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Chronic Kidney Disease Prediction Using Data Mining Algorithms
Abstract
In today's contemporary world, it is important to know about the odds of having a disease because of changing living standards of the population overall in the continent. The disease on which the authors are working is chronic kidney disease. Once the person gets chronic kidney disease (CKD), his working capability decreases along with other adverse effects. It is possible to get rid of diseases like CKD with new methodologies that will help us to predict the stage of kidney disease at an early stage. Under big data analytics, data may be structured, unstructured, quasi- or semi-structured. The CKD detected and predicted by applying classification models: support vector machine (SVM), K-nearest neighbor (KNN), and logistic regression algorithm. It helps in predicting the likelihood of occurrence of disease on various different features. The two algorithms KNN and SVM are compared to find the algorithm that gives better accuracy. Further regression technique has been used to detect the disease based on, which the stages are classified by using GFR (glomerular filtration rate) formula.
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