An explainable predictive model for anxiety symptoms risk among Chinese older adults with abdominal obesity using a machine learning and SHapley Additive exPlanations approach

Abstract

Background: Early detection of anxiety symptoms can support early intervention and may help reduce the burden of disease in later life in the elderly with abdominal obesity, thereby increasing the chances of healthy aging. The objective of this research is to formulate and validate a predictive model that forecasts the probability of developing anxiety symptoms in elderly Chinese individuals with abdominal obesity.

Method: This research's model development and internal validation encompassed 2,427 participants from the 2017-2018 Study of the Chinese Longitudinal Healthy Longevity Survey (CLHLS). Forty-six variables were defined based on the Health Ecology Model (HEM) theoretical framework. Key variables were screened using LASSO regression, and the XGBoost (Extreme Gradient Boosting) model was further introduced to forecast the risk of developing anxiety symptoms in the elderly with abdominal obesity. SHapley Additive exPlanations (SHAP) was adopted to further interpret and show how the eigenvalues contributed to the model predictions.

Results: A total of 240 participants (9.89%) with anxiety symptoms out of 2,427 participants were included. LASSO regression identified nine key variables: looking on the bright side, self-reported economic status, self-reported quality of life, self-reported health status, watching TV or listening to the radio, feeling energetic, feeling ashamed/regretful/guilty, feeling angry, and fresh fruits. All the evaluation indicators of the XGBoost model showed good predictive efficacy. Based on the significance of the features identified by SHAP (Model Interpretation Methodology), the feature 'looking on the bright side' was the most important, and the feature 'self-reported quality of life' was the least important. The SHAP beeswarm plot illustrated the impacts of features affected by XGBoost.

Conclusion: Utilizing machine learning techniques, our predictive model can precisely evaluate the risk of anxiety symptoms among elderly individuals with abdominal obesity, facilitating the timely adoption of targeted intervention measures. The integration of XGBoost and SHAP offers transparent interpretations for customized risk forecasts.

Keywords: SHAP; XGBoost; abdominal obesity; anxiety symptoms; older adults.

Figure 1

Data cleaning flow chart.

Figure 2

Variable screening process of Lasso regression. (A) Lasso coefficient curves for candidate features; (B) The best parameter (lambda) selected by ten-fold cross-validation, where a perpendicular dotted-line is drawn at the best value, using the minimum standard and the constraints defined by 1 standard deviation.

Figure 3

XGBoost ROC curves generated from the training and test datasets.

Figure 4

(A) Calibration plot for the training dataset. (B) Calibration plot for the test dataset.

Figure 5

(A) DCA curves for the training dataset. (B) DCA curves for the test dataset.

Figure 6

SHapley Additive exPlanation (SHAP) values.

Figure 7

Bar chart of variable contributions based on absolute values of SHAP.

Figure 8

Individual prediction of anxiety symptoms in patients with abdominal obesity number 2.