Employing Machine Learning Models in the Prediction and Diagnosis of Chronic Kidney Disease

Authors

  • Anandkumar A. Sutariya, Dushyantsinh B. Rathod

Keywords:

Chronic Kidney Disease, K-Nearest Neighbor, Decision Tree, Bayesian Classifier, Machine Learning and AI techniques.

Abstract

Chronic Kidney Failure is the medical term for chronic kidney disease. It portrays the moderate disintegration of renal disappointment and how, assuming that constant kidney infection has advanced to a high level stage, a high volume of fluid and undesirable electrolytes may develop in the body. We may see less evidence of chronic renal disease in the early phases. The treatment for chronic kidney disease focuses on slowing down the process of kidney damage. Without a trace of dialysis or kidney migration, persistent renal sickness can advance to the last periods of kidney annihilation, which is inoperable. The focal point of this examination is on early discovery of constant obstructive pneumonia illness utilizing different AI techniques, which are K-Nearest Neighbour, Decision Tree and Bayesian Classifier.

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Published

26.03.2024

How to Cite

Anandkumar A. Sutariya. (2024). Employing Machine Learning Models in the Prediction and Diagnosis of Chronic Kidney Disease. International Journal of Intelligent Systems and Applications in Engineering, 12(21s), 4792 –. Retrieved from https://ijisae.org/index.php/IJISAE/article/view/7085

Issue

Vol. 12 No. 21s (2024)

Section

Research Article

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Copyright (c) 2024 Anandkumar A. Sutariya, Dushyantsinh B. Rathod

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