背景:抑郁症在中年和老年吸烟者中非常普遍。因此,我们的目的是确定经常和不经常使用尼古丁的中老年人患抑郁症的风险,因为这对于支持他们的福祉是非常必要的。
方法:本研究共纳入10,821名参与者,这些数据来自中国健康与退休纵向研究第5波,2020(CHARLS-5)。采用了五种机器学习(ML)算法。一些指标被用来评估模型的性能,包括接受者工作特性曲线下面积(AUC),阳性预测值(PPV),特异性,准确性。
结果:10,821名参与者(6472名男性,4349名女性)的平均年龄为60.47±8.98,抑郁量表得分为8.90±6.53。对于经常使用尼古丁的中老年人,随机森林(RF)获得了最高的AUC值,PPV和特异性(分别为0.75、0.74和0.88)。对于另一组,支持向量机(SVM)表现出最高的PPV(0.74),和相对较高的准确性和特异性(分别为0.72和0.87)。特征重要性分析表明,“对生活的不满”是SVM模型中识别抑郁风险的最重要变量。而“对预期寿命的态度”是射频模型中最重要的一个。
结论:CHARLS-5是在COVID-19期间收集的,因此我们的结果可能受到大流行的影响。
结论:这项研究表明,某些ML模型可以理想地识别中老年人患抑郁症的风险,这对他们的健康管理具有重要价值。
BACKGROUND: Depression is very prevalent in middle-aged and older smokers. Therefore, we aimed to identify the risk of depression among middle-aged and older adults with frequent and infrequent nicotine use, as this is quite necessary for supporting their well-being.
METHODS: This study included a total of 10,821 participants, which were derived from the China Health and Retirement Longitudinal Study Wave 5, 2020 (CHARLS-5). Five machine learning (ML) algorithms were employed. Some metrics were used to evaluate the performance of models, including area under the receiver operating characteristic curve (AUC), positive predictive value (PPV), specificity, accuracy.
RESULTS: 10,821 participants (6472 males, 4349 females) had a mean age of 60.47 ± 8.98, with a score of 8.90 ± 6.53 on depression scale. For middle-aged and older adults with frequent nicotine use, random forest (RF) achieved the highest AUC value, PPV and specificity (0.75, 0.74 and 0.88, respectively). For the other group, support vector machines (SVM) showed the highest PPV (0.74), and relatively high accuracy and specificity (0.72 and 0.87, respectively). Feature importance analysis indicated that \"dissatisfaction with life\" was the most important variable of identifying the risk of depression in the SVM model, while \"attitude towards expected life span\" was the most important one in the RF model.
CONCLUSIONS: CHARLS-5 was collected during the COVID-19, so our results may be influenced by the pandemic.
CONCLUSIONS: This study indicated that certain ML models can ideally identify the risk of depression in middle-aged and older adults, which holds significant value for their health management.