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. 2023 Sep 12:11:1257818.
doi: 10.3389/fpubh.2023.1257818. eCollection 2023.

Predicting unmet activities of daily living needs among the oldest old with disabilities in China: a machine learning approach

Kun Wang  1   2 , Jinxu Zhao  1 , Jie Hu  3 , Dan Liang  4 , Yansong Luo  1
Affiliations

Predicting unmet activities of daily living needs among the oldest old with disabilities in China: a machine learning approach

Kun Wang et al. Front Public Health. .

Abstract

Background: The ageing population in China has led to a significant increase in the number of older persons with disabilities. These individuals face substantial challenges in accessing adequate activities of daily living (ADL) assistance. Unmet ADL needs among this population can result in severe health consequences and strain an already burdened care system. This study aims to identify the factors influencing unmet ADL needs of the oldest old (those aged 80 and above) with disabilities using six machine learning methods.

Methods: Drawing from the Chinese Longitudinal Healthy Longevity Survey (CLHLS) 2017-2018 data, we employed six machine learning methods to predict unmet ADL needs among the oldest old with disabilities. The predictive effects of various factors on unmet ADL needs were explored using Shapley Additive exPlanations (SHAP).

Results: The Random Forest model showed the highest prediction accuracy among the six machine learning methods tested. SHAP analysis based on the Random Forest model revealed that factors such as household registration, disability class, economic rank, self-rated health, caregiver willingness, perceived control, economic satisfaction, pension, educational attainment, financial support given to children, living arrangement, number of children, and primary caregiver played significant roles in the unmet ADL needs of the oldest old with disabilities.

Conclusion: Our study highlights the importance of socioeconomic factors (e.g., household registration and economic rank), health status (e.g., disability class and self-rated health), and caregiving relationship factors (e.g., caregiver willingness and perceived control) in reducing unmet ADL needs among the oldest old with disabilities in China. Government interventions aimed at bridging the urban-rural divide, targeting groups with deteriorating health status, and enhancing caregiver skills are essential for ensuring the well-being of this vulnerable population. These findings can inform policy decisions and interventions to better address the unmet ADL needs among the oldest old with disabilities.

Keywords: ADL; Shap; machine learning; oldest old; unmet needs.

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Conflict of interest statement

The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

Figures

Figure 1
Figure 1
Participant Selection Flowchart.
Figure 2
Figure 2
AUROC of Six Machine Learning Models.
Figure 3
Figure 3
Variable Importance in Random Forest Model. (A) Mean SAHP value (B) SHAP value.
See this image and copyright information in PMC

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