals under 18 are scarce. This study aims to develop machine learning (ML) models to predict SA in paediatric populations using Google Trends data. Methods: Relative Search Volumes (RSVs) from Google Trends were analysed for terms linked to suicide risk factors. Pearson Correlation Coefficients (PCC) identified terms strongly associated with SA rates. Based on these, several ML models were developed and evaluated, including Random Forest Regression, Support Vector Regression (SVR), XGBoost, and Linear Regression. Model performance was assessed using metrics such as PCC, mean absolute error (MAE), mean squared error (MSE), root mean square error (RMS

SUBMITTER: Kachlik Z 

PROVIDER: S-EPMC12470995 | biostudies-literature | 2025 Sep

REPOSITORIES: biostudies-literature