Explainable machine learning model for pre‐frailty risk assessment in community‐dwelling older adults
Abstract Background Frailty in older adults is linked to increased risks and lower quality of life. Pre‐frailty, a condition preceding frailty, is intervenable, but its determinants and assessment are challenging. This study aims to develop and validate an explainable machine learning model for pre‐...
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| Format: | Article |
| Language: | English |
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Wiley
2024-12-01
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| Series: | Health Care Science |
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| Online Access: | https://doi.org/10.1002/hcs2.120 |
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| author | Chenlin Du Zeyu Zhang Baoqin Liu Zijian Cao Nan Jiang Zongjiu Zhang |
| author_facet | Chenlin Du Zeyu Zhang Baoqin Liu Zijian Cao Nan Jiang Zongjiu Zhang |
| author_sort | Chenlin Du |
| collection | DOAJ |
| description | Abstract Background Frailty in older adults is linked to increased risks and lower quality of life. Pre‐frailty, a condition preceding frailty, is intervenable, but its determinants and assessment are challenging. This study aims to develop and validate an explainable machine learning model for pre‐frailty risk assessment among community‐dwelling older adults. Methods The study included 3141 adults aged 60 or above from the China Health and Retirement Longitudinal Study. Pre‐frailty was characterized by one or two criteria from the physical frailty phenotype scale. We extracted 80 distinct features across seven dimensions to evaluate pre‐frailty risk. A model was constructed using recursive feature elimination and a stacking‐CatBoost distillation module on 80% of the sample and validated on a separate 20% holdout data set. Results The study used data from 2508 community‐dwelling older adults (mean age, 67.24 years [range, 60–96]; 1215 [48.44%] females) to develop a pre‐frailty risk assessment model. We selected 57 predictive features and built a distilled CatBoost model, which achieved the highest discrimination (AUROC: 0.7560 [95% CI: 0.7169, 0.7928]) on the 20% holdout data set. The living city, BMI, and peak expiratory flow (PEF) were the three most significant contributors to pre‐frailty risk. Physical and environmental factors were the top 2 impactful feature dimensions. Conclusions An accurate and interpretable pre‐frailty risk assessment framework using state‐of‐the‐art machine learning techniques and explanation methods has been developed. Our framework incorporates a wide range of features and determinants, allowing for a comprehensive and nuanced understanding of pre‐frailty risk. |
| format | Article |
| id | doaj-art-f3c26c83d5004c8e9c70546dce02d878 |
| institution | Kabale University |
| issn | 2771-1757 |
| language | English |
| publishDate | 2024-12-01 |
| publisher | Wiley |
| record_format | Article |
| series | Health Care Science |
| spelling | doaj-art-f3c26c83d5004c8e9c70546dce02d8782024-12-27T01:50:03ZengWileyHealth Care Science2771-17572024-12-013642643710.1002/hcs2.120Explainable machine learning model for pre‐frailty risk assessment in community‐dwelling older adultsChenlin Du0Zeyu Zhang1Baoqin Liu2Zijian Cao3Nan Jiang4Zongjiu Zhang5School of Biomedical Engineering Tsinghua University Beijing ChinaTsinghua Medicine, Tsinghua University Beijing ChinaInstitute for Hospital Management Tsinghua University Beijing ChinaSchool of Biomedical Engineering Tsinghua University Beijing ChinaSchool of Biomedical Engineering Tsinghua University Beijing ChinaSchool of Biomedical Engineering Tsinghua University Beijing ChinaAbstract Background Frailty in older adults is linked to increased risks and lower quality of life. Pre‐frailty, a condition preceding frailty, is intervenable, but its determinants and assessment are challenging. This study aims to develop and validate an explainable machine learning model for pre‐frailty risk assessment among community‐dwelling older adults. Methods The study included 3141 adults aged 60 or above from the China Health and Retirement Longitudinal Study. Pre‐frailty was characterized by one or two criteria from the physical frailty phenotype scale. We extracted 80 distinct features across seven dimensions to evaluate pre‐frailty risk. A model was constructed using recursive feature elimination and a stacking‐CatBoost distillation module on 80% of the sample and validated on a separate 20% holdout data set. Results The study used data from 2508 community‐dwelling older adults (mean age, 67.24 years [range, 60–96]; 1215 [48.44%] females) to develop a pre‐frailty risk assessment model. We selected 57 predictive features and built a distilled CatBoost model, which achieved the highest discrimination (AUROC: 0.7560 [95% CI: 0.7169, 0.7928]) on the 20% holdout data set. The living city, BMI, and peak expiratory flow (PEF) were the three most significant contributors to pre‐frailty risk. Physical and environmental factors were the top 2 impactful feature dimensions. Conclusions An accurate and interpretable pre‐frailty risk assessment framework using state‐of‐the‐art machine learning techniques and explanation methods has been developed. Our framework incorporates a wide range of features and determinants, allowing for a comprehensive and nuanced understanding of pre‐frailty risk.https://doi.org/10.1002/hcs2.120China Health and Retirement Longitudinal StudyChinese community‐dwelling older adultsexplainable machine learningpre‐frailtyrisk assessment |
| spellingShingle | Chenlin Du Zeyu Zhang Baoqin Liu Zijian Cao Nan Jiang Zongjiu Zhang Explainable machine learning model for pre‐frailty risk assessment in community‐dwelling older adults Health Care Science China Health and Retirement Longitudinal Study Chinese community‐dwelling older adults explainable machine learning pre‐frailty risk assessment |
| title | Explainable machine learning model for pre‐frailty risk assessment in community‐dwelling older adults |
| title_full | Explainable machine learning model for pre‐frailty risk assessment in community‐dwelling older adults |
| title_fullStr | Explainable machine learning model for pre‐frailty risk assessment in community‐dwelling older adults |
| title_full_unstemmed | Explainable machine learning model for pre‐frailty risk assessment in community‐dwelling older adults |
| title_short | Explainable machine learning model for pre‐frailty risk assessment in community‐dwelling older adults |
| title_sort | explainable machine learning model for pre frailty risk assessment in community dwelling older adults |
| topic | China Health and Retirement Longitudinal Study Chinese community‐dwelling older adults explainable machine learning pre‐frailty risk assessment |
| url | https://doi.org/10.1002/hcs2.120 |
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