Open Access

ARTICLE

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

Nazik Alturki¹, Abdulaziz Altamimi², Muhammad Umer^3,*, Oumaima Saidani¹, Amal Alshardan¹, Shtwai Alsubai⁴, Marwan Omar⁵, Imran Ashraf^6,*

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

* Corresponding Authors: Muhammad Umer. Email: ; Imran Ashraf. Email: