ArticleInternational journal of environmental research and public health2021
Exploring Factors for Predicting Anxiety Disorders of the Elderly Living Alone in South Korea Using Interpretable Machine Learning: A Population-Based Study.
Article in International journal of environmental research and public health, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 21 papers, 1 of them a synthesis that pooled it.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
21 citing papers in PubMed, 1 synthesis or guideline pooled it, 49 citations in OpenAlex.
- Mapping the global research landscape and trends of older people living alone: a bibliometric analysis.Frontiers in aging · 2025Pooled it
- Explainable AI for Digital Health: Predicting Depression Risk in Older Adults Living Alone Using Machine Learning.Healthcare (Basel, Switzerland) · 2026Article
- Machine learning in mental health promotion for older adults: a scoping review.BMC geriatrics · 2026Article
- Developmental trajectories of vocabulary and logical thinking skills across adolescence: a person-centered longitudinal study in China.BMC public health · 2026Article
- Contribution of social determinants to symptoms of generalized anxiety disorder.PLOS mental health · 2026Article
- Psychological and Sociodemographic Variables Associated with Increased Anxiety and Anxiety Symptoms in Older Adults: A Scoping Review.Geriatrics (Basel, Switzerland) · 2025Review
- Association of risk factors with mental illness in a rural community: insights from machine learning models.BJPsych open · 2025Article
- Factors and Reasons Associated with Hesitating to Seek Care for Migraine: Results of the OVERCOME (US) Study.Neurology and therapy · 2025Article
- A network analysis of depression and anxiety symptoms among Chinese elderly living alone: based on the 2017-2018 Chinese Longitudinal Healthy Longevity Survey (CLHLS).BMC psychiatry · 2025Article
- Network of anxiety and depression symptoms in older Chinese adults living alone: a cross-sectional study.Frontiers in psychiatry · 2025Article
- Which factors affect anxiety in rural older adults in China?Frontiers in public health · 2025Article
- Job satisfaction and psychological factors influence the caring behavior in nurses in Sierra Leone: a cross-sectional study.Frontiers in psychology · 2024Article
- Depressive Disorder Recognition Based on Frontal EEG Signals and Deep Learning.Sensors (Basel, Switzerland) · 2023Article
- Predicting unplanned readmission due to cardiovascular disease in hospitalized patients with cancer: a machine learning approach.Scientific reports · 2023Article
- Machine Learning Techniques Reveal Aberrated Multidimensional EEG Characteristics in Patients with Depression.Brain sciences · 2023Article
- Explainable artificial intelligence for mental health through transparency and interpretability for understandability.NPJ digital medicine · 2023Review
- Clinical decision support system for quality of life among the elderly: an approach using artificial neural network.BMC medical informatics and decision making · 2022Article
- Prediction of successful aging using ensemble machine learning algorithms.BMC medical informatics and decision making · 2022Article
- Aberrated Multidimensional EEG Characteristics in Patients with Generalized Anxiety Disorder: A Machine-Learning Based Analysis Framework.Sensors (Basel, Switzerland) · 2022Article
- Developing a Predictive Model for Depressive Disorders Using Stacking Ensemble and Naive Bayesian Nomogram: Using Samples Representing South Korea.Frontiers in psychiatry · 2021Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
1 author at 1 institution in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
This epidemiological study aimed to develop an X-AI that could explain groups with a high anxiety disorder risk in old age. To achieve this objective, (1) this study explored the predictors of senile anxiety using base models and meta models. (2) This study presented decision tree visualization that could help psychiatric consultants and primary physicians easily interpret the path of predicting high-risk groups based on major predictors derived from final machine learning models with the best performance. This study analyzed 1558 elderly (695 males and 863 females) who were 60 years or older and completed the Zung's Self-Rating Anxiety Scale (SAS). We used support vector machine (SVM), random forest, LightGBM, and Adaboost for the base model, a single predictive model, while using XGBoost algorithm for the meta model. The analysis results confirmed that the predictive performance of the "SVM + Random forest + LightGBM + AdaBoost + XGBoost model (stacking ensemble: accuracy 87.4%, precision 85.1%, recall 87.4%, and F1-score 85.5%)" was the best. Also, the results of this study showed that the elderly who often (or mostly) felt subjective loneliness, had a Self Esteem Scale score of 26 or less, and had a subjective communication with their family of 4 or less (on a 10-point scale) were the group with the highest risk anxiety disorder. The results of this study imply that it is necessary to establish a community-based mental health policy that can identify elderly groups with high anxiety risks based on multiple risk factors and manage them constantly.
Indexed as
Identifiers
What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.