PDF(Pubmed)
Abstract：
UNASSIGNED: Hypertension is a major public health problem, and its resulting other cardiovascular diseases are the leading cause of death worldwide. In this study, we constructed a convenient and high-performance hypertension risk prediction model to assist in clinical diagnosis and explore other important influencing factors.
UNASSIGNED: We included 8,073 people from NHANES (2017-March 2020), using their 120 features to form the original dataset. After data pre-processing, we removed several redundant features through LASSO regression and correlation analysis. Thirteen commonly used machine learning methods were used to construct prediction models, and then, the methods with better performance were coupled with recursive feature elimination to determine the optimal feature subset. After data balancing through SMOTE, we integrated these better-performing learners to construct a fusion model based for predicting hypertension risk on stacking strategy. In addition, to explore the relationship between serum ferritin and the risk of hypertension, we performed a univariate analysis and divided it into four level groups (Q1 to Q4) by quartiles, with the lowest level group (Q1) as the reference, and performed multiple logistic regression analysis and trend analysis.
UNASSIGNED: The optimal feature subsets were: age, BMI, waist, SBP, DBP, Cre, UACR, serum ferritin, HbA1C, and doctors recommend reducing salt intake. Compared to other machine learning models, the constructed fusion model showed better predictive performance with precision, accuracy, recall, F1 value and AUC of 0.871, 0.873, 0.871, 0.869 and 0.966, respectively. For the analysis of the relationship between serum ferritin and hypertension, after controlling for all co-variates, OR and 95% CI from Q2 to Q4, compared to Q1, were 1.396 (1.176-1.658), 1.499 (1.254-1.791), and 1.645 (1.360-1.989), respectively, with P < 0.01 and P for trend <0.001.
UNASSIGNED: The hypertension risk prediction model developed in this study is efficient in predicting hypertension with only 10 low-cost and easily accessible features, which is cost-effective in assisting clinical diagnosis. We also found a trend correlation between serum ferritin levels and the risk of hypertension.
UNASSIGNED: We included 8,073 people from NHANES (2017-March 2020), using their 120 features to form the original dataset. After data pre-processing, we removed several redundant features through LASSO regression and correlation analysis. Thirteen commonly used machine learning methods were used to construct prediction models, and then, the methods with better performance were coupled with recursive feature elimination to determine the optimal feature subset. After data balancing through SMOTE, we integrated these better-performing learners to construct a fusion model based for predicting hypertension risk on stacking strategy. In addition, to explore the relationship between serum ferritin and the risk of hypertension, we performed a univariate analysis and divided it into four level groups (Q1 to Q4) by quartiles, with the lowest level group (Q1) as the reference, and performed multiple logistic regression analysis and trend analysis.
UNASSIGNED: The optimal feature subsets were: age, BMI, waist, SBP, DBP, Cre, UACR, serum ferritin, HbA1C, and doctors recommend reducing salt intake. Compared to other machine learning models, the constructed fusion model showed better predictive performance with precision, accuracy, recall, F1 value and AUC of 0.871, 0.873, 0.871, 0.869 and 0.966, respectively. For the analysis of the relationship between serum ferritin and hypertension, after controlling for all co-variates, OR and 95% CI from Q2 to Q4, compared to Q1, were 1.396 (1.176-1.658), 1.499 (1.254-1.791), and 1.645 (1.360-1.989), respectively, with P < 0.01 and P for trend <0.001.
UNASSIGNED: The hypertension risk prediction model developed in this study is efficient in predicting hypertension with only 10 low-cost and easily accessible features, which is cost-effective in assisting clinical diagnosis. We also found a trend correlation between serum ferritin levels and the risk of hypertension.
摘要：
■高血压是一个主要的公共卫生问题，及其导致的其他心血管疾病是全球死亡的主要原因。在这项研究中,我们构建了一个方便、高效的高血压风险预测模型来辅助临床诊断和探索其他重要影响因素。
■我们包括来自NHANES的8073人（2017年至2020年3月），使用它们的120个特征来形成原始数据集。数据预处理后,我们通过LASSO回归和相关分析去除了几个冗余特征.13种常用的机器学习方法被用来构建预测模型，然后,性能较好的方法与递归特征消除相结合，以确定最佳特征子集。通过SMOTE实现数据平衡后，我们整合了这些表现较好的学习者,构建了一个基于堆叠策略预测高血压风险的融合模型.此外,探讨血清铁蛋白与高血压发病风险的关系,我们进行了单变量分析，并按四分位数将其分为四个水平组（Q1至Q4），以最低级别组(Q1)为参考，进行多元logistic回归分析和趋势分析。
■最佳特征子集为：年龄，BMI,腰部,SBP,DBP,Cre,UACR,血清铁蛋白,HbA1C,医生建议减少盐的摄入量。与其他机器学习模型相比，所构建的融合模型显示出更好的预测性能和精度，准确度,召回，F1值和AUC分别为0.871、0.873、0.871、0.869和0.966。为分析血清铁蛋白与高血压的关系,在控制所有协变量后，与第一季度相比，第二季度至第四季度的OR和95%CI为1.396（1.176-1.658），1.499(1.254-1.791)，和1.645（1.360-1.989），分别,P<0.01，趋势P<0.001。
■本研究开发的高血压风险预测模型仅具有10种低成本且易于获取的特征，就可以有效预测高血压，这在辅助临床诊断方面具有成本效益。我们还发现血清铁蛋白水平与高血压风险之间存在趋势相关性。
■我们包括来自NHANES的8073人（2017年至2020年3月），使用它们的120个特征来形成原始数据集。数据预处理后,我们通过LASSO回归和相关分析去除了几个冗余特征.13种常用的机器学习方法被用来构建预测模型，然后,性能较好的方法与递归特征消除相结合，以确定最佳特征子集。通过SMOTE实现数据平衡后，我们整合了这些表现较好的学习者,构建了一个基于堆叠策略预测高血压风险的融合模型.此外,探讨血清铁蛋白与高血压发病风险的关系,我们进行了单变量分析，并按四分位数将其分为四个水平组（Q1至Q4），以最低级别组(Q1)为参考，进行多元logistic回归分析和趋势分析。
■最佳特征子集为：年龄，BMI,腰部,SBP,DBP,Cre,UACR,血清铁蛋白,HbA1C,医生建议减少盐的摄入量。与其他机器学习模型相比，所构建的融合模型显示出更好的预测性能和精度，准确度,召回，F1值和AUC分别为0.871、0.873、0.871、0.869和0.966。为分析血清铁蛋白与高血压的关系,在控制所有协变量后，与第一季度相比，第二季度至第四季度的OR和95%CI为1.396（1.176-1.658），1.499(1.254-1.791)，和1.645（1.360-1.989），分别,P<0.01，趋势P<0.001。
■本研究开发的高血压风险预测模型仅具有10种低成本且易于获取的特征，就可以有效预测高血压，这在辅助临床诊断方面具有成本效益。我们还发现血清铁蛋白水平与高血压风险之间存在趋势相关性。
