KIDNEY DISEASE PREDICTION USING DIFFERENT CLASSIFICATION TECHNIQUES

KIDNEY DISEASE PREDICTION USING DIFFERENT CLASSIFICATION TECHNIQUES

Registration ID: IJNRD_184040

Published ID: IJNRD2212196

DOI: Click Here to Get

Ritika Garad , Harsh Said , Omkar Bhosale , Rakeshkumar Visave , Usture Samarth

disease, prediction, algorithm, SVM,k-nearest neighbor algorithm ; the Latent Dirichlet Allocation

Based on the rise in chronic kidney disease (CKD) incidence in recent years, a more accurate early prediction model is required to identify high-risk individuals before they develop end-stage renal failure. To date, it has been determined that diabetes mellitus6, obesity5, and female sex4 are all significant risk factors for chronic renal disease. Recently, several biomarkers connected to CKD have been identified. Treatment for renal failure and chronic kidney disease is both expensive and inefficient. Only around 5% of those with early CKD are aware of their illness, though20. Once CKD is detected, glomerular damage has typically reached over 50% and is irreversible. An accurate chronic renal illness prediction can be very helpful in this regard. This study aims to forecast renal failure. Static Vector Machine (SVM)

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"KIDNEY DISEASE PREDICTION USING DIFFERENT CLASSIFICATION TECHNIQUES", IJNRD - INTERNATIONAL JOURNAL OF NOVEL RESEARCH AND DEVELOPMENT (www.IJNRD.org), ISSN:2456-4184, Vol.7, Issue 12, page no.b876-b883, December-2022, Available :https://ijnrd.org/papers/IJNRD2212196.pdf

Volume 7 Issue 12, December-2022

Pages : b876-b883

Paper Reg. ID: IJNRD_184040

Published Paper Id: IJNRD2212196

Downloads: 000121147

Research Area: Engineering

Country: aundh,pune, maharashtra, india

Published Paper PDF: https://ijnrd.org/papers/IJNRD2212196.pdf

Published Paper URL: https://ijnrd.org/viewpaperforall?paper=IJNRD2212196

Journal Name: INTERNATIONAL JOURNAL OF NOVEL RESEARCH AND DEVELOPMENT(IJNRD)

ISSN: 2456-4184 | IMPACT FACTOR: 8.76 Calculated By Google Scholar | ESTD YEAR: 2016

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