Comparison of Machine Learning Algorithms for Predicting Chronic Kidney Disease

Title

Comparison of Machine Learning Algorithms for Predicting Chronic Kidney Disease

Creator

James N.; Kaushik J.

Description

Early detection and characterization of chronic renal disease are crucial to ensure that patients receive the best possible treatment. This study uses data mining techniques to uncover hidden information about patients. The outcomes of using the Random Forest, Multilayer Perceptron (MLP), Support Vector Machine (SVM), Decision Tree, XGBoost, LGBM Classifier, GaussianNB, KNeighbors Classifier, and XGBRF classifier have been compared. In our study, we demonstrate that Random Forest and XGBoost algorithms are more effective in classifying and predicting the severity level of chronic kidney disease 2022 IEEE.

Source

2022 2nd International Conference on Advance Computing and Innovative Technologies in Engineering, ICACITE 2022, pp. 1134-1139.

Date

2022-01-01

Publisher

Institute of Electrical and Electronics Engineers Inc.

Subject

Chronic Kidney Disease; CKD classification; Machine learning algorithms; Random Forest; XGBoost

Coverage

James N., Christ (Seems to Be University), Department of Data Science, Pune, India; Kaushik J., Christ (Seems to Be University), Department of Data Science, Pune, India

Rights

Restricted Access

Relation

ISBN: 978-166543789-9

Format

Online

Language

English

Type

Conference paper

Identifier

https://doi.org/10.1109/ICACITE53722.2022.9823572

https://www.scopus.com/inward/record.uri?eid=2-s2.0-85135454910&doi=10.1109%2fICACITE53722.2022.9823572&partnerID=40&md5=65ef5e0557bf4c52f52418742048c87b