Home Health Care Science Article
PDF (3.4 MB)
Cite
EndNote(RIS) BibTeX
Collect
Submit Manuscript
Original Article | Open Access

Explainable machine learning model for pre‐frailty risk assessment in community‐dwelling older adults

Chenlin Du1,2, Zeyu Zhang2,3, Baoqin Liu3,4, Zijian Cao1,2, Nan Jiang1,2,3 (), Zongjiu Zhang1,2,3()
School of Biomedical Engineering, Tsinghua University, Beijing, China
Tsinghua Medicine, Tsinghua University, Beijing, China
Institute for Hospital Management, Tsinghua University, Beijing, China
Department of Gynecology of Traditional Chinese Medicine, China‐Japan Friendship Hospital, Beijing, China

Chenlin Du, Zeyu Zhang, and Baoqin Liu contributed equally to this study.

Show Author Information

Graphical Abstract

View original image Download original image
The PACIFIC framework utilizes the CHARLS (2011) data set, which consists of 80 features organized into seven dimensions, and integrates an efficient recursive feature elimination method with a stacking‐CatBoost distillation module and explainable AI techniques to analyze pre‐frailty risk. By employing Tree Explainer for SHAP values and SAGE values for feature contribution, PACIFIC provides individualized explanations that highlight the impact of each risk factor on the overall risk score, thereby enhancing clinical credibility.

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.

Keywords

China Health and Retirement Longitudinal StudyChinese community‐dwelling older adultsexplainable machine learningpre‐frailtyrisk assessment

References

1

Hoogendijk EO, Afilalo J, Ensrud KE, Kowal P, Onder G, Fried LP. Frailty: implications for clinical practice and public health. Lancet. 2019;394(10206):1365–75. https://doi.org/10.1016/S0140‐6736(19)31786‐6

Crossref Google Scholar
2

Fan J, Yu C, Guo Y, Bian Z, Sun Z, Yang L, et al. Frailty index and all‐cause and cause‐specific mortality in Chinese adults: a prospective cohort study. Lancet Public Health. 2020;5(12):e650–60. https://doi.org/10.1016/S2468‐2667(20)30113‐4

Crossref Google Scholar
3

Fan L, Tian Y, Wang J, Ding Y, Wang S, Xue H, et al. Frailty predicts increased health care utilization among community‐dwelling older adults: a longitudinal study in China. J Am Med Dir Assoc. 2021;22(9):1819–24. https://doi.org/10.1016/j.jamda.2021.01.082

Crossref Google Scholar
4

Sezgin D, Liew A, O'Donovan MR, O'Caoimh R. Pre‐frailty as a multi‐dimensional construct: a systematic review of definitions in the scientific literature. Geriatr Nurs (Minneap). 2020;41(2):139–46. https://doi.org/10.1016/j.gerinurse.2019.08.004

Crossref Google Scholar
5

He B, Ma Y, Wang C, Jiang M, Geng C, Chang X, et al. Prevalence and risk factors for frailty among community‐dwelling older people in China: a systematic review and meta‐analysis. J Nutr Health Aging. 2019;23(5):442–50. https://doi.org/10.1007/s12603‐019‐1179‐9

Crossref Google Scholar
6

Gené Huguet L, Navarro González M, Kostov B, Ortega Carmona M, Colungo Francia C, Carpallo Nieto M, et al. Pre frail 80: multifactorial intervention to prevent progression of pre‐frailty to frailty in the elderly. J Nutr Health Aging. 2018;22(10):1266–74. https://doi.org/10.1007/s12603‐018‐1089‐2

Crossref Google Scholar
7

Sajeev S, Champion S, Maeder A, Gordon S. Machine learning models for identifying pre‐frailty in community dwelling older adults. BMC Geriatr. 2022;22(1):794. https://doi.org/10.1186/s12877‐022‐03475‐9

Crossref Google Scholar
8

Teh SK, Rawtaer I, Tan HP. Predictive accuracy of digital biomarker technologies for detection of mild cognitive impairment and pre‐frailty amongst older adults: a systematic review and meta‐analysis. IEEE J Biomed Health Inform. 2022;26(8):3638–48. https://doi.org/10.1109/JBHI.2022.3185798

Crossref Google Scholar
9

Akbari G, Nikkhoo M, Wang L, Chen CPC, Han DS, Lin YH, et al. Frailty level classification of the community elderly using microsoft kinect‐based skeleton pose: a machine learning approach. Sensors. 2021;21(12):4017. https://doi.org/10.3390/s21124017

