Ensemble Prediction of Chronic Renal Disease by Using Fuzzy Clustering Technique


  • P. Nithya, G. Sumathi, R. Vijayalakshmi


Data Mining, Fuzzy System, Renal Disease, Neural Network, Pre- diction.


The term "data mining" refers to the process of discovering previously unknown patterns in massive databases. It is possible to extract valuable medical information from the medical field's heterogeneous data, which includes text, graphics, and photographs. The severity of a patient's survival, illness, etc. following a medical condition can be predicted using medical data that reveals a pattern of the disease. An automated calculation was utilized to generate the patient data set utilized for the analysis of patients with renal illness. Predictions are employed in patients with renal illness based on past predictions. Since this pertains to the patient's life and an accurate result is required, conventional theory is preferable to the probability theory utilized to get the outcome. As the population ages, chronic kidney illness will only become worse. In order to give patients the best care possible, it is crucial to be able to detect and anticipate renal illness. The traditional methods employed to identify patients suffering from renal disease, as well as the outcomes of the traditional methods applied in the if-then rule and in conjunction with the generated agency. This novel approach takes the output data set as input and generates results using a combination of two fuzzy systems—neu-ral blur systems—and neural networks. Instead of using probabilistic neural networks, this new method combines fuzzy logic with other types of systems that provide mathematical conclusions. Mathematical computations typically yield more precise outcomes.


Download data is not yet available.


Allen, Z. Iqbal, A. Green-Saxena et al., “Prediction of diabetic kidney dis- ease with machine learning algorithms, upon the initial diagnosis of type 2 diabetes mellitus,” BMJ Open Diabetes Research & Amp; Care, vol. 10, no.

1, p. e002560, 2022.

N. H. Chowdhury, M. B. Reaz, F. Haque et al., “Performance analysis of Con- ventional machine learning algorithms for identification of chronic kidney dis- ease in type 1 diabetes mellitus patients,” Diagnostics, vol. 11, no. 12, 2021.

E. M. Senan, M. H. Al-Adhaileh, F. W. Alsaade et al., “Diagnosis of chronic kidney disease using Effective classification algorithms and recursive feature Elimination techniques,” Journal of Healthcare Engineering, vol. 2021, p.1004767, 2021.

E. Dovgan, A. Gradišek, M. Luštrek et al., “Using machine learning models to predict the initiation of renal replacement therapy among chronic kidney disease patients,” PLoS One, vol. 15, no. 6, p. e0233976, 2020.

Sobrinho, A. C. M. D. S. Queiroz, L. D. Da Silva, E. D. B. Costa, M. E. Pinheiro, and A. Perkusich, “Computer-aided diagnosis of chronic kidney dis- ease in developing Countries: a comparative analysis of machine learning techniques,” IEEE Access, vol. 8, pp. 25407–25419, 2020.

M. Makino, R. Yoshimoto, M. Ono et al., “Artificial intelligence predicts the progression of diabetic kidney disease using big data machine learning,” Sci- entific Reports, vol. 9, no. 1, pp. 1–9, 2019.

N. A. Almansour, H. F. Syed, N. R. Khayat et al., “Neural network and support vector machine for the prediction of chronic kidney disease: a comparative study,” Computers in Biology and Medicine, vol. 109, pp. 101–111, 2019.

Y. Hayashi, “Detection of lower albuminuria levels and early development of diabetic kidney disease using an artificial intelligence-based rule extraction Approach,” Diagnostics, vol. 9, no. 4, 2019.

S. Ravizza, T. Huschto, A. Adamov et al., “Predicting the early risk of chronic kidney disease in patients with diabetes using real-world data,” Nature Medi- cine, vol. 25, no. 1, pp. 57–59, 2019.

T. R. Gadekallu, N. Khare, S. Bhattacharya et al., “Early detection of diabetic retinopathy using pca-firefly based deep learning model,” Electronics, vol. 9, no. 2, pp. 1–16, 2020.

K. Al-Rubeaan, K. Siddiqui, M. Alghonaim, A. M. Youssef, and D. AlNaqeb, “The Saudi Diabetic Kidney Disease study (Saudi-DKD): clinical characteris- tics and biochemical parameters,” Annals of Saudi Medicine, vol. 38, no. 1, pp. 46–56, 2018.

H. Polat, H. Danaei Mehr, and A. Cetin, “Diagnosis of chronic kidney disease based on support vector machine by feature selection methods,” Journal of Medical Systems, vol. 41, no. 4, p. 55, 2017.

O. Corporation, Machine Learning-Based Adaptive Intelligence: The Future of Cybersecurity Executive Summary. January, 2018.

J. J. Khanam and S. Y. Foo, “A comparison of machine learning algorithms for diabetes prediction,” ICT Express, vol. 7, no. 4, pp. 432–439, 2021.

E.-H. A. Rady and A. S. Anwar, “Prediction of kidney disease stages using data mining algorithms,” Informatics in Medicine Unlocked, vol. 15, p.100178, 2019.

M. Sohail, H. M. Ahmed, M. Shabbir, and K. Noor, “Predicting chronic kid- ney disease by using classification algorithms in,” WE!, vol. 11, no. 6, pp.1047–1050, 2020.




How to Cite

P. Nithya,. (2024). Ensemble Prediction of Chronic Renal Disease by Using Fuzzy Clustering Technique. International Journal of Intelligent Systems and Applications in Engineering, 12(21s), 3168–3173. Retrieved from https://ijisae.org/index.php/IJISAE/article/view/6005



Research Article