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http://dx.doi.org/10.22156/CS4SMB.2019.9.12.115

Convergence Analysis of Risk factors for Readmission in Cardiovascular Disease: A Machine Learning Approach  

Kim, Hyun-Su (Department of Nursing, KyungDong University)
Publication Information
Journal of Convergence for Information Technology / v.9, no.12, 2019 , pp. 115-123 More about this Journal
Abstract
This is descriptive study to 2nd analysis data KNHANES IV-VI about risk factors of readmission among patients with cardiovascular disease. Among the total 65,973 adults, 1,037 with angina or myocardial infarction were analyzed. The analysis was conducted using SPSS window 21 Program and CHAID decision tree was used in the classification analysis. Root nodes are economic activity(χ2=12.063, p=.001), children's nodes are personal income(χ2=6.575, p=.031), weight change(χ2=12.758, p=.001), residential area(χ2=4.025, p=.045), direct smoking(χ2=3.884, p=.031). p=.049), level of education(χ2=9.630, p=.024). Terminal nodes are hypertension(χ2=3.854, p=.050), diabetes mellitus(χ2=6.056, p=.014), occupation type(χ2=7.799, p=.037). We suggest that the development and operation of programs considering the integrated approach of various factors is necessary for the readmission management of cardiovascular patients.
Keywords
Decision Trees; Myocardial Infarction; Angina Pectoris; Patients Readmission; Risk Factors;
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