International Journal of Engineering Research in Computer Science and Engineering (IJERCSE)

A survey on Support Vector Machine in Chronic Kidney Disease Prediction

Author : Pallavi Sharma ¹ Dr. Gurmanik Kaur ²

Date of Publication :21st July 2021

Abstract: Chronic kidney disease (CKD) is one of the main reasons behind death all throughout the world these days. The term â€œchronic kidney diseaseâ€ signifies enduring damage to the kidneys that can deteriorate over the long run. On the off chance that the damage is very terrible, then kidney may quit working. This is called End stage renal failure. The prediction of CKD is perhaps the most significant and challenging issues in medical services examination. To acquire the hidden data from the given dataset, data mining is utilized to settle on the decisions. Big data is the latest advancement used to store and deal with voluminous data and that data can be organized data, unstructured data and semi-organized data. This paper aims to assist in the prediction of chronic kidney disease (CKD) by utilizing the support vector machine (SVM) classifier in the medical domain.

Reference :

1. Neelamadhab Padhy, Dr. Pragnyaban Mishra and Rasmita Panigrahi, “The Survey of Data Mining Applications and Feature Scope,” International Journal of Computer Science, Engineering and Information Technology (IJCSEIT), 2(3), pp 43-58, 2012.
2. J.C. Prather, D. F. Lobach, L. K. Goodwin, J. W. Hales, M. L. Hage, and W. E. Hammond, “Medical data mining: knowledge discovery in a clinical data warehouse,” Proceedings of the AMIA Annual Fall Symposium, pp 101–105, 1997.
3. Subhash Chandra Pandey, “Data mining techniques for medical data: A review,” International Conference on Signal Processing, Communication, Power and Embedded System, Paralakhemundi, India, pp 972-982, 2016.
4. H. Polat, H. D. Mehr, and A. Cetin, “Diagnosis of Chronic Kidney Disease Based on Support Vector Machine by Feature Selection Methods,” J. Med. Syst., Vol. 41, no. 4, Apr. 2017.
5. K. Chandel, V. Kunwar, S. Sabitha, T. Choudhury, and S. Mukherjee, “A Comparative study on thyroid disease detection using K-nearest neighbor and Naive Bayes classification techniques,” CSI Trans. ICT, Vol. 4, no. 2–4, pp. 313–319, Dec. 2016.
6. Vijayarani, S. and Dhayanand, S., “Data mining classification algorithms for kidney disease prediction”, Int. J. Cybern. Inf. (IJCI), Vol. 4, No. 4, pp 13–25, 2015. A. R. Shetty, F. B. Ahmed, V. M. Naik, “CKD Prediction Using Data Mining Technique as SVM and KNN with Pycharm”, International Research Journal of Engineering and Technology, Vol. 6, No. 5, pp 4399-4405, 2019.
7. S. Tekale, S. Pranjal, S. Wandhekar, A. Chatorikar, “Prediction of Chronic Kidney Disease Using Machine Learning Algorithm”, International Journal of Advanced Research in Computer and Communication Engineering, Vol. 7, No. 10, pp 92-96, 2018
8. G. Kaur, A. Sharma, “Predict Chronic Kidney Disease using Data Mining Algorithms in Hadoop”, International Journal of Engineering Researches and Management Studies, Vol. 5, No. 2, pp 34-48, 2018.
9. S. Vijayarani, S. Dhayanand, “Kidney Disease Prediction using SVM and ANN Algorithms”, International Journal of Computing and Business Research, Vol. 6, No. 2, 2015.
10. A. Charleonnan, T. Fufaung, T. Niyomwong, W. Chokchueypattanakit, S. Suwannawach, N. Ninchawee, “Predictive Analytics for Chronic Kidney Disease using Machine Learning Techniques”, IEEE International Conference on Management and Innovation Technology, 2016
11. Z. Chen, X. Zhang, Z. Zhang, “Clinical risk assessment of patients with chronic kidney disease by using clinical data and multivariate models”, Int. Urol. Nephrol., Vol. 48, No. 12, pp 2069–2075, 2016.