Literature Review and Discussion of Machine Learning Algorithms for Predicting Chronic Kidney Disease

Year : 2024 | Volume : 15 | Issue : 03 | Page : 34 39
    By

    Rajesh Yadav,

  1. Assistant Professor, Department of Computer Science, South Indian Education Society College of Arts, Science and Commerce (Empowered Autonomous), Mumba, Maharashtra, India

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 chronic diseases are no longer a mystery to healthcare workers who can foretell them 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 ML algorithms, such as K-nearest neighbor (KNN), Support Vector Machine (SVM), Random Forest, Recurrent Neural Network (RNN), etc., in predicting chronic kidney disease (CKD).

Keywords: Data analytics, machine learning algorithms, chronic kidney disease (CKD), disease prediction

[This article belongs to Journal of Computer Technology & Applications ]

How to cite this article:
Rajesh Yadav. Literature Review and Discussion of Machine Learning Algorithms for Predicting Chronic Kidney Disease. Journal of Computer Technology & Applications. 2024; 15(03):34-39.
How to cite this URL:
Rajesh Yadav. Literature Review and Discussion of Machine Learning Algorithms for Predicting Chronic Kidney Disease. Journal of Computer Technology & Applications. 2024; 15(03):34-39. Available from: https://journals.stmjournals.com/jocta/article=2024/view=177282


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Regular Issue Subscription Review Article
Volume 15
Issue 03
Received 08/09/2024
Accepted 12/09/2024
Published 07/10/2024
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