There are about 1 billion hypertensives patients on a global scale. Hypertension has become the main cause of shorter lifespan and disability for humans worldwide. In this essay, we constructed a new model based on hybrid feature selection and the standard XGBoost for hypertension detection and prediction. After having successfully utilized Lasso regression to identify hypertension-related factors, we used the standard XGBoost model for hypertension prediction. The result from the experiments conducted on the data from the BRFSS shows that proposed model can achieve 77.2% accuracy and 84.6% AUC, both about 7% higher than that without the nonoptimized model. Our proposed model can not only be used to predict the risk of hypertension, but also provide customers with suggestions on how to lead a healthy lifestyle.

中文翻译：

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使用特征选择和 XGBOOST 预测高血压风险

全球范围内约有 10 亿高血压患者。高血压已成为全世界人类寿命缩短和残疾的主要原因。在本文中，我们构建了一个基于混合特征选择和标准 XGBoost 的新模型，用于高血压检测和预测。在成功利用 Lasso 回归识别高血压相关因素后，我们使用标准 XGBoost 模型进行高血压预测。对来自 BRFSS 的数据进行的实验结果表明，所提出的模型可以达到 77.2% 的准确率和 84.6% 的 AUC，均比未优化模型的模型高约 7%。我们提出的模型不仅可以用来预测高血压的风险，还可以为客户提供有关如何过上健康生活方式的建议。