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

Kyungwon Kim^1,2,3, Eunsoo Moon^1,2,3,4, Hyunju Lim^1,2

¹Department of Psychiatry, Pusan National University Hospital, Busan, Korea.

Clinical Psychopharmacology and Neuroscience : the Official Scientific Journal of the Korean College of Neuropsychopharmacology

|October 26, 2025

Summary

This summary is machine-generated.

Predicting suicidal ideation in older adults is possible using self-report scales. Machine learning models, particularly those using more items from the Patient Health Questionnaire-9 (PHQ-9), show high accuracy in identifying at-risk individuals for suicide prevention.

Keywords:

Aged Community health services Machine learning Suicide Surveys and questionnaires

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Area of Science:

Gerontology
Public Health
Computational Psychiatry

Background:

Suicide is a critical public health concern, with older adults facing elevated risks.
Early identification of suicidal ideation is crucial for effective intervention and prevention strategies.

Purpose of the Study:

To develop a machine learning model for predicting suicidal ideation in community-dwelling older adults.
To evaluate the efficacy of psychiatric self-report scales in identifying at-risk elderly individuals.

Main Methods:

Assessed 238 older adults using PHQ-9, GAD-7, PSS-10, and WHOQOL-BREF.
Employed a nested 5-fold cross-validation with 100 repetitions for feature selection and model evaluation.
Utilized various machine learning classifiers, including SVM, Random Forest, and Gradient Boosting.

Main Results:

Model performance, measured by AUC, increased from 0.835 to 0.892 as PHQ-9 items increased from two to six.
Using nine stable features improved the AUC to 0.904, demonstrating the benefit of informative items.
The study confirmed the predictive power of psychiatric self-report scales for suicidal ideation.

Conclusions:

Psychiatric self-report scales effectively predict suicidal ideation risk in older adults.
Optimizing feature selection enhances predictive model accuracy for early identification systems.
Findings support community-based suicide prevention programs incorporating screening for at-risk elderly individuals.