USING MULTINOMIAL LOGISTIC REGRESSION TO IDENTIFY FACTORS AFFECTING PLATELET - AJEEL, SHERZAD MOHAMMAD; HAJI, JIYAN ALI; JAHWAR, BANAZ HAMZA (Journal of Duhok University, 2023)
The objective of this work is to find an application for the Multinomial Logistic Regression (MLR) model, which is one of the essential methods for categorical data analysis. The focus of this paradigm is a single nominal or ordinal response variable with more than two categories. Data analysis using this method has been conducted in various disciplines, including health, social sciences, behavioral studies, and education. To practically identify the model’s application, we utilized real data from Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2) patients. Five explanatory variables were included in creating the main multinomial logistic regression model. A series of statistical tests were performed to confirm the model’s suitability for the data. Furthermore, the model was put to the test by randomly selecting two observations from the data to forecast their categorization based on the explanatory variable values used. Our conclusion is that the multinomial logistic regression model enables us to effectively characterize the link between the explanatory variable set and the response variable, identify the impact of each variable, and forecast the classification of any given case