Chang Wenbing, Liu Yinglai, Xiao Yiyong, Yuan Xinglong, Xu Xingxing, Zhang Siyue, Zhou Shenghan
School of Reliability and Systems Engineering, Beihang University, Beijing 100191 China.
Diagnostics (Basel). 2019 Nov 7;9(4):178. doi: 10.3390/diagnostics9040178.
The outcomes of hypertension refer to the death or serious complications (such as myocardial infarction or stroke) that may occur in patients with hypertension. The outcomes of hypertension are very concerning for patients and doctors, and are ideally avoided. However, there is no satisfactory method for predicting the outcomes of hypertension. Therefore, this paper proposes a prediction method for outcomes based on physical examination indicators of hypertension patients. In this work, we divide the patients' outcome prediction into two steps. The first step is to extract the key features from the patients' many physical examination indicators. The second step is to use the key features extracted from the first step to predict the patients' outcomes. To this end, we propose a model combining recursive feature elimination with a cross-validation method and classification algorithm. In the first step, we use the recursive feature elimination algorithm to rank the importance of all features, and then extract the optimal features subset using cross-validation. In the second step, we use four classification algorithms (support vector machine (SVM), C4.5 decision tree, random forest (RF), and extreme gradient boosting (XGBoost)) to accurately predict patient outcomes by using their optimal features subset. The selected model prediction performance evaluation metrics are accuracy, F1 measure, and area under receiver operating characteristic curve. The 10-fold cross-validation shows that C4.5, RF, and XGBoost can achieve very good prediction results with a small number of features, and the classifier after recursive feature elimination with cross-validation feature selection has better prediction performance. Among the four classifiers, XGBoost has the best prediction performance, and its accuracy, F1, and area under receiver operating characteristic curve (AUC) values are 94.36%, 0.875, and 0.927, respectively, using the optimal features subset. This article's prediction of hypertension outcomes contributes to the in-depth study of hypertension complications and has strong practical significance.
高血压的结局是指高血压患者可能发生的死亡或严重并发症（如心肌梗死或中风）。高血压的结局是患者和医生非常关注的问题，最好能避免。然而，目前尚无令人满意的方法来预测高血压的结局。因此，本文提出了一种基于高血压患者体格检查指标的结局预测方法。在这项工作中，我们将患者的结局预测分为两个步骤。第一步是从患者众多的体格检查指标中提取关键特征。第二步是使用第一步中提取的关键特征来预测患者的结局。为此，我们提出了一种将递归特征消除与交叉验证方法及分类算法相结合的模型。在第一步中，我们使用递归特征消除算法对所有特征的重要性进行排序，然后使用交叉验证提取最优特征子集。在第二步中，我们使用四种分类算法（支持向量机（SVM）、C4.5决策树、随机森林（RF）和极端梯度提升（XGBoost）），通过使用其最优特征子集来准确预测患者的结局。所选模型预测性能评估指标为准确率、F1值和受试者工作特征曲线下面积。10折交叉验证表明，C4.5、RF和XGBoost在特征数量较少的情况下能取得很好的预测结果，经过交叉验证特征选择的递归特征消除后的分类器具有更好的预测性能。在这四种分类器中，XGBoost的预测性能最佳，使用最优特征子集时，其准确率、F1值和受试者工作特征曲线下面积（AUC）值分别为94.36%、0.875和0.927。本文对高血压结局的预测有助于深入研究高血压并发症，并具有很强的实际意义。
