This paper is published in Volume-7, Issue-4, 2021
Area
Computer Science
Author
Annapoorna B. A., Nisarga Y. N., Rachana R. Shastry, Sreelatha P. K.
Org/Univ
Sai Vidya Institute of Technology, Bangalore, Karnataka, India
Pub. Date
14 July, 2021
Paper ID
V7I4-1351
Publisher
Keywords
Chronic Kidney Disease, Glomerular Filtration Rate, Naïve Bayes, Decision Tree, Random Forest, K-Nearest Neighbors Classifier

Citationsacebook

IEEE
Annapoorna B. A., Nisarga Y. N., Rachana R. Shastry, Sreelatha P. K.. Prediction of chronic kidney disease and diet recommendation, International Journal of Advance Research, Ideas and Innovations in Technology, www.IJARIIT.com.

APA
Annapoorna B. A., Nisarga Y. N., Rachana R. Shastry, Sreelatha P. K. (2021). Prediction of chronic kidney disease and diet recommendation. International Journal of Advance Research, Ideas and Innovations in Technology, 7(4) www.IJARIIT.com.

MLA
Annapoorna B. A., Nisarga Y. N., Rachana R. Shastry, Sreelatha P. K.. "Prediction of chronic kidney disease and diet recommendation." International Journal of Advance Research, Ideas and Innovations in Technology 7.4 (2021). www.IJARIIT.com.

Abstract

Chronic renal disorder is that the sort of disease within which there's a decrease in kidney function over a period of months or years. Early prediction of CKD is one in all the main problem in medical fields. So automated. tools which use. machine learning techniques determine the patient’s kidney condition which will be helpful to the doctors in prediction of disease.. Our system retrieves the features which are significantly affects the human with CKD, and so the ML technique which automates the classification of the disease into different stages. Our main goal is to predict the disease stage and suggest suitable diet for CKD patients using classification algorithms on medical test records. Diet recommendations for patients are going to be given per the potassium zone which is calculated using blood potassium level to weigh down the progression of CKD.