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- Title
Improving Prediction of Chronic Kidney Disease Using KNN Imputed SMOTE Features and TrioNet Model.
- Authors
Alturki, Nazik; Altamimi, Abdulaziz; Umer, Muhammad; Saidani, Oumaima; Alshardan, Amal; Alsubai, Shtwai; Omar, Marwan; Ashraf, Imran
- Abstract
Chronic kidney disease (CKD) is a major health concern today, requiring early and accurate diagnosis. Machine learning has emerged as a powerful tool for disease detection, and medical professionals are increasingly using ML classifier algorithms to identify CKD early. This study explores the application of advanced machine learning techniques on a CKD dataset obtained from the University of California, UC Irvine Machine Learning repository. The research introduces TrioNet, an ensemble model combining extreme gradient boosting, random forest, and extra tree classifier, which excels in providing highly accurate predictions for CKD. Furthermore, K nearest neighbor (KNN) imputer is utilized to deal withmissing values while synthetic minority oversampling (SMOTE) is used for class-imbalance problems. To ascertain the efficacy of the proposed model, a comprehensive comparative analysis is conducted with various machine learning models. The proposed TrioNet using KNN imputer and SMOTE outperformed other models with 98.97% accuracy for detectingCKD. This in-depth analysis demonstrates the model's capabilities and underscores its potential as a valuable tool in the diagnosis of CKD.
- Subjects
UNIVERSITY of California, Irvine; UNIVERSITY of California, San Francisco; CHRONIC kidney failure; MACHINE learning; RANDOM forest algorithms; BOOSTING algorithms
- Publication
CMES-Computer Modeling in Engineering & Sciences, 2024, Vol 139, Issue 3, p3513
- ISSN
1526-1492
- Publication type
Article
- DOI
10.32604/cmes.2023.045868