Identifying the suicidal ideation risk group among older adults in rural areas: Developing a predictive model using machine learning methods

Abstract

Aims: The aim of this study was to develop a predictive model that can identify the suicidal ideation risk group among older adults in rural areas using machine learning methods.

Design: This study applied an exploratory, descriptive and cross-sectional design.

Methods: The participants were older adults (N = 650) aged over 65 living in rural areas of South Korea. Self-report questionnaires were used to collect the demographics, suicidal ideation, depression, socioeconomic information and basic health information from September to October 2020. The collected data were analysed using machine learning methods with R statistical software 4.1.0.

Results: The predictive models indicated that depression, pain, age and loneliness were significant factors of suicidal ideation. Good performance was observed based on the area under the receiver operating characteristic curve in the decision tree, random forest and logistic regression. Finally, the evaluation of model performance indicated moderate to high sensitivity and specificity.

Conclusion: The predictive models using machine learning methods may be useful to predict the risk of suicidal ideation. Furthermore, depression with pain, age and feelings of loneliness should be included in the initial screening to assess suicide risk among older adults in rural areas.

Impact: Identifying suicidal risk among older adults is challenging. Thus, employing predictive models that can assess depression, pain, age and loneliness can enable public healthcare providers to detect suicidal risk groups. Particularly, the presented models from this study can facilitate healthcare providers with initiating early interventions to prevent suicide among older adults in clinical and community nursing care settings.

Reporting method: The reporting of this study (Observational, cross-sectional study) conforms to the STROBE statement.

Patient or public contribution: No patient or public contribution. This study did not involve patients, service users, caregivers or members of the public.

Implication for the profession and/or patients care: Applying this model may help to prevent geriatric suicide because the nursing staff will have a greater awareness regarding the suicide ideation risk of older adults, thereby reducing the possibility of their suicide.

Keywords: aged; depression; loneliness; machine learning; nurses; pain; rural population; suicidal ideation.