Turnbull Niruwan, Nghiep Le Ke, Butsorn Aree, Khotprom Anuwat, Tudpor Kukiat
Faculty of Public Health, Mahasarakham University, Maha Sarakham, Thailand.
Public Health and Environmental Policy in Southeast Asia Research Cluster (PHEP-SEA), Mahasarakham University, Maha Sarakham, Thailand.
Front Nutr. 2024 Jul 16;11:1411363. doi: 10.3389/fnut.2024.1411363. eCollection 2024.
To develop a predictive model for undiagnosed hypertension (UHTN) in older adults based on five modifiable factors [eating behaviors, emotion, exercise, stopping smoking, and stopping drinking alcohol (3E2S) using machine learning (ML) algorithms.
The supervised ML models [random forest (RF), support vector machine (SVM), and extreme gradient boosting (XGB)] with SHapley Additive exPlanations (SHAP) prioritization and conventional statistics (χ and binary logistic regression) were employed to predict UHTN from 5,288 health records of older adults from ten primary care hospitals in Thailand.
The χ analyses showed that age and eating behavior were the predicting features of UHTN occurrence. The binary logistic regression revealed that taking food supplements/vitamins, using seasoning powder, and eating bean products were related to normotensive and hypertensive classifications. The RF, XGB, and SVM accuracy were 0.90, 0.89, and 0.57, respectively. The SHAP identified the importance of salt intake and food/vitamin supplements. Vitamin B6, B12, and selenium in the UHTN were lower than in the normotensive group.
ML indicates that salt intake, soybean consumption, and food/vitamin supplements are primary factors for UHTN classification in older adults.
基于五个可改变因素（饮食行为、情绪、运动、戒烟和戒酒，即3E2S），使用机器学习（ML）算法建立老年人未诊断高血压（UHTN）的预测模型。
采用具有SHapley加性解释（SHAP）优先级的监督ML模型[随机森林（RF）、支持向量机（SVM）和极端梯度提升（XGB）]以及传统统计方法（χ检验和二元逻辑回归），从泰国十家初级保健医院的5288份老年人健康记录中预测UHTN。
χ检验分析表明，年龄和饮食行为是UHTN发生的预测特征。二元逻辑回归显示，服用食品补充剂/维生素、使用调味粉和食用豆制品与血压正常和高血压分类有关。RF、XGB和SVM的准确率分别为0.90、0.89和0.57。SHAP确定了盐摄入量和食品/维生素补充剂的重要性。UHTN组中维生素B6、B12和硒的含量低于血压正常组。
ML表明，盐摄入量、大豆消费量以及食品/维生素补充剂是老年人UHTN分类的主要因素。
