Literature Review Discussion of Machine Learning Algorithms for Predicting Chronic

Rajesh Yadav

Abstract

Being one of the most serious and most occurring diseases in our era, chronic kidney disease requires a fast and correct diagnosis. The usage of machine learning in medicine has now grown to such a level that it could be a means of diagnosis. The doctor can be the first one to get the ailment by using machine learning classifier algorithms. This has been the data science sector’s new horizons, just as a lot of other industries have been covered, healthcare in the first place. Now, most of the chronic diseases are no longer a mystery to healthcare workers who can foretell it using data analytics. Multiple machine learning (ML) algorithms are likely to be the future of healthcare. Not only do they identify chronic diseases, but they also forecast them at an early stage. This literature survey will focus on the effectiveness of various machine learning algorithms, such as KNN, SVM, Random Forest, RNN, etc., in predicting CKD.

Literature Review Discussion of Machine Learning Algorithms for Predicting Chronic

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