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Geriatric nursing (New York, N.Y.) · 2025

Interpretable machine learning for identifying overweight and obesity risk factors of older adults in China.

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Peng Bozhezi, Wu Jiani, Liu Xiaofei, Yin Pei, Wang Tao, Li Chaoyang, Yuan Shengqiang, Zhang Yi

Аннотация

OBJECTIVE: To estimate the importance of risk factors on overweight/obesity among older adults by comparing different predictive model.

METHODS: Survey data from 400 older individuals in China was employed to assess the impacts of four domains of risk factors (demographic, health status, physical activity and neighborhood environment) on overweight/obesity. Six machine learning algorithms were utilized for prediction, and SHapley Additive exPlanations (SHAP) was employed for model interpretation.

RESULTS: The CatBoost model demonstrated the highest performance among the prediction models for overweight/obesity. Gender, transportation-related physical activity and road network density were top three important features. Other significant factors included falls, cardiovascular conditions, distance to the nearest bus stop and land use mixture.

CONCLUSION: Insufficient physical activity, denser road network and incidents of falls increased the likelihood of older adults being overweight/obese. Strategies for preventing overweight/obesity should target transportation-related physical activity, neighborhood environments, and fall prevention specifically.

Предметные рубрики MeSH

ИП Урсолов Владлен Сергеевич

ИНН 500118482352 · ОГРНИП 314500117700049

Реквизиты и контакты