Data Mining Techniques to Predict Chronic Kidney Diseases
DOI:
https://doi.org//10.32628/CSEIT217345Keywords:
Chronic Kidney Disease, Naïve Bayes, Pre-processingAbstract
Chronic Kidney Disease (CKD) is one of the most widespread illnesses nowadays in the world. Some statistics shows that 26 million adults in the United States have CKD and million others are at increasing risk. When condition of the kidney get worse, the wastes in the blood are formed in a high level. Data mining has been a present pattern for an accomplishing analytic outcomes. The Clinical diagnosis of CKD is based on blood and urine tests as well as removing a sample of kidney tissues for testing. By Some previous diagnosis and method of detection the kidney diseases are important to help stop the progression to kidney failure. Data mining and analytics techniques which can be used for predicting CKD by utilizing samples of patient’s data and diagnosis records done previously. The aim of my project is to anticipate CKD utilizing the classification strategy Naïve Bayes. Pre-processing the data is performed to impute any missing data and identified the variables that should be considered in the prediction models. Based on the accuracy of prediction the different predictive analytics models are assessed and compared. By presenting a decision support tool which will be used to help in the diagnosis of CKD.
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- International Journal of Scientific Research in Computer Science, Engineering and Information Technology © 2019 IJSRCSEIT | Volume 5 | Issue 2 | ISSN : 2456-3307 DOI : https://doi.org/10.32628/CSEIT1952331
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