An artificial neural network approach for predicting hypertension using NHANES data

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.

Machine Learning Algorithms to Predict Recurrence within 10 Years after Breast Cancer Surgery: A Prospective Cohort Study

“…Recent works have repeatedly demonstrated the superior performance of the ANN model compared to other forecasting models [ 25 , 26 , 27 ]. The advantages offered by the unique characteristics of the ANN model have been confirmed by statistical analyses.…”

A Multilayer Perceptron Neural Network Model to Classify Hypertension in Adolescents Using Anthropometric Measurements: A Cross-Sectional Study in Sarawak, Malaysia

“…Many researchers have implemented ANN models for hypertension prediction, and some of these recent researches are [19][20][21][22][23][24][25][26][27][28][29][30]. Among these, Bani-Salameh et al [26] developed a multilayer perceptron (MLP) neural network model with six inputs: age, weight, fat ratio, blood pressure, alcohol, and smoking; one hidden layer and one output layer of hypertension and nonhypertension classes were implemented to train and test a sample size of 760 patients.…”

Applications of artificial intelligence for hypertension management

“…Health data provide valuable information for AI applications in assisting health assessment and disease prognosis. Current evidence has revealed AI's efficacy on identification of hypertensive status, [12][13][14] prediction for incidence, [15][16][17] and outcomes from hypertension. 18,19 Masked hypertension, with a reported prevalence ranging from 9% to 30% 20,21 globally, persists as an obstacle for accurate hypertension diagnosis which mainly relies on elevated office blood pressure (BP) level.…”

“…Health data provide valuable information for AI applications in assisting health assessment and disease prognosis. Current evidence has revealed AI’s efficacy on identification of hypertensive status, 12‐14 prediction for incidence, 15‐17 and outcomes from hypertension 18,19 …”