Latent trajectories of frailty and risk prediction models among geriatric community dwellers: an interpretable machine learning perspective
Abstract Background This study aimed to identify long-term frailty trajectories among older adults (≥65) and construct interpretable prediction models to assess the risk of developing abnormal frailty trajectory among older adults and examine significant factors related to the progression of frailty...
| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
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BMC
2022-11-01
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| Series: | BMC Geriatrics |
| Subjects: | |
| Online Access: | https://doi.org/10.1186/s12877-022-03576-5 |
| _version_ | 1811215931629633536 |
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| author | Yafei Wu Maoni Jia Chaoyi Xiang Ya Fang |
| author_facet | Yafei Wu Maoni Jia Chaoyi Xiang Ya Fang |
| author_sort | Yafei Wu |
| collection | DOAJ |
| description | Abstract Background This study aimed to identify long-term frailty trajectories among older adults (≥65) and construct interpretable prediction models to assess the risk of developing abnormal frailty trajectory among older adults and examine significant factors related to the progression of frailty. Methods This study retrospectively collected data from the Chinese Longitudinal Healthy Longevity and Happy Family Study between 2002 and 2018 (N = 4083). Frailty was defined by the frailty index. The whole study consisted of two phases of tasks. First, group-based trajectory modeling was used to identify frailty trajectories. Second, easy-to-access epidemiological data was utilized to construct machine learning algorithms including naïve bayes, logistic regression, decision tree, support vector machine, random forest, artificial neural network, and extreme gradient boosting to predict the risk of long-term frailty trajectories. Further, Shapley additive explanations was employed to identify feature importance and open-up the black box model of machine learning to further strengthen decision makers’ trust in the model. Results Two distinct frailty trajectories (stable-growth: 82.54%, rapid-growth: 17.46%) were identified. Compared with other algorithms, random forest performed relatively better in distinguishing the stable-growth and rapid-growth groups. Physical function including activities of daily living and instrumental activities of daily living, marital status, weight, and cognitive function were top five predictors. Conclusions Interpretable machine learning can achieve the primary goal of risk stratification and make it more transparent in individual prediction beneficial to primary screening and tailored prevention. |
| first_indexed | 2024-04-12T06:31:38Z |
| format | Article |
| id | doaj.art-a59fa81524534dd5984c08033b781d2a |
| institution | Directory Open Access Journal |
| issn | 1471-2318 |
| language | English |
| last_indexed | 2024-04-12T06:31:38Z |
| publishDate | 2022-11-01 |
| publisher | BMC |
| record_format | Article |
| series | BMC Geriatrics |
| spelling | doaj.art-a59fa81524534dd5984c08033b781d2a2022-12-22T03:44:01ZengBMCBMC Geriatrics1471-23182022-11-0122111210.1186/s12877-022-03576-5Latent trajectories of frailty and risk prediction models among geriatric community dwellers: an interpretable machine learning perspectiveYafei Wu0Maoni Jia1Chaoyi Xiang2Ya Fang3School of Public Health, Xiamen UniversitySchool of Public Health, Xiamen UniversitySchool of Public Health, Xiamen UniversitySchool of Public Health, Xiamen UniversityAbstract Background This study aimed to identify long-term frailty trajectories among older adults (≥65) and construct interpretable prediction models to assess the risk of developing abnormal frailty trajectory among older adults and examine significant factors related to the progression of frailty. Methods This study retrospectively collected data from the Chinese Longitudinal Healthy Longevity and Happy Family Study between 2002 and 2018 (N = 4083). Frailty was defined by the frailty index. The whole study consisted of two phases of tasks. First, group-based trajectory modeling was used to identify frailty trajectories. Second, easy-to-access epidemiological data was utilized to construct machine learning algorithms including naïve bayes, logistic regression, decision tree, support vector machine, random forest, artificial neural network, and extreme gradient boosting to predict the risk of long-term frailty trajectories. Further, Shapley additive explanations was employed to identify feature importance and open-up the black box model of machine learning to further strengthen decision makers’ trust in the model. Results Two distinct frailty trajectories (stable-growth: 82.54%, rapid-growth: 17.46%) were identified. Compared with other algorithms, random forest performed relatively better in distinguishing the stable-growth and rapid-growth groups. Physical function including activities of daily living and instrumental activities of daily living, marital status, weight, and cognitive function were top five predictors. Conclusions Interpretable machine learning can achieve the primary goal of risk stratification and make it more transparent in individual prediction beneficial to primary screening and tailored prevention.https://doi.org/10.1186/s12877-022-03576-5Frailty trajectories; machine learningGroup-based trajectory modelingSHapley additive exPlanations |
| spellingShingle | Yafei Wu Maoni Jia Chaoyi Xiang Ya Fang Latent trajectories of frailty and risk prediction models among geriatric community dwellers: an interpretable machine learning perspective BMC Geriatrics Frailty trajectories; machine learning Group-based trajectory modeling SHapley additive exPlanations |
| title | Latent trajectories of frailty and risk prediction models among geriatric community dwellers: an interpretable machine learning perspective |
| title_full | Latent trajectories of frailty and risk prediction models among geriatric community dwellers: an interpretable machine learning perspective |
| title_fullStr | Latent trajectories of frailty and risk prediction models among geriatric community dwellers: an interpretable machine learning perspective |
| title_full_unstemmed | Latent trajectories of frailty and risk prediction models among geriatric community dwellers: an interpretable machine learning perspective |
| title_short | Latent trajectories of frailty and risk prediction models among geriatric community dwellers: an interpretable machine learning perspective |
| title_sort | latent trajectories of frailty and risk prediction models among geriatric community dwellers an interpretable machine learning perspective |
| topic | Frailty trajectories; machine learning Group-based trajectory modeling SHapley additive exPlanations |
| url | https://doi.org/10.1186/s12877-022-03576-5 |
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