Prediction of late-onset depression in the elderly Korean population using machine learning algorithms
Abstract Late-onset depression (LOD) refers to depression that newly appears in elderly individuals without prior depression episodes. Predicting future depression is crucial for mitigating the risk of major depression in prospective patients. This study aims to develop machine learning models to pr...
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| Format: | Article |
| Language: | English |
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Nature Portfolio
2025-01-01
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| Series: | Scientific Reports |
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| Online Access: | https://doi.org/10.1038/s41598-025-85157-1 |
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| author | Jong Wan Park Chang Woo Ko Diane Youngmi Lee Jae Chul Kim |
| author_facet | Jong Wan Park Chang Woo Ko Diane Youngmi Lee Jae Chul Kim |
| author_sort | Jong Wan Park |
| collection | DOAJ |
| description | Abstract Late-onset depression (LOD) refers to depression that newly appears in elderly individuals without prior depression episodes. Predicting future depression is crucial for mitigating the risk of major depression in prospective patients. This study aims to develop machine learning models to predict future depression. Using public data from the nationwide panel survey ‘Korean Longitudinal Study of Aging,’ we employed latent growth modeling and growth mixture modeling to identify four latent classes of depression trajectories in the elderly Korean population. Based on the results of binary logistic regression, we selected 12 variables capable of distinguishing the LOD population from the reference population and tested 12 machine learning (ML) algorithms. While most ML algorithms showed acceptable predictive capability, Random Forest Classifier and Gradient Boosting Classifier demonstrated superior performance. Consequently, we successfully established new ML-based LOD prediction programs. These programs could be further developed into self-checking online tools, expected to serve as decision support systems for primary medical care and health screening services. |
| format | Article |
| id | doaj-art-d7649dcce47b45b188fb45d7376260ec |
| institution | Kabale University |
| issn | 2045-2322 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | Nature Portfolio |
| record_format | Article |
| series | Scientific Reports |
| spelling | doaj-art-d7649dcce47b45b188fb45d7376260ec2025-01-12T12:19:53ZengNature PortfolioScientific Reports2045-23222025-01-0115111210.1038/s41598-025-85157-1Prediction of late-onset depression in the elderly Korean population using machine learning algorithmsJong Wan Park0Chang Woo Ko1Diane Youngmi Lee2Jae Chul Kim3Department of Counseling, Graduate School of Hannam UniversityDepartment of Biomedical Science, Seoul National University College of MedicineDepartment of Art Therapy, Daegu Cyber UniversityDepartment of Counseling, Graduate School of Hannam UniversityAbstract Late-onset depression (LOD) refers to depression that newly appears in elderly individuals without prior depression episodes. Predicting future depression is crucial for mitigating the risk of major depression in prospective patients. This study aims to develop machine learning models to predict future depression. Using public data from the nationwide panel survey ‘Korean Longitudinal Study of Aging,’ we employed latent growth modeling and growth mixture modeling to identify four latent classes of depression trajectories in the elderly Korean population. Based on the results of binary logistic regression, we selected 12 variables capable of distinguishing the LOD population from the reference population and tested 12 machine learning (ML) algorithms. While most ML algorithms showed acceptable predictive capability, Random Forest Classifier and Gradient Boosting Classifier demonstrated superior performance. Consequently, we successfully established new ML-based LOD prediction programs. These programs could be further developed into self-checking online tools, expected to serve as decision support systems for primary medical care and health screening services.https://doi.org/10.1038/s41598-025-85157-1Late-onset depressionLongitudinal study of agingDepression trajectoriesMachine learning algorithmsPredictive performance |
| spellingShingle | Jong Wan Park Chang Woo Ko Diane Youngmi Lee Jae Chul Kim Prediction of late-onset depression in the elderly Korean population using machine learning algorithms Scientific Reports Late-onset depression Longitudinal study of aging Depression trajectories Machine learning algorithms Predictive performance |
| title | Prediction of late-onset depression in the elderly Korean population using machine learning algorithms |
| title_full | Prediction of late-onset depression in the elderly Korean population using machine learning algorithms |
| title_fullStr | Prediction of late-onset depression in the elderly Korean population using machine learning algorithms |
| title_full_unstemmed | Prediction of late-onset depression in the elderly Korean population using machine learning algorithms |
| title_short | Prediction of late-onset depression in the elderly Korean population using machine learning algorithms |
| title_sort | prediction of late onset depression in the elderly korean population using machine learning algorithms |
| topic | Late-onset depression Longitudinal study of aging Depression trajectories Machine learning algorithms Predictive performance |
| url | https://doi.org/10.1038/s41598-025-85157-1 |
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