Article: Early detection of chronic kidney disease using data mining methods Journal: International Journal of Advanced Intelligence Paradigms (IJAIP) 2024 Vol.28 No.1/2 pp.86 - 99 Abstract: Early examination and acknowledgment of kidney disease is a fundamental issue to help and stop the kidney failure. Data mining and examination strategies can be used for anticipating chronic kidney disease (CKD) by utilising obvious patient's data and assurance records. In this examination, fuzzy neural algorithm is used for predicting CKD or notCKD. Pre-treatment of the data is performed to trait any missing data and perceive the components that should be considered in the identification models. The different examination models are assessed and contemplated in perspective of exactness and accuracy of estimations. The examination gives a decision contraption that can help in the identification of CKD. Various undertakings are done to adjust to remedial data impact on one hand, and to get important gaining from it, predict diseases and suspect the cure of course. This incited experts to apply all the particular progressions like huge data examination, farsighted examination, machine learning and learning computations with a particular true objective to remove profitable data and help in choosing. In this paper, we will present an examination on the headway of colossal data in human administrations structure. Inderscience Publishers - linking academia, business and industry through research

Title: Early detection of chronic kidney disease using data mining methods

Authors: Suresh Babu Satukumati; M. Nirupama Bhat

Addresses: Department of Information Technology, VFSTR (Deemed to be University), Vadlamudi-522213, Guntur, India ' Department of Computer Science Engineering, VFSTR (Deemed to be University), Vadlamudi-522213, Guntur, India

Abstract: Early examination and acknowledgment of kidney disease is a fundamental issue to help and stop the kidney failure. Data mining and examination strategies can be used for anticipating chronic kidney disease (CKD) by utilising obvious patient's data and assurance records. In this examination, fuzzy neural algorithm is used for predicting CKD or notCKD. Pre-treatment of the data is performed to trait any missing data and perceive the components that should be considered in the identification models. The different examination models are assessed and contemplated in perspective of exactness and accuracy of estimations. The examination gives a decision contraption that can help in the identification of CKD. Various undertakings are done to adjust to remedial data impact on one hand, and to get important gaining from it, predict diseases and suspect the cure of course. This incited experts to apply all the particular progressions like huge data examination, farsighted examination, machine learning and learning computations with a particular true objective to remove profitable data and help in choosing. In this paper, we will present an examination on the headway of colossal data in human administrations structure.

Keywords: keen examination; machine adjustment; huge data examination; kidney failure problems; learning estimations; diagnostics; data examination; data mining; sensible examination.

DOI: 10.1504/IJAIP.2024.139951

International Journal of Advanced Intelligence Paradigms, 2024 Vol.28 No.1/2, pp.86 - 99

Received: 22 Jun 2018
Accepted: 21 Jul 2018
Published online: 15 Jul 2024 *

Full-text access for editors Full-text access for subscribers Purchase this article Comment on this article

Title: Early detection of chronic kidney disease using data mining methods

Keep up-to-date