Departamento de Informática y Computación, Facultad de Administración, Grupo GAIA, Universidad Nacional de Colombia, Manizales, Colombia.
Research Group in Geriatrics and Gerontology, Faculty of Health Sciences, Universidad de Caldas, Manizales, Colombia.
Aging Clin Exp Res. 2022 Nov;34(11):2761-2768. doi: 10.1007/s40520-022-02227-4. Epub 2022 Sep 7.
Some studies have employed machine learning (ML) methods for mobility prediction modeling in older adults. ML methods could be a helpful tool for life-space mobility (LSM) data analysis.
This study aimed to evaluate the predictive value of ML algorithms for the restriction of life-space mobility (LSM) among elderly people and to identify the most important risk factors for that prediction model.
A 2-year LSM reduction prediction model was developed using the ML-based algorithms decision tree, random forest, and eXtreme gradient boosting (XGBoost), and tested on an independent validation cohort. The data were collected from the International Mobility in Aging Study (IMIAS) from 2012 to 2014, comprising 372 older patients (≥ 65 years of age). LSM was measured by the Life-Space Assessment questionnaire (LSA) with five levels of living space during the month before assessment.
According to the XGBoost algorithm, the best model reached a mean absolute error (MAE) of 10.28 and root-mean-square error (RMSE) of 12.91 in the testing portion. The variables frailty (39.4%), mobility disability (25.4%), depression (21.9%), and female sex (13.3%) had the highest importance.
The model identified risk factors through ML algorithms that could be used to predict LSM restriction; these risk factors could be used by practitioners to identify older adults with an increased risk of LSM reduction in the future. The XGBoost model offers benefits as a complementary method of traditional statistical approaches to understand the complexity of mobility.
一些研究采用机器学习 (ML) 方法对老年人的移动性进行预测建模。ML 方法可能是生活空间移动性 (LSM) 数据分析的有用工具。
本研究旨在评估 ML 算法对老年人生活空间移动性限制 (LSM) 的预测价值，并确定该预测模型的最重要风险因素。
使用基于 ML 的决策树、随机森林和极端梯度增强 (XGBoost) 算法开发了一个 2 年 LSM 减少预测模型，并在独立验证队列中进行了测试。数据来自 2012 年至 2014 年的国际老龄化移动性研究 (IMIAS)，包括 372 名老年人 (≥65 岁)。LSM 通过生活空间评估问卷 (LSA) 进行测量，该问卷在评估前一个月有五个生活空间级别。
根据 XGBoost 算法，最佳模型在测试部分的平均绝对误差 (MAE) 为 10.28，均方根误差 (RMSE) 为 12.91。脆弱性 (39.4%)、行动障碍 (25.4%)、抑郁 (21.9%) 和女性性别 (13.3%) 等变量具有最高的重要性。
该模型通过 ML 算法确定了可用于预测 LSM 限制的风险因素；这些风险因素可被从业者用于识别未来 LSM 减少风险增加的老年人。XGBoost 模型作为传统统计方法的补充方法，具有理解移动性复杂性的优势。
