Chronic Kidney Disease Prediction Using Different Algorithms
DOI:
https://doi.org/10.32628/CSEIT20652Keywords:
CKD, SVM, Machine Learning, Random Forest Classifier, KNN, Naïve Bayes.Abstract
Chronic Kidney Disease (CKD) is a disease which doesn't shows symptoms at all or in some cases it doesn't show any disease specific symptoms it is hard to predict, detect and prevent such a disease and this could be lead to permanently health damage, but machine learning can be hope in this problem it is best in prediction and analysis. The objective of paper is to build the model for predicting the Chronic Kidney Disease using various machine learning classification algorithm. Classification is a powerful machine learning technique that is commonly used for prediction. Some of the classification algorithm are Logistic Regression, Support Vector Machine, Naïve Bayes, Random Forest Classifier, KNN. This paper investigate which algorithm is used for the improving the accuracy in the prediction of Chronic Kidney Disease. And, a comparative analysis on the accuracy and mean squared error is to done for predicting the best model.
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