Hypertension (HTN) is the most important controllable risk factor for non-communicable diseases that can have various causes, which vary in different subgroups. This secondary analysis was conducted using the data obtained through the recruitment phase of Ravansar non-communicable cohort study (RaNCD). The multivariable logistic regression was used to determine the risk factors of HTN, and a decision tree with the CART algorithm was used to determine the predictive power of these variables. Of the 10,046 individuals aged 35 to 65 participating in RaNCD, 1579 (15.72%) of the participants had HTN. Aging and diabetes were the most important risk factors of HTN. The sensitivity and specificity of the decision tree for the training and testing models were very similar, such that the sensitivity of training was 69.0% and testing 68.0%, and their specificity was 73.0% and 71.0%, respectively. Overall, the accuracy rate of the training and testing models was 70% and 68%, respectively. The variable that best discriminated people with HTN from non-HTN was diabetes. In people with diabetes, the incidence of HTN was 5 years higher than those without diabetes. Since the predictive power and effect of the risk factors of HTN vary from one group to another, the decision tree can be of great help in identifying people with HTN due to the latent nature of the disease.

高血压（HTN）是非传染性疾病最重要的可控危险因素，其病因多种多样，不同亚组的病因各不相同。这项二次分析是使用 Ravansar 非传染性队列研究 (RaNCD) 招募阶段获得的数据进行的。使用多变量逻辑回归来确定 HTN 的危险因素，并使用 CART 算法的决策树来确定这些变量的预测能力。在参与 RaNCD 的 10,046 名 35 岁至 65 岁个体中，1579 名（15.72%）参与者患有 HTN。衰老和糖尿病是高血压最重要的危险因素。决策树对训练和测试模型的敏感性和特异性非常相似，训练的敏感性为69.0%，测试的敏感性为68.0%，特异性分别为73.0%和71.0%。总体而言，训练和测试模型的准确率分别为 70% 和 68%。区分高血压患者和非高血压患者的最佳变量是糖尿病。在糖尿病患者中，高血压的发病率比非糖尿病患者高 5 年。由于高血压风险因素的预测能力和影响因群体而异，由于疾病的潜在性质，决策树对于识别患有高血压的人有很大帮助。