Identifying Factors that Affect Diabetes through Regression Analysis

HASHEM, HUSSEIN ABDULRAHMNAN; Shareef, Azad Adil; Ajeel, Sherzad Mohammad

doi:10.33899/berj.2025.188736

	Identifying Factors that Affect Diabetes through Regression Analysis
College Of Basic Education Research Journal
Volume 21, Issue 2, June 2025, Pages 542-553 PDF (611.15 K)
Document Type: Research Paper
DOI: 10.33899/berj.2025.188736
Authors
HUSSEIN ABDULRAHMNAN HASHEM^* ¹; Azad Adil Shareef²; Sherzad Mohammad Ajeel¹
¹Dept. of Mathematics, College of Science, University of Duhok, Kurdistan Region, Iraq
²Dept. of Statistics, College of Administration and Economics, University of Duhok, Kurdistan Region, Iraq
Abstract
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 weight—four 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.
Keywords
Diabetes data; SCAD; LASSO

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