A Random Forest Classifier Approach for Predicting Chronic Kidney Disease
S Vidya
, Dr. M. Ganesan , D. Haritha , V. Ragapriya
Chronic Kidney Disease, Prediction, Random Forest, CKD, Machine Learning
Chronic Kidney Disease (CKD) is an international fitness trouble with excessive morbidity, mortality, and different illnesses. Kidney ailment impacts 1 in 10 human beings worldwide. As the quantity of CKD sufferers increases, powerful predictors for early detection of CKD are needed. Therefore, a higher prognosis of persistent kidney ailment is wanted to save you endured progression. Machine learning assist clinicians gain this intention with their rapid and correct prediction capabilities. In this project, we've the usage of Random Forest Algorithm to decide if a person has CKD or Not. The CKD dataset has been taken from the Kaggle repository. This helps in the early detection of persistent kidney ailment.
"A Random Forest Classifier Approach for Predicting Chronic Kidney Disease", IJSDR - International Journal of Scientific Development and Research (www.IJSDR.org), ISSN:2455-2631, Vol.8, Issue 4, page no.435 - 440, April-2023, Available :https://ijsdr.org/papers/IJSDR2304081.pdf
Volume 8
Issue 4,
April-2023
Pages : 435 - 440
Paper Reg. ID: IJSDR_204068
Published Paper Id: IJSDR2304081
Downloads: 000347184
Research Area: Computer Science & Technology
Country: Puducherry, Puducherry, India
ISSN: 2455-2631 | IMPACT FACTOR: 9.15 Calculated By Google Scholar | ESTD YEAR: 2016
An International Scholarly Open Access Journal, Peer-Reviewed, Refereed Journal Impact Factor 9.15 Calculate by Google Scholar and Semantic Scholar | AI-Powered Research Tool, Multidisciplinary, Monthly, Multilanguage Journal Indexing in All Major Database & Metadata, Citation Generator
Publisher: IJSDR(IJ Publication) Janvi Wave
