Articles

Analyzing Cardiovascular Disease Risk Factors Using Generalized Logistic Logic Regression: A Retrospective Study

Analyzing cardiovascular disease risk factors

Abstract

Introduction: Cardiovascular disease (CVD) is a general term that refers to diseases of the heart or blood vessels. Logic
regression is a machine learning method that is commonly used when the number of predictor variables is high, and it can
account for interaction effects between predictor variables. As CVD can be influenced by multiple factors, this study was
conducted to identify variables related to CVD and predict the occurrence of CVD using generalized logistic logic regression.
Methods: The present study is a retrospective study utilizing data from phase one of the MASHAD study. The analysis was
performed on the information of 7,385 individuals. Generalized logistic logic regression analysis was performed using the
“LogicReg” package in R software.
Results: Out of the 7385 individuals included in this study, 235 (3.2%) were diagnosed with CVD, while 7150 (96.8%) did
not have CVD. Of the variables examined, age, anxiety, depression, metabolic syndrome, and family history were significant
as main effects, and an interaction between smoking status and education had a significant effect.
Conclusion: Based on the findings of this study, it can be tentatively concluded that for CVD, the existence of interaction
effects among the mentioned risk factors may not be a significant concern. In other words, the primary effects of each
variable may be more important, as these variables appear to play a role in CVD independently of each other.

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IssueVol 11 No 3 (2025): . QRcode
SectionArticles
Keywords
generalized logistic logic regression cardiovascular disease machine learning interaction effects risk factors
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How to Cite
1.
kianfard mohammadAli, Esmaily H, Jahani M, Ghayour-Mobarhan M. Analyzing Cardiovascular Disease Risk Factors Using Generalized Logistic Logic Regression: A Retrospective Study. JBE. 2026;11(3):335-345.