Identifying Foremost Factors Relevant to Blood Pressure Level Using Logistic Regression Analysis: A Case Study (Desert Dwellers Data)

Abstract

The current research investigates the use of logistic regression as a statistical technique for modelling real blood pressure (BP) data. This study uses a dataset collected from a desert community in southwestern Libya. Six factors that are widely believed to play an important role in the process of BP were considered. Statistical analyses of the available dataset revealed that the main cause of hypertension in such community is age. The proposed multiple logistic regression analysis also revealed that two factors, age and systolic BP, showed greater significance among the six examined variables. These two variables were identified as having a significant effect on blood pressure performance. Based on a determined criterion, each page as the main cause of hypertension in such community participants was classified as hypertensive or not, significant variables were selected based on the p-value associated with the model significance level, and these factors were selected based on the criteria to achieve the model significance level (p < 0.05). The statistical analysis was carried out using R language.