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Comparison of Performance of Various Machine Learning Classification Techniques With Ensemble Classifiers for Prediction of Chronic Kidney Disease

Comparison of Performance of Various Machine Learning Classification Techniques With Ensemble Classifiers for Prediction of Chronic Kidney Disease

Noopur Goel
Copyright: © 2021 |Pages: 26
ISBN13: 9781799844204|ISBN10: 179984420X|ISBN13 Softcover: 9781799852797|EISBN13: 9781799844211
DOI: 10.4018/978-1-7998-4420-4.ch011
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MLA

Goel, Noopur. "Comparison of Performance of Various Machine Learning Classification Techniques With Ensemble Classifiers for Prediction of Chronic Kidney Disease." Innovations in Digital Branding and Content Marketing, edited by Subhankar Das and Subhra Rani Mondal, IGI Global, 2021, pp. 245-270. https://doi.org/10.4018/978-1-7998-4420-4.ch011

APA

Goel, N. (2021). Comparison of Performance of Various Machine Learning Classification Techniques With Ensemble Classifiers for Prediction of Chronic Kidney Disease. In S. Das & S. Mondal (Eds.), Innovations in Digital Branding and Content Marketing (pp. 245-270). IGI Global. https://doi.org/10.4018/978-1-7998-4420-4.ch011

Chicago

Goel, Noopur. "Comparison of Performance of Various Machine Learning Classification Techniques With Ensemble Classifiers for Prediction of Chronic Kidney Disease." In Innovations in Digital Branding and Content Marketing, edited by Subhankar Das and Subhra Rani Mondal, 245-270. Hershey, PA: IGI Global, 2021. https://doi.org/10.4018/978-1-7998-4420-4.ch011

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Abstract

Chronic kidney disease has become a very prevalent problem worldwide and almost 10% of the population is suffering and millions of people are dying every year because of chronic kidney disease. Numerous machine learning and data mining techniques are applied by many researchers round the world to diagnose the presence of chronic kidney disease, so that the patients of chronic kidney disease may get benefitted in terms of getting proper healthcare follow-up. In this chapter, Experiment 1 is conducted by implementing five different classifiers on the original chronic kidney disease dataset. In Experiment 2, two different ensemble classifiers are implemented combining all five individual classifiers. The Results of both the Experiments 1 and 2 are compared, and it is observed that the accuracy of ensemble classifiers is far better than the accuracy of individual classifiers. It may be concluded that the two experiments conducted in the chapter show the performance of ensemble classifiers is better than the individual classifiers.

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