Department of Computer Science, Oviedo University, C/ Federico Garca Lorca, 33007, Oviedo, Spain.
Sanitas, 8400 NW 33rd St, Doral, FL, 33122, USA.
Sci Rep. 2020 Jun 30;10(1):10620. doi: 10.1038/s41598-020-67640-z.
This paper focus on a neural network classification model to estimate the association among gender, race, BMI, age, smoking, kidney disease and diabetes in hypertensive patients. It also shows that artificial neural network techniques applied to large clinical data sets may provide a meaningful data-driven approach to categorize patients for population health management, and support in the control and detection of hypertensive patients, which is part of the critical factors for diseases of the heart. Data was obtained from the National Health and Nutrition Examination Survey from 2007 to 2016. This paper utilized an imbalanced data set of 24,434 with (69.71%) non-hypertensive patients, and (30.29%) hypertensive patients. The results indicate a sensitivity of 40%, a specificity of 87%, precision of 57.8% and a measured AUC of 0.77 (95% CI [75.01-79.01]). This paper showed results that are to some degree more effectively than a previous study performed by the authors using a statistical model with similar input features that presents a calculated AUC of 0.73. This classification model can be used as an inference agent to assist the professionals in diseases of the heart field, and can be implemented in applications to assist population health management programs in identifying patients with high risk of developing hypertension.
本文重点介绍了一种神经网络分类模型，用于估计高血压患者中的性别、种族、BMI、年龄、吸烟、肾脏疾病和糖尿病之间的关联。它还表明，将人工神经网络技术应用于大型临床数据集可能为人口健康管理提供有意义的数据驱动方法，并支持高血压患者的控制和检测，这是心脏病等疾病的关键因素之一。数据来自 2007 年至 2016 年的国家健康和营养检查调查。本文利用一个不平衡的数据集，其中 69.71%的患者是非高血压患者，30.29%的患者是高血压患者。结果表明，该模型的灵敏度为 40%，特异性为 87%，精度为 57.8%，测量 AUC 为 0.77（95%CI [75.01-79.01]）。与作者之前使用具有相似输入特征的统计模型进行的研究相比，本文的结果在某种程度上更为有效，该模型的计算 AUC 为 0.73。该分类模型可以用作推理代理，以协助心脏病领域的专业人员，并可在应用中用于帮助人口健康管理计划识别有发展为高血压风险的患者。
