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http://dx.doi.org/10.17703/JCCT.2022.8.4.339

A study of methodology for identification models of cardiovascular diseases based on data mining  

Lee, Bum Ju (Digital Health Research Division, Korea Institute of Oriental Medicine)
Publication Information
The Journal of the Convergence on Culture Technology / v.8, no.4, 2022 , pp. 339-345 More about this Journal
Abstract
Cardiovascular diseases is one of the leading causes of death in the world. The objectives of this study were to build various models using sociodemographic variables based on three variable selection methods and seven machine learning algorithms for the identification of hypertension and dyslipidemia and to evaluate predictive powers of the models. In experiments based on full variables and correlation-based feature subset selection methods, our results showed that performance of models using naive Bayes was better than those of models using other machine learning algorithms in both two diseases. In wrapper-based feature subset selection method, performance of models using logistic regression was higher than those of models using other algorithms. Our finding may provide basic data for public health and machine learning fields.
Keywords
Cardiovascular Diseases; Identification Model; Data Mining; Methodology; Machine Learning;
Citations & Related Records
Times Cited By KSCI : 3  (Citation Analysis)
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