Abstract
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.
Highlights
This paper focus on a neural network classification model to estimate the association among gender, race, body mass index (BMI), age, smoking, kidney disease and diabetes in hypertensive patients
The motivation to develop a new model was the non-linearity of the input features, and neural networks are usually trained to treat non-linearity due to the non-linear nature of t hem[13], making the model more flexible compared to logistic regression
The paper has shown that the classification capability of the model improved (AUC—0.77), based on the results of the statistical model utilized in a previous paper (AUC—0.73) when applied to the input features gender, race, BMI, age, smoking, kidney conditions and diabetes
Summary
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. 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. We develop a neural network classification model to predict hypertension with non-invasive risk factors applying healthcare data from the NHANES. Gender, BMI, sys/diast BP, high and low density lipoproteins, triglycerides, cholesterol, microalbumin, and urine albumin creatinine ratio
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