Abstract:
Chronic kidney disease is one of a major global public health issue, affecting over 10% of the
population worldwide. It is the leading cause of death in 2016 ranking 16th and is expected to
rise to 5th rank by 2040. Due to the significant morbidity, mortality and growing prevalence of
the disease, there is a need to identify high risk subjects to avoid a greater burden. Identification
of factors leading an individual to chronic kidney disease is also essential, as some risk factors
can be improved, can stop or slow down progression to chronic kidney disease, and enhance the
ability of health care providers to prevent kidney failure. Different risk factors have been
identified for different countries, and risk prediction models also have been developed depending
on risk factors worldwide for different countries to identify risk groups. This identified risk
factor has shown variations in different countries. In Ethiopia identification of risk factors in
general people is limited. Variety of the studies done in Ethiopia identified risk factors only for
on one or two disease affected population, even though the disease affects any person. In
addition, since risk factors vary in different countries due to life styles and other factors,
prediction models are needed to be developed specifically. In Ethiopia there is lack of
developing predictions systems which has considered Ethiopian people. Moreover, studies have
indicated there is high prevalence and low awareness of chronic kidney disease in Ethiopia.
In this research, a system that that can, estimate probability of having CKD , identify risk level
of CKD, and recommends management of CKD risk factors is developed. Additionally,
significant risk factors are also identified in the study. The study uses expert knowledge and
statistical analysis to identify risk factors, to develop risk prediction and management system.
From multivariable logistic regression analysis it‟s observed that male gender, overweight,
hypertension, diabetes, experiencing injury on kidney, smoking above four years and family
history of kidney disease were found to be significantly associated with chronic kidney disease.
The system has showed 63.3 %, 65.3 % and 77.5% accuracy at 14%, 24% and 34% cut off
percent respectively in estimating probability. This study will have significance in preventing
chronic kidney disease at early stage and creating awareness.
