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http://dx.doi.org/10.9728/dcs.2018.19.4.685

Statistical Analysis for Risk Factors and Prediction of Hypertension based on Health Behavior Information  

Heo, Byeong Mun (Database/Bioinformatics Lab, School of Electrical & Computer Engineering, Chungbuk National University)
Kim, Sang Yeob (Database/Bioinformatics Lab, School of Electrical & Computer Engineering, Chungbuk National University)
Ryu, Keun Ho (Database/Bioinformatics Lab, School of Electrical & Computer Engineering, Chungbuk National University)
Publication Information
Journal of Digital Contents Society / v.19, no.4, 2018 , pp. 685-692 More about this Journal
Abstract
The purpose of this study is to develop a prediction model of hypertension in middle-aged adults using Statistical analysis. Statistical analysis and prediction models were developed using the National Health and Nutrition Survey (2013-2016).Binary logistic regression analysis showed statistically significant risk factors for hypertension, and a predictive model was developed using logistic regression and the Naive Bayes algorithm using Wrapper approach technique. In the statistical analysis, WHtR(p<0.0001, OR = 2.0242) in men and AGE (p<0.0001, OR = 3.9185) in women were the most related factors to hypertension. In the performance evaluation of the prediction model, the logistic regression model showed the best predictive power in men (AUC = 0.782) and women (AUC = 0.858). Our findings provide important information for developing large-scale screening tools for hypertension and can be used as the basis for hypertension research.
Keywords
Hypertension; Prediction model; Machine Learning; Clinical Information; Anthropometry;
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