Open Access
Issue
ITM Web Conf.
Volume 57, 2023
Fifth International Conference on Advances in Electrical and Computer Technologies 2023 (ICAECT 2023)
Article Number 01011
Number of page(s) 12
Section Software Engineering & Information Technology
DOI https://doi.org/10.1051/itmconf/20235701011
Published online 10 November 2023
  1. Prasanta Kumar Sahoo;Goraknath Kashyap Modali “An Effective Way to Identify Chronic Kidney Disease Using Machine Learning” 2023 International Conference on Emerging Smart Computing and Informatics (ESCI) [Google Scholar]
  2. Debabrata Swain; Hardeep Patel, Kevin Patel, Vivek Sakariya, Nishtha Chaudhar “An Intelligent Clinical Support System For The Early Diagnosis Of The Chronic Kidney Disease” 2022 IEEE 2nd International Symposium on Sustainable Energy, Signal Processing and Cyber Security (iSSSC) [Google Scholar]
  3. Prasanta Kumar Sahoo;Goraknath Kashyap Modali “An Effective Way to Identify Chronic Kidney Disease Using Machine Learning” 2023 International Conference on Emerging Smart Computing and Informatics (ESCI) [Google Scholar]
  4. Debabrata Swain; Hardeep Patel, Kevin Patel, Vivek Sakariya, Nishtha Chaudhar “An Intelligent Clinical Support System For The Early Diagnosis Of The Chronic Kidney Disease” 2022 IEEE 2nd International Symposium on Sustainable Energy, Signal Processing and Cyber Security (iSSSC) [Google Scholar]
  5. Women More Affected By Kidney Diseases, Women.prothomalo.com, 2018.[Online]. Available: http://women.prothomalo.com/bangladesh/Womenmore-affected-by-kidneydiseases. [Accessed:20Sep-2019]. [Google Scholar]
  6. Sujata Drall, Gurdeep Singh Drall, Sugandha Singh, Bharat Bhushan Naib, “Chronic Kidney Disease Prediction Using Machine Learning: A New Approach”, International Journal of Management, Technology And Engineering, vol. 8, no. 5, pp. 278-287, 2018. [Google Scholar]
  7. Vivekanand Jha, ”Chronic Kidney Disease Global Dimension and Perspectives”, Lancet, National Library of Medicine, 2013 [Google Scholar]
  8. Siddeshwar Tekale, “Prediction of Chronic Kidney disease Using Machine Learning, International Journal of Advanced Research in Computer and Communication Engineering, 2018. [Google Scholar]
  9. L. Rubini, “Early stage of chronic kidney disease UCI machine learning repository, ” 2015. [Online]. Available: Kidney Disease. http://archive.ics.uci.edu/ml/datasets/Chronic [Google Scholar]
  10. P. Yildirim, “Chronic kidney disease prediction on imbalanced data by multilayer perceptron: Chronic kidney disease prediction, ” in Computer Software and Applications Conference (COMPSAC), 2017 IEEE 41st Annual, 2017. [Google Scholar]
  11. Khamparia, G. Saini, B. Pandey, S. Tiwari, D. Gupta, and A. Khanna, “KDSAE: chronic kidney disease classificationwith multimedia data learning using deep stacked autoen coder network, ” Multimedia Tools and Applications, vol. 79, no. 4748, pp. 35425–35440, 2019. [Google Scholar]
  12. Salekin and J. Stankovic, “Detection of chronic kidney disease and selecting important predictive attributes, ” in Healthcare Informatics (ICHI), 2016 IEEE International Conference On, 2016 [Google Scholar]
  13. M. S. Wibawa, I. M. D. Maysanjaya, and I. M. A. W. Putra, “Boosted classifier and features selection for enhancing chronic kidney disease diagnoses, ” in Proceedings of the 2017 [Google Scholar]
  14. 5th international conference on cyber and IT service management (CITSM), pp. 1–6, IEEE, Denpasar, Indonesia, August 2017. [Google Scholar]
  15. Ene-Iordache B, Perico N, Bikbov B, et al. Chronic kidney disease and cardiovascular risk in six regions of the world (ISN-KDDC): a cross-sectional study. Lancet Glob Health 2016; 4: e307–19. [CrossRef] [Google Scholar]
  16. Wong CW, Wong TY, Cheng CY, Sabanayagam C. Kidney and eye diseases: common risk factors, etiological mechanisms, and pathways. Kidney Int 2014; 85: 1290–302. [CrossRef] [Google Scholar]

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