Improving Prediction of Chronic Kidney Disease Using KNN Imputed SMOTE Features and TrioNet Model

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 with missing 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 detecting CKD. This in-depth analysis demonstrates the model's capabilities and underscores its potential as a valuable tool in the diagnosis of CKD.