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Machine learning based suicide prediction and development of suicide vulnerability index for US counties

Medicine and Health

Machine learning based suicide prediction and development of suicide vulnerability index for US counties

V. Kumar, K. K. Sznajder, et al.

Discover groundbreaking insights from Vishnu Kumar, Kristin K. Sznajder, and Soundar Kumar as they explore alarming suicide trends across the US. Their innovative machine learning model boasts an impressive R² of 0.98 and identifies critical socioeconomic factors, paving the way for an essential Suicide Vulnerability Index (SVI) aimed at enhancing targeted prevention efforts.

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Playback language: English
Abstract
This article investigates suicide trends and geographical distribution in the US, developing a machine learning model to predict suicide rates at the county level using data from 2010-2019. Analysis revealed significant increases in suicides across many counties. An XGBoost model, utilizing 17 features, achieved an R² of 0.98. SHAP analysis identified key features like total population, % African American population, % White population, median age, and % female population. These features were used to create a Suicide Vulnerability Index (SVI) for US counties, aiding targeted suicide prevention efforts.
Publisher
npj Mental Health Research
Published On
Jun 01, 2022
Authors
Vishnu Kumar, Kristin K. Sznajder, Soundar Kumar
Tags
suicide trends
machine learning
predictive modeling
county-level data
Suicide Vulnerability Index
socioeconomic factors
XGBoost

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