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

作　　者：Nazik Alturki Abdulaziz Altamimi Muhammad Umer Oumaima Saidani Amal Alshardan Shtwai Alsubai Marwan Omar Imran Ashraf 

机构地区：[1]Department of Information Systems,College of Computer and Information Sciences,Princess Nourah bint Abdulrahman University,P.O.Box 84428,Riyadh,11671,Saudi Arabia [2]Department College of Computer Science and Engineering,University of Hafr Al-Batin,Hafar,Al-Batin,39524,Saudi Arabia [3]Department of Computer Science&Information Technology,The Islamia University of Bahawalpur,P.O.Box 63100,Bahawalpur,Pakistan [4]Department of Computer Science,College of Computer Engineering and Sciences,Prince Sattam bin Abdulaziz University,P.O.Box 151,Al-Kharj,11942,Saudi Arabia [5]Information Technology and Management,Illinois Institute of Technology,Chicago,IL 60616-3793,USA [6]Information and Communication Engineering,Yeungnam University,Gyeongsan,38541,Korea

出　　处：《Computer Modeling in Engineering & Sciences》2024年第6期3513-3534,共22页工程与科学中的计算机建模（英文）

基　　金：funded by Princess Nourah bint Abdulrahman University Researchers Supporting Project Number PNURSP2024R333,Princess Nourah bint Abdulrahman University,Riyadh,Saudi Arabia.

摘　　要：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.

关 键 词：Precisionmedicine chronic kidney disease detection SMOTE missing values healthcare KNNimputer ensemble learning 

分 类 号：R692[医药卫生—泌尿科学] TP3[医药卫生—外科学]