Crossref Google Scholar
10

Ju C, Zhou J, Lee S, Tan MS, Liu T, Bazoukis G, et al. Derivation of an electronic frailty index for predicting short‐term mortality in heart failure: a machine learning approach. ESC Heart Failure. 2021;8(4):2837–45. https://doi.org/10.1002/ehf2.13358

Crossref Google Scholar
11

Lv W, Liao H, Wang X, Yu S, Peng Y, Li X, et al. A machine learning‐based assistant tool for early frailty screening of patients receiving maintenance hemodialysis. Int Urol Nephrol. 2024;56(1):223–35. https://doi.org/10.1007/s11255‐023‐03640‐y

Crossref Google Scholar
12

Wu Y, Jia M, Xiang C, Fang Y. Latent trajectories of frailty and risk prediction models among geriatric community dwellers: an interpretable machine learning perspective. BMC Geriatr. 2022;22(1):900. https://doi.org/10.1186/s12877‐022‐03576‐5

Crossref Google Scholar
13

Zhao Y, Hu Y, Smith JP, Strauss J, Yang G. Cohort profile: the China health and retirement longitudinal study (CHARLS). Int J Epidemiol. 2014;43(1):61–8. https://doi.org/10.1093/ije/dys203

Crossref Google Scholar
14

Payrovnaziri SN, Xing A, Salman S, Liu X, Bian J, He Z. Assessing the impact of imputation on the interpretations of prediction models: a case study on mortality prediction for patients with acute myocardial infarction. AMIA Joint Summits Transl Sci. 2021;2021:465–74.

Crossref Google Scholar
15

Wu C, Smit E, Xue QL, Odden MC. Prevalence and correlates of frailty among community‐dwelling Chinese older adults: the China health and retirement longitudinal study. J Gerontol: Ser A. 2018;73(1):102–8. https://doi.org/10.1093/gerona/glx098

Crossref Google Scholar
16

Fried LP, Tangen CM, Walston J, Newman AB, Hirsch C, Gottdiener J, et al. Frailty in older adults: evidence for a phenotype. J Gerontol A Biol Sci Med Sci. 2001;56(3):M146–57. https://doi.org/10.1093/gerona/56.3.m146

Crossref Google Scholar
17

Lundberg SM, Erion G, Chen H, DeGrave A, Prutkin JM, Nair B, et al. From local explanations to global understanding with explainable AI for trees. Nat Mach Intell. 2020;2(1):56–67. https://doi.org/10.1038/s42256‐019‐0138‐9

Crossref Google Scholar
18
Covert I, Lundberg S, Lee SI. Explaining by removing: a unified framework for model explanation. 2020. http://arxiv.org/abs/2011.14878
19

Qiu W, Chen H, Dincer AB, Lundberg S, Kaeberlein M, Lee SI. Interpretable machine learning prediction of all‐cause mortality. Commun Med. 2022;2:125. https://doi.org/10.1038/s43856‐022‐00180‐x

Crossref Google Scholar
20
Prokhorenkova L, Gusev G, Vorobev A., Dorogush A. V., Gulin A. CatBoost: unbiased boosting with categorical features. In: Proceedings of the 32nd International Conference on Neural Information Processing Systems. Montréal, Canada: Curran Associates Inc; 2018. p. 6639–49. https://doi.org/10.5555/3327757.3327770
21
Fakoor R, Mueller J, Erickson N, Chaudhari P, Smola AJ. Fast, accurate, and simple models for tabular data via augmented distillation. In: Proceedings of the 34th International Conference on Neural Information Processing Systems. Vancouver, BC, Canada: Curran Associates Inc; 2020. p. 727. https://doi.org/10.5555/3495724.3496451
22

Parmanto B, Munro PW, Doyle HR. Reducing variance of committee prediction with resampling techniques. Connection Sci. 1996;8(3–4):405–26. https://doi.org/10.1080/095400996116848

Crossref Google Scholar
23
Caruana R, Niculescu‐Mizil A, Crew G, Ksikes A. Ensemble selection from libraries of models.In: Proceedings of the twenty‐first international conference on Machine learning. Banff, Alberta, Canada: Association for Computing Machinery; 2004. p. 18. https://doi.org/10.1145/1015330.1015432
Crossref
24
Roy D, Murty KSR, Mohan CK. Feature selection using Deep Neural Networks. 2015 International Joint Conference on Neural Networks (IJCNN), Killarney, Ireland, 2015:1–6. https://doi.org/10.1109/IJCNN.2015.7280626
Crossref
25
Ke G, Meng Q, Finley T, Wang T, Chen W, Ma W, et al. LightGBM: a highly efficient gradient boosting decision tree. In: Proceedings of the 31st International Conference on Neural Information Processing Systems. Long Beach, California, USA: Curran Associates Inc; 2017. p. 3149–57. https://doi.org/10.5555/3294996.3295074
26

