PDF(Pubmed)
Abstract：
BACKGROUND: Chronic stress is highly prevalent in the German population. It has known adverse effects on mental health, such as burnout and depression. Known long-term effects of chronic stress are cardiovascular disease, diabetes, and cancer.
OBJECTIVE: This study aims to derive an interpretable multiclass machine learning model for predicting chronic stress levels and factors protecting against chronic stress based on representative nationwide data from the German Health Interview and Examination Survey for Adults, which is part of the national health monitoring program.
METHODS: A data set from the German Health Interview and Examination Survey for Adults study including demographic, clinical, and laboratory data from 5801 participants was analyzed. A multiclass eXtreme Gradient Boosting (XGBoost) model was constructed to classify participants into 3 categories including low, middle, and high chronic stress levels. The model\'s performance was evaluated using the area under the receiver operating characteristic curve, precision, recall, specificity, and the F1-score. Additionally, SHapley Additive exPlanations was used to interpret the prediction XGBoost model and to identify factors protecting against chronic stress.
RESULTS: The multiclass XGBoost model exhibited the macroaverage scores, with an area under the receiver operating characteristic curve of 81%, precision of 63%, recall of 52%, specificity of 78%, and F1-score of 54%. The most important features for low-level chronic stress were male gender, very good general health, high satisfaction with living space, and strong social support.
CONCLUSIONS: This study presents a multiclass interpretable prediction model for chronic stress in adults in Germany. The explainable artificial intelligence technique SHapley Additive exPlanations identified relevant protective factors for chronic stress, which need to be considered when developing interventions to reduce chronic stress.
OBJECTIVE: This study aims to derive an interpretable multiclass machine learning model for predicting chronic stress levels and factors protecting against chronic stress based on representative nationwide data from the German Health Interview and Examination Survey for Adults, which is part of the national health monitoring program.
METHODS: A data set from the German Health Interview and Examination Survey for Adults study including demographic, clinical, and laboratory data from 5801 participants was analyzed. A multiclass eXtreme Gradient Boosting (XGBoost) model was constructed to classify participants into 3 categories including low, middle, and high chronic stress levels. The model\'s performance was evaluated using the area under the receiver operating characteristic curve, precision, recall, specificity, and the F1-score. Additionally, SHapley Additive exPlanations was used to interpret the prediction XGBoost model and to identify factors protecting against chronic stress.
RESULTS: The multiclass XGBoost model exhibited the macroaverage scores, with an area under the receiver operating characteristic curve of 81%, precision of 63%, recall of 52%, specificity of 78%, and F1-score of 54%. The most important features for low-level chronic stress were male gender, very good general health, high satisfaction with living space, and strong social support.
CONCLUSIONS: This study presents a multiclass interpretable prediction model for chronic stress in adults in Germany. The explainable artificial intelligence technique SHapley Additive exPlanations identified relevant protective factors for chronic stress, which need to be considered when developing interventions to reduce chronic stress.
摘要：
背景：慢性应激在德国人群中非常普遍。已知它对心理健康有不良影响，如倦怠和抑郁。慢性压力的已知长期影响是心血管疾病，糖尿病,和癌症。
目的：本研究旨在基于德国成人健康访谈和检查调查的全国代表性数据，得出一个可解释的多类机器学习模型，用于预测慢性压力水平和预防慢性压力的因素。这是国家健康监测计划的一部分。
方法：来自德国成人健康访谈和检查调查研究的数据集，包括人口统计学，临床,分析了5801名参与者的实验室数据.构建了一个多类极限梯度提升（XGBoost）模型，将参与者分为3类，包括低，中间,和高慢性压力水平。使用接收器工作特性曲线下的面积评估模型的性能，精度,召回，特异性，和F1得分。此外,使用Shapley加法扩张来解释预测XGBoost模型并确定保护免受慢性压力的因素。
结果：多类XGBoost模型显示了宏观平均分数，接收器工作特性曲线下面积为81%，精度为63%，召回52%,特异性为78%，F1得分为54%。低水平慢性压力的最重要特征是男性，良好的整体健康，对生活空间的高度满意，强大的社会支持。
结论：本研究为德国成年人的慢性应激提供了一个多类可解释的预测模型。可解释的人工智能技术Shapley加法扩张确定了慢性压力的相关保护因素，在制定减少慢性压力的干预措施时需要考虑这一点。
目的：本研究旨在基于德国成人健康访谈和检查调查的全国代表性数据，得出一个可解释的多类机器学习模型，用于预测慢性压力水平和预防慢性压力的因素。这是国家健康监测计划的一部分。
方法：来自德国成人健康访谈和检查调查研究的数据集，包括人口统计学，临床,分析了5801名参与者的实验室数据.构建了一个多类极限梯度提升（XGBoost）模型，将参与者分为3类，包括低，中间,和高慢性压力水平。使用接收器工作特性曲线下的面积评估模型的性能，精度,召回，特异性，和F1得分。此外,使用Shapley加法扩张来解释预测XGBoost模型并确定保护免受慢性压力的因素。
结果：多类XGBoost模型显示了宏观平均分数，接收器工作特性曲线下面积为81%，精度为63%，召回52%,特异性为78%，F1得分为54%。低水平慢性压力的最重要特征是男性，良好的整体健康，对生活空间的高度满意，强大的社会支持。
结论：本研究为德国成年人的慢性应激提供了一个多类可解释的预测模型。可解释的人工智能技术Shapley加法扩张确定了慢性压力的相关保护因素，在制定减少慢性压力的干预措施时需要考虑这一点。
