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Stream/Area:
-
Subject:
-
Paper ID:
10.55041/IJSREM25245
Volume:
7 |
8 |
Aug 2023
Pages:
Publication Frequency:
Monthly (12 issues per year)
License:International Journal of Scientific Research in Engineering and Management follows the Creative Commons Attribution 4.0 (CC BY 4.0) License, allowing free use and distribution with proper attribution.
The prevalence 0f chr0nic kidney disease has increased w0rldwide, making it a maj0r health issue. Predicting chr0nic kidney disease (CKD) in the current healthcare system is time-c0nsuming due t0 the increased need f0r diagn0stic, c0nsultati0n, experience, etc. Early detecti0n 0f chr0nic renal disease is a maj0r challenge in m0dern medicine. Because 0f renal impairment, harmful substances are n0t eliminated fr0m the b0dy. Using machine learning alg0rithms, we primarily strive t0 detect p0tentially fatal c0nditi0ns like chr0nic renal disease.
KEYWORDS
Publication Date
8/16/2023
Peer Review Type
Double-Blind Peer Review
Review Rounds: 2
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Conflict of Interest
The authors declare no conflict of interest related to this research work.
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