Altmann A, Toloşi L, Sander O, Lengauer T. Permutation importance: a corrected feature importance measure. Bioinformatics. 2010;26(10):1340–7. https://doi.org/10.1093/bioinformatics/btq134

Crossref Google Scholar
27
Erickson N, Mueller J, Shirkov A, Zhang H, Larroy P, Li M, et al. AutoGluon‐tabular: robust and accurate AutoML for structured data. 2020. http://arxiv.org/abs/2003.06505
28

Hancock JT, Khoshgoftaar TM. CatBoost for big data: an interdisciplinary review. J Big Data. 2020;7(1):94. https://doi.org/10.1186/s40537‐020‐00369‐8

Crossref Google Scholar
29

Ramírez S, Quiroz AJ, Riascos AJ. A supervised clustering MCMC methodology for large categorical feature spaces. Stat Methods Med Res. 2021;30(7):1708–24. https://doi.org/10.1177/09622802211009258

Crossref Google Scholar
30

Kim M, Lee Y, Kim EY, Park Y. Mediating effect of waist: height ratio on the association between BMI and frailty: the Korean frailty and aging cohort study. Br J Nutr. 2020;124(5):513–20. https://doi.org/10.1017/s0007114519002058

Crossref Google Scholar
31

Lee Y, Kim J, Han ES, Ryu M, Cho Y, Chae S. Frailty and body mass index as predictors of 3‐year mortality in older adults living in the community. Gerontology. 2014;60(6):475–82. https://doi.org/10.1159/000362330

Crossref Google Scholar
32

Vaz Fragoso CA, Enright PL, McAvay G, Van Ness PH, Gill TM. Frailty and respiratory impairment in older persons. Am J Med. 2012;125(1):79–86. https://doi.org/10.1016/j.amjmed.2011.06.024

Crossref Google Scholar
33

Magave JA, Bezerra SJS, Matos AP, Pinto ACPN, Pegorari MS, Ohara DG. Peak expiratory flow as an index of frailty syndrome in older adults: a cross‐sectional study. J Nutr Health Aging. 2020;24(9):993–8. https://doi.org/10.1007/s12603‐020‐1509‐y

Crossref Google Scholar
34

Goisser S, Guyonnet S, Volkert D. The role of nutrition in frailty: an overview. J Frailty Aging. 2016;5(2):74–7. https://doi.org/10.14283/jfa.2016.87

Crossref Google Scholar
35

Rockwood K. Changes with age in the distribution of a frailty index. Mech Ageing Dev. 2004;125(7):517–9. https://doi.org/10.1016/j.mad.2004.05.003

Crossref Google Scholar
36

Liu LK, Lee WJ, Chen LY, Hwang AC, Lin MH, Peng LN, et al. Association between frailty, osteoporosis, falls and hip fractures among community‐dwelling people aged 50 years and older in Taiwan: results from Ⅰ‐lan longitudinal aging study. PLoS One. 2015;10(9):e0136968. https://doi.org/10.1371/journal.pone.0136968

Crossref Google Scholar
37

Salaffi F, Di Matteo A, Farah S, Di Carlo M. Inflammaging and frailty in immune‐mediated rheumatic diseases: how to address and score the issue. Clin Rev Allergy Immunol. 2023;64(2):206–21. https://doi.org/10.1007/s12016‐022‐08943‐z

Crossref Google Scholar
38

Gao RC, Wu ZG, Wu ZZ, Hao M, Wu GC. Frailty in rheumatoid arthritis: a systematic review and meta‐analysis. Joint Bone Spine. 2022;89(4):105343. https://doi.org/10.1016/j.jbspin.2022.105343

Crossref Google Scholar
39

Deng H, Eftekhari Z, Carlin C, Veerapong J, Fournier KF, Johnston FM, et al. Development and validation of an explainable machine learning model for major complications after cytoreductive surgery. JAMA Netw Open. 2022;5(5):e2212930. https://doi.org/10.1001/jamanetworkopen.2022.12930

Crossref Google Scholar
40

Swap CJ. Value and limitations of chest pain history in the evaluation of patients with suspected acute coronary syndromes. JAMA. 2005;294(20):2623–9. https://doi.org/10.1001/jama.294.20.2623

Crossref Google Scholar
41

Sergi G, Veronese N, Fontana L, De Rui M, Bolzetta F, Zambon S, et al. Pre‐frailty and risk of cardiovascular disease in elderly men and women. J Am Coll Cardiol. 2015;65(10):976–83. https://doi.org/10.1016/j.jacc.2014.12.040

