Machine learning methods are widely used within the medical field to enhance prediction. However, little is known about the reliability and efficacy of these models to predict long-term medical outcomes such as blood pressure using lifestyle factors, such as diet. The authors assessed whether machine-learning techniques could accurately predict hypertension risk using nutritional information. A cross-sectional study using data from the National Health and Nutrition Examination Survey (NHANES) between January 2017 and March 2020. XGBoost was used as the machine-learning model of choice in this study due to its increased performance relative to other common methods within medical studies. Model prediction metrics (e.g., AUROC, Balanced Accuracy) were used to measure overall model efficacy, covariate Gain statistics (percentage each covariate contributes to the overall prediction) and SHapely Additive exPlanations (SHAP, method to visualize each covariate) were used to provide explanations to machine-learning output and increase the transparency of this otherwise cryptic method. Of a total of 9650 eligible patients, the mean age was 41.02 (SD = 22.16), 4792 (50%) males, 4858 (50%) female, 3407 (35%) White patients, 2567 (27%) Black patients, 2108 (22%) Hispanic patients, and 981 (10%) Asian patients. From evaluation of model gain statistics, age was found to be the single strongest predictor of hypertension, with a gain of 53.1%. Additionally, demographic factors such as poverty and Black race were also strong predictors of hypertension, with gain of 4.33% and 4.18%, respectively. Nutritional Covariates contributed 37% to the overall prediction: Sodium, Caffeine, Potassium, and Alcohol intake being significantly represented within the model. Machine Learning can be used to predict hypertension.

中文翻译：

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在预测高血压时，形状相加值可以有效地可视化机器学习中的相关协变量

机器学习方法广泛应用于医学领域以增强预测。然而，人们对这些模型利用饮食等生活方式因素预测血压等长期医疗结果的可靠性和有效性知之甚少。作者评估了机器学习技术是否可以利用营养信息准确预测高血压风险。一项横断面研究，使用 2017 年 1 月至 2020 年 3 月期间国家健康和营养检查调查 (NHANES) 的数据。XGBoost 被用作本研究中选择的机器学习模型，因为它相对于其他常见方法具有更高的性能医学研究。模型预测指标（例如，AUROC，平衡精度）用于测量总体模型功效，协变量增益统计（每个协变量对总体预测的贡献百分比）和 SHapely 加性解释（SHAP，可视化每个协变量的方法）用于提供解释机器学习输出并增加这种原本神秘的方法的透明度。在总共 9650 名符合条件的患者中，平均年龄为 41.02 岁（SD = 22.16），其中男性 4792 名（50%），女性 4858 名（50%），白人患者 3407 名（35%），黑人患者 2567 名（27%），黑人患者 2108 名。 (22%) 西班牙裔患者和 981 名 (10%) 亚洲患者。通过对模型增益统计数据的评估，发现年龄是高血压的单一最强预测因子，增益为 53.1%。此外，贫困和黑人种族等人口因素也是高血压的有力预测因素，分别增加了 4.33% 和 4.18%。营养协变量对总体预测贡献了 37%：钠、咖啡因、钾和酒精的摄入量在模型中得到了显着体现。机器学习可用于预测高血压。