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

• Published in: 2021 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
• Language: English
• 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
• DOI: 10.1109/ICECA52323.2021.9675935

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.