Crossref Google Scholar
42

Liu H, Chen B, Li Y, Morrow‐Howell N. Neighborhood resources associated with frailty trajectories over time among community‐dwelling older adults in China. Health Place. 2022;74:102738. https://doi.org/10.1016/j.healthplace.2021.102738

Crossref Google Scholar
43

Ye B, Chen H, Huang L, Ruan Y, Qi S, Guo Y, et al. Changes in frailty among community‐dwelling Chinese older adults and its predictors: evidence from a two‐year longitudinal study. BMC Geriatr. 2020;20(1):130. https://doi.org/10.1186/s12877‐020‐01530‐x

Crossref Google Scholar
44

Trombetti A, Hars M, Hsu FC, Reid KF, Church TS, Gill TM, et al. Effect of physical activity on frailty: secondary analysis of a randomized controlled trial. Ann Intern Med. 2018;168(5):309–16. https://doi.org/10.7326/M16‐2011

Crossref Google Scholar
45

Chen X, Giles J, Yao Y, Yip W, Meng Q, Berkman L, et al. The path to healthy ageing in China: a Peking University‐Lancet Commission. Lancet. 2022;400(10367):1967–2006. https://doi.org/10.1016/S0140‐6736(22)01546‐X

Crossref Google Scholar
46

Erion G, Janizek JD, Hudelson C, Utarnachitt RB, McCoy AM, Sayre MR, et al. A cost‐aware framework for the development of AI models for healthcare applications. Nat Biomed Eng. 2022;6(12):1384–98. https://doi.org/10.1038/s41551‐022‐00872‐8

Crossref Google Scholar
47

Janizek JD, Dincer AB, Celik S, Chen H, Chen W, Naxerova K, et al. Uncovering expression signatures of synergistic drug responses via ensembles of explainable machine‐learning models. Nat Biomed Eng. 2023;7(6):811–29. https://doi.org/10.1038/s41551‐023‐01034‐0

Crossref Google Scholar
48

Zimmermann J, Hansen S, Wagner M. Home environment and frailty in very old adults. Zeitschrift für Gerontologie und Geriatrie. 2021;54(Suppl 2):114–9. https://doi.org/10.1007/s00391‐021‐01969‐6

Crossref Google Scholar
49

Ma L, Tang Z, Zhang L, Sun F, Li Y, Chan P. Prevalence of frailty and associated factors in the community‐dwelling population of China. J Am Geriatr Soc. 2018;66(3):559–64. https://doi.org/10.1111/jgs.15214

Crossref Google Scholar
50

Sinclair DR, Maharani A, Chandola T, Bower P, Hanratty B, Nazroo J, et al. Frailty among older adults and its distribution in England. J Frailty Aging. 2022;11(2):163–8. https://doi.org/10.14283/jfa.2021.55

Crossref Google Scholar
51
China statistical yearbook, National bureau of statistics of the People's Republic of China. https://www.stats.gov.cn/sj/ndsj/2011/indexeh.htm
52

Qi X, Li Y, Hu J, Meng L, Zeng P, Shi J, et al. Prevalence of social frailty and its associated factors in the older Chinese population: a national cross‐sectional study. BMC Geriatr. 2023;23(1):532. https://doi.org/10.1186/s12877‐023‐04241‐1

Crossref Google Scholar
53

Zacharaki EI, Deltouzos K, Kalogiannis S, Kalamaras I, Bianconi L, Degano C, et al. FrailSafe: an ICT platform for unobtrusive sensing of Multi‐Domain frailty for personalized interventions. IEEE J Biomed Health Inform. 2020;24(6):1557–68. https://doi.org/10.1109/JBHI.2020.2986918

Crossref Google Scholar
54

Sha S, Pan Y, Xu Y, Chen L. Associations between loneliness and frailty among older adults: evidence from the China Health and Retirement Longitudinal Study. BMC Geriatr. 2022;22(1):537. https://doi.org/10.1186/s12877‐022‐03044‐0

Crossref Google Scholar
Health Care Science
Volume 3 Issue 6,
December 2024
Pages 426-437
DOI: 10.1002/hcs2.120
Cite this article:
Du C, Zhang Z, Liu B, et al. Explainable machine learning model for pre‐frailty risk assessment in community‐dwelling older adults. Health Care Science, 2024, 3(6): 426-437. https://doi.org/10.1002/hcs2.120
Metrics & Citations  
Article History
Copyright
Rights and Permissions
Return