Identifying Factors that Affect Diabetes through Regression Analysis

Globally, diabetes has been shown to be a significant cause of both death and morbidity, affecting a wide range of populations irrespective of age, gender, or region. The purpose of this communication is to draw attention to this urgent public health issue and to increase awareness of it among world leaders and policymakers. The current study looks into the relationship between diabetes and age, gender, height, and weightfour important characteristics. The Azadi Hospital in Dohuk provided the study's data. Finding out whether gender, age, weight, or height are related to the high prevalence of diabetes was the main goal of the study. To achieve this, the researchers utilized a variety of multiple regression analysis techniques, including Least Absolute Shrinkage and Selection Operator (LASSO), Ordinary Least Square (OLS), Minimax Concave Penalty (MCP) Regression, Quantile Regression (QR), and Smoothly Clipped Absolute Deviation (SCAD) penalty. Age, weight, and diabetes were found to be significantly correlated by the data analysis.