Association of Premature Ventricular Contraction (PVC) with hematological parameters: a data mining approach

Abstract

Abstract Premature ventricular contraction (PVC) is characterized by early repolarization of the myocardium originating from Purkinje fibers. PVC may occur in individuals who are otherwise healthy. However, it may be associated with some pathological conditions. In this research the association between hematological factors and PVC was studied. In this study, 9,035 participants were enrolled in the Mashhad stroke and heart atherosclerotic disorder (MASHAD) cohort study. The association of hematological factors with PVC was evaluated using different machine learning (ML) algorithms, including logistic regression (LR), C5.0, and boosting decision tree (DT). The dataset was divided into training and test, and each model’s performance was appraised on the test dataset. All data analyses used SPSS version 26 and SPSS Modeler 10. The results show that the Boosting DT was the most effective algorithm. Boosting DT had an accuracy of 98.13% and 96.92% for males and females respectively. According to the models, RDW and PLT were the most significant hematological factors for both males and females. WBC, PDW, and HCT for males and RBC, MCV, and MXD for females were also important. Some hematological factors associated with PVC were found using ML models. Further studies are needed to confirm these results in other populations, considering the novelty of the exploration of the relationship between hematological parameters and PVC.

Keywords

• Premature ventricular contraction
• PVC
• Complete blood count
• CBC
• Data Mining
• Hematological parameters