Key result
An artificial neural network model using non-invasive risk factors predicted hypertension with an AUC of 0.77, demonstrating better predictive ability than a traditional logistic regression model.
Why the study?
Applying artificial neural network techniques to clinical datasets could provide a data-driven approach to categorize patients for population health management and assist in the detection and control of hypertension.
Does an artificial neural network model accurately predict hypertension using demographic and clinical features in a large population dataset?
Cross-Sectional (n=24,434)
Does an artificial neural network model accurately predict hypertension using demographic and clinical features in a large population dataset?
Effect estimate: AUC 0.77 (95% CI 0.75-0.79)
Absolute Event Rate: 0.77% vs 0.73%
An artificial neural network model using basic demographic and clinical features can predict hypertension with moderate accuracy (AUC 0.77), outperforming a previous statistical model.
Neural network models may aid hypertension categorization; hypothesis-generating pending prospective validation.
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.
No takes yet. Share an insight, caveat, or question.
Martínez et al. (2020) conducted a cross-sectional in Hypertension (n=24,434). Artificial neural network model vs. Logistic regression model was evaluated on Area under the ROC curve (AUC) for predicting hypertension (AUC 0.77, 95% CI 0.75-0.79). An artificial neural network model using non-invasive risk factors predicted hypertension with an AUC of 0.77, demonstrating better predictive ability than a traditional logistic regression model.
Synapse has enriched 3 closely related papers on similar clinical questions. Consider them for comparative context: