Predicting Suicidal Ideation in Community-based Older Adults Using Self-report Questionnaires with Machine Learning.

ABSTRACT:

Objective

Suicide is a significant public health issue, particularly among older adults, where the risk is heightened. Early identification of individuals at risk for suicidal ideation is essential for timely interventions, greatly improving prevention efforts. This study aimed to develop a predictive model for suicidal ideation in community-dwelling older adults using psychiatric self-report scales and machine learning classifiers.

Methods

A total of 238 older adults were assessed using the Patient Health Questionnaire-9 (PHQ-9), Generalized Anxiety Disorder-7, Perceived Stress Scale-10, and World Health Organization Quality of Life Scale - Abbreviated Version. A nested 5-fold cross-validation procedure repeated 100 times was used for feature selection and model evaluation.

SUBMITTER: Kim K 

PROVIDER: S-EPMC12559936 | biostudies-literature | 2025 Nov

REPOSITORIES: biostudies-literature