• KIPS Transactions on Software and Data Engineering
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• v.11 no.3
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• pp.107-114
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• 2022

In this paper, we have enhanced the risk prediction of hypertension using the feature selection method in the Korean National Health and Nutrition Examination Survey (KNHANES) database of the Korea Centers for Disease Control and Prevention. The study identified various risk factors correlated with chronic hypertension. The paper is divided into three parts. Initially, the data preprocessing step of removes missing values, and performed z-transformation. The following is the feature selection (FS) step that used a factor analysis (FA) based on the feature selection method in the dataset, and feature importance (FI) and multicollinearity analysis (MC) were compared based on FS. Finally, in the predictive analysis stage, it was applied to detect and predict the risk of hypertension. In this study, we compare the accuracy, f-score, area under the ROC curve (AUC), and mean standard error (MSE) for each model of classification. As a result of the test, the proposed MC-FA-RF model achieved the highest accuracy of 80.12%, MSE of 0.106, f-score of 83.49%, and AUC of 85.96%, respectively. These results demonstrate that the proposed MC-FA-RF method for hypertension risk predictions is outperformed other methods.

• Journal of Korean Academy of Nursing
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• v.21 no.2
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• pp.195-203
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• 1991

Correlations among body weight and sociodemographic factors, including life - style were tested as social determinants of health in a sample of 5,201 adults in Korea. The aim of this study was to determine the extent to which sociodemographic variables and life-style associated health behaviors explain body weight distribution. A second aim was to explain the relation of body weight and health status to stress the importance of body weight as an early risk indicator of health status. The canonical correlation between the weight distribution(underweight and overweight) and the independent variables was 0.29, 17% of the total variance was explained. Perceived health level represented the highest contribution(canonical coefficient 0.82) to body weight. Sociodemographic factors such as sex, economic status, and life-style factors such as smoking, exercise, regular meais and sleep showed comparatively high contributions to body weight. The relevance of body weight for health status including the rate of chronic disease and the rate of medical utilization was significant. Especially, underweight was clarified as being mere important than overweight to morbidity level and medical utilization. These findings suggest that perceived body weight is an important indicator of health status and is thus a valuable variable to be considered for nursing intervention and health education related to the promotion of health.