KIDNEY DISEASE PREDICTION USING MACHINE LEARNING

KIDNEY DISEASE PREDICTION USING MACHINE LEARNING
Author(s):
Ch. Papa rao, Syed Amjad, MADDIGARI RUPA VISWANATH, Shaik Mansoor, Gunji Vasavi
Keywords:
Chronic Kidney Disease, Predictive Modelling, Data Elements, Target Class Features.
Abstract
A serious condition that can last a lifetime, chronic kidney disease (CKD) is brought on by either impaired kidney function or kidney cancer. It is possible to stop or limit the advancement of this chronic illness to the point when a patient's sole options for survival are dialysis or surgery. An earlier diagnosis and the right treatment can make this more likely to occur. The potential of various distinct machine learning techniques for offering an early diagnosis of chronic kidney disease (CKD) has been examined throughout this study. On this subject, a substantial quantity of research has been done. Nevertheless, by utilizing predictive modelling, we are strengthening our strategy. As such, in our methodology, we explore the relationship between data elements and target class features. Because predictive modelling allows for the introduction of improved attribute measures, we may use machine learning and predictive analytics to build a collection of prediction models.
Article Details
Unique Paper ID: 164718 Publication Volume & Issue: Volume 10, Issue 12 Page(s): 2574 - 2579
Article Preview & Download

Share This Article

NCSEM 2024

National Conference on Sustainable Engineering and Management - 2024

Last Date: 15th March 2024

IJIRT.org enables door in research by providing high quality research articles in open access market.

Send us any query related to your research on editor@ijirt.org