Ensemble Prediction of Chronic Renal Disease by Using Fuzzy Clustering Technique

Authors

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

Keywords:

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

Abstract

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.

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Published

26.03.2024

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

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Section

Research Article