This is an Open Access article distributed under the terms of the Creative Commons Attribution License (CC BY 4.0) (http://creativecommons.org/licenses/by/4.0)
Champa M S , Prof. Rekha B S
In this proposed project, the patients will perform the registration, once the patient is registered into the application then the patients are allowed to enter there scanning attributes of kidney diseases and then the user kidney disease stage level is determined by making use of C5.0 and Naïve Bayes supervised machine learning algorithm, the system provides, the user will get suggestions from the doctor at various level of kidney stage and if the user belongs to kidney stage 5 then patient will get suggestions patient will get suggestions as well as appointment request.
 Kubra Eroglu, Tugba Palabas,” The Impact on the Classification Performance of the Combined Use of Different Classification Methods and Different Ensemble Algorithms in Chronic Kidney Disease Detection”, Journal of Applied Engineering, vol. 53, no. 2, Feb., pp. 1144-1149, 2002.
 Veenita Kunwar 1 Khushboo Chandel2 A. Sai Sabitha3 Abhay Bansal4“ Chronic Kidney Disease Analysis Using Classification Technique”, in Proc. of the 2018 1st Int. Conf. on Information Technology, IT 2018, 19-21 May 2018, Gdansk, Poland[Online].
 K.R.Lakshmi1, Y.Nagesh2 and M.VeeraKrishna3, “Performance Comparison of Three Data Mining Techniques for Predicting Kidney Dialysis Survivability”, International Journal of Advances in Engineering & Technology, Mar. 2014, Vol. 7, Issue 1, pp. 242-252
 Naganna Chetty, Kunwar Singh Vaisla, Sithu D Sudarsan,”Role of Attributes Selection in Classification of Chronic Kidney Disease Patients”, in Proc. of the 2015 1st Int. Conf. on Applied Science, 2015, 21-25 june 2015, Russia [Online].
 Zeinab Sedighi, Hossein Ebrahimpour-Komleh, Seyed jalaleddin Mousavirad, ”Featue selection effects on Kidney disease analysis,” in Proc. of the 2016 7th Int.Conf. on Formal Engineering Method, 2016, 20-25 May 2016, Washington, DC[Online].
 Ani R1, Greeshma Sasi1, Resmi Sankar U1, O.S Deepa, ”Decision Support system for diagnosis and prediction of Chronic Renal Failure using Random Subspace Classification”, 2016 Intl. Conference on Advances in Computing, Communications and Informatics (ICACCI), Sept. 21-24, 2016.
 ]Miguel A. Estudillo-Valderrama, Alejandro TalaminosBarroso, Laura M. Roa,” A Distributed Approach to Alarm Management in Chronic Kidney Disease”, IEEE Journal of Biomedical and Health Informatics, November 2014,vol 18,no 16.
 Merve DoÄŸruyol BaÅŸar1, Pelin SarÄ±1 , Niyazi KÄ±lÄ±ç2 , AydÄ±n Akan, ”Detection of Chronic Kidney Disease by Using Adaboost Ensemble Learning Approach”, in Proc. of the 2015 1st Int. Conf. on Data Mining, 2015, 17-20 September 2015, Singapore [Online].
 Anu Chaudhary, Puneet Garg,(2014) “Detecting and Diagnosing a Disease by Patient Monitoring System”, International Journal of Mechanical Engineering And Information Technology, Vol. 2 ,Issue 6 ,June 2017,Page No: 493-499.
 Lakshmi. K.R, Nagesh. Y and VeeraKrishna. M, (2014) “Performance Comparison Of Three Data Mining Techniques For Predicting Kidney Dialysis Survivability”, International Journal of Advances in Engineering & Technology, Mar., Vol. 7, Issue 1, pg no. 242-254.
Information Science and Engineering, RV College of Engineering, Bengaluru, INDIA, +916360920131, ( email: firstname.lastname@example.org)
No. of Downloads: 25 | No. of Views: 1643