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Early Prediction of Chronic Kidney Disease by using Machine Learning Techniques

Bidri Deepika*, Vasudeva Rao KR, Dharmaj N Rampure, Prajwal P and Devanand Gowda G

There are many people who are suffering from chronic kidney diseases worldwide. Due to the several risk factors like food, environment and living standards many people get diseases suddenly. Diagnosing of chronic kidney diseases is generally invasive, costly, time-consuming and often risky. That is why many patients reach late stages of it without treatment, especially in those countries where the resources are limited. Therefore, the early detection strategy of the disease remains important, particularly in developing countries, where the diseases are generally diagnosed in later stages. Finding a solution for the above-mentioned problems and riding out from disadvantages became a strong motive to conduct this study. Chronic Kidney Disease (CKD) is one of the types of kidney disease, which results in a gradual loss of kidney function. This phenomenon can be observed over a period of months or years due to several living conditions of patients. The goal is to build a real time application by using the machine learning techniques (Naive Bayes and KNN algorithms), to detect the CKD at an early stage.