■基于血管老化先验概率的易于访问且无成本的机器学习模型使应用程序能够在进行身体检查之前确定高风险人群并优化医疗保健投资。
■从湘雅三医院健康管理中心的电子记录中提取了一个包含来自77,134名成年人的问卷答复和身体测量参数的数据集。采用最小绝对收缩和选择运算符以及递归特征消除-轻量级梯度电梯从潜在协变量池中选择特征。参与者被随机分为训练组(70%)和测试组(30%)。四种机器学习算法被应用于建立动脉僵硬度(EAS)升高的筛查模型,并通过计算受试者工作特征曲线下面积(AUC)评估模型的性能,灵敏度,特异性,和准确性。
■选择了十四个易于访问的特征来构建模型,包括“收缩压”(SBP),“年龄,\"\"腰围,“\”高血压病史,\"\"性别,\"\"练习,对正常血压的认识,\"\"吃水果,“\”工作强度,“喝牛奶,\"\"吃豆制品,\"\"吸烟,“\”酒精消费,\"和\"易怒。“极端梯度提升(XGBoost)模型优于其他三个模型,在训练集和测试集中实现0.8722和0.8710的AUC值,分别。最重要的五个特征是SBP,年龄,腰部,高血压病史,和性爱。
■XGBoost模型理想地评估了当前EAS在一般人群中的先验概率。将该模型集成到初级保健设施中有可能降低医疗费用并增强动脉老化的管理。
An easily accessible and cost-free machine learning model based on prior probabilities of vascular aging enables an application to pinpoint high-risk populations before physical checks and optimize healthcare investment.
A dataset containing questionnaire responses and physical measurement parameters from 77,134 adults was extracted from the electronic records of the Health Management Center at the Third Xiangya Hospital. The least absolute shrinkage and selection operator and recursive
feature elimination-Lightweight Gradient Elevator were employed to select features from a pool of potential covariates. The participants were randomly divided into training (70%) and test cohorts (30%). Four machine learning algorithms were applied to build the screening models for elevated arterial stiffness (EAS), and the performance of models was evaluated by calculating the area under the receiver operating characteristic curve (AUC), sensitivity, specificity, and accuracy.
Fourteen easily accessible features were selected to construct the model, including \"systolic blood pressure\" (SBP), \"age,\" \"waist circumference,\" \"history of hypertension,\" \"sex,\" \"exercise,\" \"awareness of normal blood pressure,\" \"eat fruit,\" \"work intensity,\" \"drink milk,\" \"eat bean products,\" \"smoking,\" \"alcohol consumption,\" and \"Irritableness.\" The extreme gradient boosting (XGBoost) model outperformed the other three models, achieving AUC values of 0.8722 and 0.8710 in the training and test sets, respectively. The most important five features are SBP, age, waist, history of hypertension, and sex.
The XGBoost model ideally assesses the prior probability of the current EAS in the general population. The integration of the model into primary care facilities has the potential to lower medical expenses and enhance the management of arterial aging.