Zhong Xia, Zhao Tianen, Lv Shimeng, Zhang Guangheng, Li Jing, Liu Donghai, Jiao Huachen
Institute of Child and Adolescent Health, School of Public Health, Peking University, Beijing, China.
Purchasing Department, Jinan Lixia Dezhengtang Hospital of Traditional Chinese Medicine, Jinan, Shandong, China.
Front Cardiovasc Med. 2025 Aug 29;12:1477185. doi: 10.3389/fcvm.2025.1477185. eCollection 2025.
Risk-based predictive models are a reliable tool for early identification of hypertensive cognitive impairment. However, the evidence of the combination of individual factors and natural environmental factors is still insufficient. The aim of this study was to establish a well-performing machine learning (ML) model based on personal and natural environmental factors to help assess the risk of early cognitive impairment in hypertension.
In this study, a total of 757 Chinese hypertensive patients from from different regions of Shandong Province, China (aged 31-95, male 49.01%) were randomly divided into training group (70%) and verification group (30%). Modelling variables were determined by a 5-fold cross-validated least absolute shrinkage and selection operator (LASSO) regression analysis. Five ML classifiers, XGB (extreme gradient boosting), LR (logistic regression), AdaBoost (adaptive boosting), GNB (gaussian naive bayes), and SVM (support vector machines), have been developed. Area under the ROC curve (AUC), accuracy, sensitivity, specificity, and F1 scores were used to access the model performance. Shape Additive explanation (SHAP) models reveal the feature importance. The clinical performance of the model was evaluated by Decision Curve Analysis (DCA).
Cognitive impairment was diagnosed in 17.44% ( = 132). LASSO regression analyses suggested that age, waist circumference, urban green coverage, educational levels, annual sunshine hours, and area whole-day average noise were considered significant predictors of early cognitive impairment in hypertension. The obtained XGBoost model yielded good predictive performance with the AUC (0.893), F1 score (0.627), accuracy (0.837), sensitivity (0.780), and specificity (0.853). The predictive model's clinical net benefit was confirmed through DCA analysis.
The XGBoost model developed based on personal factors and natural environmental factors can predict early cognitive impairment of hypertension with superior predictive performance. Larger population cohorts are needed in the future to validate these findings and potentially enhance the ability to identify the occurrence of early cognitive impairment in people with hypertension.
基于风险的预测模型是早期识别高血压认知功能障碍的可靠工具。然而，个体因素与自然环境因素相结合的证据仍不充分。本研究旨在建立一个基于个人和自然环境因素的性能良好的机器学习（ML）模型，以帮助评估高血压患者早期认知功能障碍的风险。
在本研究中，来自中国山东省不同地区的757例中国高血压患者（年龄31 - 95岁，男性占49.01%）被随机分为训练组（70%）和验证组（30%）。通过5折交叉验证的最小绝对收缩和选择算子（LASSO）回归分析确定建模变量。开发了5种机器学习分类器，即XGB（极端梯度提升）、LR（逻辑回归）、AdaBoost（自适应提升）、GNB（高斯朴素贝叶斯）和SVM（支持向量机）。采用ROC曲线下面积（AUC）、准确率、敏感性、特异性和F1分数来评估模型性能。形状加性解释（SHAP）模型揭示特征重要性。通过决策曲线分析（DCA）评估模型的临床性能。
17.44%（n = 132）的患者被诊断为认知功能障碍。LASSO回归分析表明，年龄、腰围、城市绿化覆盖率、教育水平、年日照时数和区域全天平均噪声被认为是高血压患者早期认知功能障碍的重要预测因素。所获得XGBoost模型具有良好的预测性能，AUC为0.893，F1分数为0.627，准确率为0.837，敏感性为0.780，特异性为0.853。通过DCA分析证实了预测模型的临床净效益。
基于个人因素和自然环境因素开发的XGBoost模型能够以卓越的预测性能预测高血压患者的早期认知功能障碍。未来需要更大的人群队列来验证这些发现，并可能提高识别高血压患者早期认知功能障碍发生的能力。
