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. 2025 Jul 29;25(1):2577.
doi: 10.1186/s12889-025-23862-2.

COVID-19 risk stratification among older adults: a machine learning approach to identify personal and health-related risk factors

Arezoo Abasi  1 , Seyed Abbas Motevalian  2   3 , Haleh Ayatollahi  4
Affiliations

COVID-19 risk stratification among older adults: a machine learning approach to identify personal and health-related risk factors

Arezoo Abasi et al. BMC Public Health. .

Abstract

Background: The COVID-19 pandemic highlighted the need to understand factors influencing individuals' risk perceptions and health behaviors. This study aimed to explore the roles of individuals' knowledge, perception, and health-related issues in determining COVID-19 risk by developing a predictive model for classifying individuals into the risk categories, incorporating both clustering and model interpretation techniques.

Methods: To identify distinct COVID-19 risk groups, clustering analysis was applied using the demographic, health, and behavioral data. Subsequently, several machine learning models-including CatBoost, XGBoost, Random Forest, Generalized Linear Model (GLM), Decision Tree, H2O Deep Neural Network (DNN), and L2 SVM-were used to predict risk classifications. SHAP (SHapley Additive exPlanations) analysis was applied to interpret the contribution of individual features in model predictions.

Results: Three distinct risk classes were identified: Class 0 (high knowledge, low-risk factors, no household COVID-19 diagnosis), Class 1 (health-related issues (e.g., hypertension), low lnowldge), and Class 2 (high knowledge, higher health risks (e.g., hypertension, household COVID-19 diagnosis)). L2 SVM achieved the highest accuracy (0.9724), followed by XGBoost (0.9301) and CatBoost (0.9265). SHAP analysis revealed that household hygiene practices and health-related issues, such as hypertension and Gastrointestinal symptoms were key drivers of risk classification.

Conclusion: Integrating individuals' knowledge, perception, and health-related issues into COVID-19 risk assessments enhances predictive accuracy. Public health policies should focus on both physical and psychological factors to effectively mitigate the spread and impact of COVID-19. Data-driven models may inform future efforts to prioritize resource allocation and improve public health responses for vulnerable populations.

Keywords: COVID-19; Health behavior; Machine learning; Perception; Predictive learning models.

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Conflict of interest statement

Declarations. Ethics approval and consent to participate: All procedures were performed in accordance with the Declaration of Helsinki and have been approved by the ethics committee of Iran University of Medical Sciences (IR.IUMS.REC.1399.1310). Written informed consent was obtained from all participants before their involvement. Consent for publication: Not applicable. Competing interests: The authors declare no competing interests.

Figures

Fig. 1
Fig. 1
Visualizing pairwise relationships and dependencies between features
Fig. 2
Fig. 2
ROC curves for class 0-2 using splitted data
Fig. 3
Fig. 3
Model performance comparison using 10-fold CV
Fig. 4
Fig. 4
SHAP Plot for class 0
Fig. 5
Fig. 5
SHAP Plot for class 1
Fig. 6
Fig. 6
SHAP Plot for class 2
See this image and copyright information in PMC

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