A Proposed Method Based on Logistic Regression and Cluster Analysis in Selecting Influential Variables for Kidney Failure Patients

Section: Article
Issue
Vol. 22 No. 2 (2025): Volume 22 Issue 2
Published
Nov 30, 2025
Pages
87-95

Abstract

The research aims to study kidney failure by analyzing the relationship between it and a set of independent variables. To achieve this, a method was proposed that relies on reducing the number of independent variables used in binary logistic regression. The method relies on merging the independent variables with the dependent variable using cluster analysis, to improve the accuracy of the model and obtain the best possible results.The proposed method was applied to a sample of 142 individuals to study the relationship between the response variable (renal and non-renal failure) and independent variables such as gender, age, smoking, urea, creatine, and calcium. The results showed that the proposed method succeeded in reducing the number of independent variables and provided an ideal model that classifies the data with high accuracy. The resulting model focused on the two most influential variables, urea and creatine, and achieved a high classification rate of 94.4%.,The proposed method proved effective in reducing the number of variables and achieving accurate results in classifying data related to kidney failure.

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Authors

Suhaib Bashar Hameed

Department of Statistics and Informatics, College of Computer Science and Mathematics, University of Mosul, Mosul, Iraq

Mahmood M Taher

Department of Statistics and Informatics, College of Computer Science and Mathematics, University of Mosul, Mosul, Iraq

Identifiers

https://doi.org/10.33899/iqjoss.v22i2.54080
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How to Cite

Hameed, S. B. ., & Taher, M. M. . (2025). A Proposed Method Based on Logistic Regression and Cluster Analysis in Selecting Influential Variables for Kidney Failure Patients. IRAQI JOURNAL OF STATISTICAL SCIENCES, 22(2), 87–95. https://doi.org/10.33899/iqjoss.v22i2.54080