Construction of disability risk prediction model for the elderly based on machine learning
Abstract The study aimed to develop a predictive model using machine learning algorithms, providing healthcare professionals with a novel tool for assessing disability risk in older adults. Data from the 2018 and 2020 waves of the China Health and Retirement Longitudinal Study were utilized, includi...
Saved in:
| Main Authors: | , , , , , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
Nature Portfolio
2025-05-01
|
| Series: | Scientific Reports |
| Subjects: | |
| Online Access: | https://doi.org/10.1038/s41598-025-01404-5 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1849728238651179008 |
|---|---|
| author | Jing Chen Yifei Ren Jie Ding Qingqing Hu Jiajia Xu Jun Luo Zhaowen Wu Ting Chu |
| author_facet | Jing Chen Yifei Ren Jie Ding Qingqing Hu Jiajia Xu Jun Luo Zhaowen Wu Ting Chu |
| author_sort | Jing Chen |
| collection | DOAJ |
| description | Abstract The study aimed to develop a predictive model using machine learning algorithms, providing healthcare professionals with a novel tool for assessing disability risk in older adults. Data from the 2018 and 2020 waves of the China Health and Retirement Longitudinal Study were utilized, including 3,172 participants aged 65 years and older with no baseline disability. In this study, five machine learning algorithms were employed to construct risk assessment and prediction models for disability in older adults. The Shapley Additive Explanations method was applied to analyze the independent predictors of disability risk. In total, 695 participants (21.9%) were disabled during follow-up. Among the five machine learning models, prediction models constructed using random forest and extreme gradient boosting methods showed superior performance, achieving F1 scores of 0.92 and 0.86 and accuracies of 0.92 and 0.85, respectively. Key predictors of disability risk included self-rated health, education, sleep duration, alcohol consumption, depressive symptoms, hypertension, and arthritis. The Machine learning models for assessing and predicting disability risk in older adults, particularly those developed using RF and XGBoost algorithms, exhibited strong predictive capabilities. These findings highlight the potential of these models for practical application in clinical and public health settings, warranting further exploration and validation. |
| format | Article |
| id | doaj-art-af68219aefc7454589d24976a089d487 |
| institution | DOAJ |
| issn | 2045-2322 |
| language | English |
| publishDate | 2025-05-01 |
| publisher | Nature Portfolio |
| record_format | Article |
| series | Scientific Reports |
| spelling | doaj-art-af68219aefc7454589d24976a089d4872025-08-20T03:09:35ZengNature PortfolioScientific Reports2045-23222025-05-0115111010.1038/s41598-025-01404-5Construction of disability risk prediction model for the elderly based on machine learningJing Chen0Yifei Ren1Jie Ding2Qingqing Hu3Jiajia Xu4Jun Luo5Zhaowen Wu6Ting Chu7School of Medical Technology and Information Engineering, Zhejiang Chinese Medical UniversitySchool of Nursing, Zhejiang Chinese Medical UniversitySchool of Nursing, Zhejiang Chinese Medical UniversitySchool of Nursing, Zhejiang Chinese Medical UniversitySchool of Nursing, Zhejiang Chinese Medical UniversitySchool of Nursing, Zhejiang Chinese Medical UniversitySchool of Nursing, Zhejiang Chinese Medical UniversitySchool of Nursing, Zhejiang Chinese Medical UniversityAbstract The study aimed to develop a predictive model using machine learning algorithms, providing healthcare professionals with a novel tool for assessing disability risk in older adults. Data from the 2018 and 2020 waves of the China Health and Retirement Longitudinal Study were utilized, including 3,172 participants aged 65 years and older with no baseline disability. In this study, five machine learning algorithms were employed to construct risk assessment and prediction models for disability in older adults. The Shapley Additive Explanations method was applied to analyze the independent predictors of disability risk. In total, 695 participants (21.9%) were disabled during follow-up. Among the five machine learning models, prediction models constructed using random forest and extreme gradient boosting methods showed superior performance, achieving F1 scores of 0.92 and 0.86 and accuracies of 0.92 and 0.85, respectively. Key predictors of disability risk included self-rated health, education, sleep duration, alcohol consumption, depressive symptoms, hypertension, and arthritis. The Machine learning models for assessing and predicting disability risk in older adults, particularly those developed using RF and XGBoost algorithms, exhibited strong predictive capabilities. These findings highlight the potential of these models for practical application in clinical and public health settings, warranting further exploration and validation.https://doi.org/10.1038/s41598-025-01404-5Prediction modelDisabilityMachine learningOlder adultsCohort study |
| spellingShingle | Jing Chen Yifei Ren Jie Ding Qingqing Hu Jiajia Xu Jun Luo Zhaowen Wu Ting Chu Construction of disability risk prediction model for the elderly based on machine learning Scientific Reports Prediction model Disability Machine learning Older adults Cohort study |
| title | Construction of disability risk prediction model for the elderly based on machine learning |
| title_full | Construction of disability risk prediction model for the elderly based on machine learning |
| title_fullStr | Construction of disability risk prediction model for the elderly based on machine learning |
| title_full_unstemmed | Construction of disability risk prediction model for the elderly based on machine learning |
| title_short | Construction of disability risk prediction model for the elderly based on machine learning |
| title_sort | construction of disability risk prediction model for the elderly based on machine learning |
| topic | Prediction model Disability Machine learning Older adults Cohort study |
| url | https://doi.org/10.1038/s41598-025-01404-5 |
| work_keys_str_mv | AT jingchen constructionofdisabilityriskpredictionmodelfortheelderlybasedonmachinelearning AT yifeiren constructionofdisabilityriskpredictionmodelfortheelderlybasedonmachinelearning AT jieding constructionofdisabilityriskpredictionmodelfortheelderlybasedonmachinelearning AT qingqinghu constructionofdisabilityriskpredictionmodelfortheelderlybasedonmachinelearning AT jiajiaxu constructionofdisabilityriskpredictionmodelfortheelderlybasedonmachinelearning AT junluo constructionofdisabilityriskpredictionmodelfortheelderlybasedonmachinelearning AT zhaowenwu constructionofdisabilityriskpredictionmodelfortheelderlybasedonmachinelearning AT tingchu constructionofdisabilityriskpredictionmodelfortheelderlybasedonmachinelearning |