Constructing a Model with Binary Response to Some of the Factors Affecting the Incidence of Chronic Kidney Failure

Abstract

Chronic kidney failure has become a disease of widespread diseases and increasingly in our society and must take precautions and prevention of causes and identify the most critical factors that lead to the disease. Since the binary logistic regression response (0,1) handles such as the model, the researcher took a sample consisting of 100 people between infected and non-infected chronic kidney. The researcher considered three factors that affect the disease: sex, age, and blood pressure. The model of logistic regression was constructed, and the parameter was estimated using the Maximum likelihood. The significance of the parameter was tested through the Wald test, and the full estimated model was tested with quality testing. The researchers recommend using logistics models for flexible use and application in the medical, economic, and social fields. Study of a larger number of variables affecting the disease and linear Multi-collinearly through the method of partial least square regression. Spreading awareness and culture among the community in identifying the causes of the disease and how to reduce treatment spread. We reached that the pressure is a factor, which is in line with the medical terms of interpretation that the blood pressure affecting the likelihood of developing high blood pressure leads significantly to chronic kidney failure, and the low blood pressure leads to the disease at a low rate. Besides, the sex variable also has a role in this model and the probability of injury for males more than females injured, but age did not appear significant to these parameters.