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In the age group ranging from 65 - 74 worldwide, it is estimated that one in five men, and one in four women, have Chronic Kidney Disorder (CKD). 10% of the population worldwide is affected by (CKD), and millions die each year due to lack of access to affordable treatment. A protein present in urine, persistent proteinuria is a key indicator for the presence of CKD. Early detection can help prevent progression of kidney disease to kidney failure. This detection and subsequent prevention can be achieved by applying Data Mining techniques on patient information to predict the occurrence of Chronic Kidney Disease. In this research paper, a Data Mining algorithm, Boruta analysis is performed to extrapolate the factors which can fortify the chances of a patient having CKD. This analysis covers statistic data along with historic and medical details. The dataset has been obtained from UCI source which contains data of 400 samples from the southern part of India with their ages ranging between 2-90 years. Making a decision concerning the seriousness of given factors, an estimate can be drawn with respect to the same. In Australia, treatment for all current and new cases of kidney failure through 2020 will cost an estimated 12 billion. Such an algorithm can help many individuals overall who may experience the ill effects of such affliction in their lifetime. Boruta Analysis, being freely available helps in medical diagnosis which can be otherwise expensive. It makes the diagnosis economical as well as faster for the patients.
Maithili Desai (Sun,) studied this question.