Automated Chronic Kidney Disease Detection Model with Knearest Neighbor


  • Tsehay Admassu Assegie Department of Computer Science, Aksum University, Axum, Ethiopia



Chronic kidney disease, Kidney disease; kidney disease detection; KNN; machine learning.


Chronic kidney disease is one of the most common disease in the world today. Kidney disease causes death if the patient is not threated at early stage. One of the challenge in kidney disease treatment is accurate identification of kidney disease at an early stage. Moreover, detecting kidney disease requires experienced nephrologist. However, in developing nations lack of medical specialist or nephrologist for identifying chronic kidney disease makes the problem more challenging. As alternative solution to kidney disease identification, researchers have developed many intelligent models using K-nearest Neighbors (KNN) algorithm. However, the accuracy of the existing KNN model has scope for improvement. Thus, this study proposed KNN based model for accurate identification of kidney disease at early stage. To develop optimized KNN model, we have employed error plot to find most favorable K value to obtain more accurate result than the existing models. To conduct experiments, study employed kidney disease dataset collected form publically available Kaggle data repository for training and testing the proposed model. Finally, we have evaluated the proposed model against predictive accuracy. The experimental result on the proposed model appears to prove that the predictive accuracy of the model is 99.86%.


Xun Li, Yao Wang, Chengxuan Wang, Sanqing Hu, Ying Xu, Fei Han, Jianghua Chen, Prediction of Renal Transplant Rejection and Acute Tubular Necrosis in Renal Transplant Based on SVM, 2012 5th International Conference on BioMedical Engineering and Informatics.

Njoud Abdullah Almansour, Hajra Fahim Syed, Nuha Radwan Khayat, Rawan Kanaan Altheeb, Renad Emad Juri, Jamal Alhiyafi, Saleh Alrashed, Sunday O. Olatunji, Neural network and support vector machine for the prediction of chronic kidney disease: A comparative study, Computers in Biology and Medicine 109 (2019).

Merve Doğruyol Başar, Aydın Akan, Chronic Kidney Disease Prediction with Reduced Individual Classifiers, Electrical (2018).

Arif-Ul-Islam, Shamim H Ripon, Rule Induction and Prediction of Chronic Kidney Disease Using Boosting Classifiers, Ant-Miner and J48 Decision Tree, International Conference on Electrical, Computer and Communication Engineering (ECCE), 7-9 February, IEEE, (2019)..

Vikas Chaurasia, Saurabh Pal, B.B. Tiwari, Chronic Kidney Disease: A Predictive model using Decision Tree, International Journal of Engineering Research and Technology. ISSN 0974-3154 Volume 11, Number 11 (2018).

Komal Kumar N, R. Lakshmi Tulasi, Vigneswari D, An ensemble multi-model technique for predicting chronic kidney disease, International Journal of Electrical and Computer Engineering (IJECE) Vol. 9, No. 2, April, (2019).

Assegie, T.A, Sushma S.J, A Support Vector Machine and Decision Tree Based Breast Cancer Prediction, International Journal of Engineering and Advanced Technology (IJEAT) ISSN: 2249 – 8958, Volume9 Issue-3, February, (2020).

Assegie, T.A, Nair, P.S, The Performance Of Different Machine Learning Models On Diabetes Prediction, International Journal Of Scientific & Technology Research Volume 9, Issue 01, January (2020).

Assegie, T.A, Sushma S.J, Prasanna Kumar S.C, Weighted Decision Tree Model for Breast Cancer Detection, Technology Reports of Knsai University, Volume 62, Issue 03, January (2020).

Dilip Singh Sisodia, Akanksha Verma, Prediction Performance of Individual and Ensemble learners for Chronic Kidney Disease, Proceedings of the International Conference on Inventive Computing and Informatics, IEEE, (2017).

Assegie, T.A, R. Lakshmi Tulasi, N. Komal Kumar, Breast cancer prediction model with decision tree and adaptive boosting, IAES International Journal of Artificial Intelligence (IJ-AI) Vol. 10, No. 1, March 2021.

Assegie, T.A, Support Vector Machine And K-Nearest Neighbor Based Liver Disease Classification Model, Indonesian Journal of Electronics, Electromedical, and Medical Informatics (IJEEEMI) Vol. 3, No. 1, February 2021.

Assegie, T.A, An optimized K-Nearest Neighbor based breast cancer detection, Journal of Robotics and Control (JRC) Volume 2, Issue 3, May 2020.

Kashi Sai Prasad, N. Chandra Sekhar Reddy, B. N. Puneeth, A Framework for Diagnosing Kidney Disease in Diabetes Patients Using Classifcation Algorithms, SN Computer Science, 2020.

Asif Salekin, John Stankovic, Detection of Chronic Kidney Disease and Selecting Important Predictive Attributes, IEEE, 2016.

Ravinra BV, N. Sriraam, M. Geetha, Chronic kdiney detection using back propagation neural network classsiferir, IEEE, 2019.




How to Cite

Assegie, T. A. . (2021). Automated Chronic Kidney Disease Detection Model with Knearest Neighbor. International Journal of Computer and Information Technology(2279-0764), 10(3).