Predicting Chronic Kidney Disease by Applying Feature Engineering & Performance Analysis of Machine Learning Classifiers

Persistent disease is defined as one that develops slowly but remains constant over time. One of the long-term diseases is chronic kidney disease. There are various stages of CKD severity. While therapy has been shown to postpone improvement, it becomes very serious over time, and if not managed pro...

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Bibliographic Details
Published in2021 5th International Conference on Electronics, Communication and Aerospace Technology (ICECA) pp. 1528 - 1533
Main Authors Kumar, R. Praveen, Erive, Sarath, Jayasri, Ganta, Srujana, Annapalli, Niharika, Vallala
Format Conference Proceeding
LanguageEnglish
Published IEEE 02.12.2021
Subjects
Chronic Kidney Disease
Classification algorithms
Decision Tree and Random Forest
Feature Engineering
Kidney
Logistic Regression
Machine Learning
Machine learning algorithms
Medical treatment
Organ transplantation
Performance analysis
Prognostication
Regression tree analysis
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Summary:Persistent disease is defined as one that develops slowly but remains constant over time. One of the long-term diseases is chronic kidney disease. There are various stages of CKD severity. While therapy has been shown to postpone improvement, it becomes very serious over time, and if not managed properly, CKD can lead to cardiovascular disease in the early stages. If our kidneys aren't working properly, we'll need dialysis or a kidney transplant. The final stage of renal disease is kidney failure, which is treated with dialysis or a kidney transplant. The classification techniques are the main subject of this research, which includes logistic regression, tree-based decision tree, and random forest and analyzed the performance of these techniques.
DOI:10.1109/ICECA52323.2021.9675935