UTILIZING MULTINOMIAL LOGISTIC REGRESSION FOR DETERMINING THE FACTORS INFLUENCING BLOOD PRESSURE

Abstract

The aim of this study is to investigate the practical application of the Multinomial (many explanatory variables and many categories) Logistic Regression (MLR) model, which is a fundamental tool for analyzing not only for scale data but also for categorical data with many explanatory variables. This method is primarily used when there is a single nominal or ordinal response variable with multiple categories or levels. MLR analysis has various applications across disciplines such as education, social sciences, healthcare, behavioural research, and some other fields. We utilized real data from the Azadi Heart Center at the Duhok Hospital in the Duhok Governorate to assess the practical applicability of the model. The main multinomial logistic regression model was used with five explanatory variables. Extensive statistical tests were performed to confirm the suitability of this model for the dataset. Furthermore, the model underwent a validation process wherein two observations were randomly selected from the dataset, and their categorization was predicted based on the values of the explanatory variables utilized. Our results suggest that the multinomial logistic regression model provides a useful method for distinguishing between the response variable and the set of explanatory factors that makes it easier to determine the exact influence of each variable and enables predictions about how a particular instance will be classified.

Keywords

• logistic regression
• Binary variable Odds ratio
• maximum likelihood method
• categorical data analysis