School of Psychology, Shenzhen University, Shenzhen 518060, China.
The Shenzhen Humanities & Social Sciences Key Research Bases of the Center for Mental Health, Shenzhen University, Shenzhen 518060, China.
Int J Environ Res Public Health. 2023 Jan 30;20(3):2445. doi: 10.3390/ijerph20032445.
Studies of life satisfaction in older adults have been conducted extensively through empirical research, questionnaires, and theoretical analysis, with the majority of these studies basing their analyses on simple linear relationships between variables. However, most real-life relationships are complex and cannot be approximated with simple correlations. Here, we first investigate predictors correlated with life satisfaction in older adults. Then, machine learning is used to generate several predictive models based on a large sample of older adults (age ≥ 50 years; = 34,630) from the RAND Health and Retirement Study. Results show that subjective social status, positive emotions, and negative emotions are the most critical predictors of life satisfaction. The Support Vector Regression (SVR) model exhibited the highest prediction accuracy for life satisfaction in older individuals among several models, including Multiple Linear Regression (MLR), Ridge Regression (RR), Least Absolute Shrinkage and Selection Operator Regression (LASSO), K Nearest Neighbors (KNN), and Decision Tree Regression (DT) models. Although the KNN and DT models exhibited better model fitting than MLR, RR, and LASSO, their performances were poor in terms of model validation and model generalization. These results indicate that machine learning is superior to simple correlations for understanding life satisfaction among older adults.
对老年人生活满意度的研究已经通过实证研究、问卷调查和理论分析广泛展开，这些研究大多数基于变量之间的简单线性关系进行分析。然而，大多数现实生活中的关系都是复杂的，不能用简单的相关性来近似。在这里，我们首先研究了与老年人生活满意度相关的预测因素。然后，我们使用机器学习方法从 RAND 健康与退休研究中生成了基于大量老年人（年龄≥50 岁；n=34630）样本的几个预测模型。结果表明，主观社会地位、积极情绪和消极情绪是预测老年人生活满意度的最关键因素。支持向量回归（SVR）模型在几个模型中对老年人生活满意度的预测精度最高，包括多元线性回归（MLR）、岭回归（RR）、最小绝对值收缩和选择算子回归（LASSO）、K 近邻（KNN）和决策树回归（DT）模型。虽然 KNN 和 DT 模型的模型拟合性能优于 MLR、RR 和 LASSO，但在模型验证和模型泛化方面表现不佳。这些结果表明，机器学习在理解老年人的生活满意度方面优于简单的相关性。
