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http://dx.doi.org/10.3745/KTSDE.2021.10.11.541

Prediction Model of Hypertension Using Sociodemographic Characteristics Based on Machine Learning  

Lee, Bum Ju (한국한의학연구원 디지털임상연구부)
Publication Information
KIPS Transactions on Software and Data Engineering / v.10, no.11, 2021 , pp. 541-546 More about this Journal
Abstract
Recently, there is a trend of developing various identification and prediction models for hypertension using clinical information based on artificial intelligence and machine learning around the world. However, most previous studies on identification or prediction models of hypertension lack the consideration of the ideas of non-invasive and cost-effective variables, race, region, and countries. Therefore, the objective of this study is to present hypertension prediction model that is easily understood using only general and simple sociodemographic variables. Data used in this study was based on the Korea National Health and Nutrition Examination Survey (2018). In men, the model using the naive Bayes with the wrapper-based feature subset selection method showed the highest predictive performance (ROC = 0.790, kappa = 0.396). In women, the model using the naive Bayes with correlation-based feature subset selection method showed the strongest predictive performance (ROC = 0.850, kappa = 0.495). We found that the predictive performance of hypertension based on only sociodemographic variables was higher in women than in men. We think that our models based on machine leaning may be readily used in the field of public health and epidemiology in the future because of the use of simple sociodemographic characteristics.
Keywords
Machine Leaning; Prediction Model; Hypertension; Sociodemographic Characteristics; Public Health;
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