Chronic Kidney Disease Prediction with Ensemble Approaches

Mr.P.L.SUBRAMANIAN , Mr.M.K.SANJAY KUMAR , Mr.P.SHREE BALAJI , Mr.B.RAMANATHAN

CKD, Accuracy, Ensemble Approach

Chronic kidney disease (CKD) represents a critical public health challenge globally, demanding early detection and intervention to mitigate its adverse effects. This initiative presents a comprehensive approach to developing a robust machine learning model for the early prediction of CKD, leveraging the power of random forest, gradient boosting, and logistic regression algorithms. By analysing extensive CKD datasets encompassing clinical and demographic attributes, advanced techniques in ensemble learning are employed to enhance diagnostic accuracy. Comparative analyses against individual classifiers demonstrate the superiority of the ensemble approach in CKD prediction. Moreover, rigorous validation techniques ensure the model's robustness and generalization across diverse patient populations and clinical scenarios. The proposed ensemble machine learning framework represents a significant advancement in CKD prediction, offering enhanced diagnostic accuracy and early intervention opportunities. By leveraging the strengths of multiple algorithms and advanced ensemble techniques, the model provides clinicians with a reliable tool for proactive CKD management.

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"Chronic Kidney Disease Prediction with Ensemble Approaches", IJSDR - International Journal of Scientific Development and Research (www.IJSDR.org), ISSN:2455-2631, Vol.9, Issue 5, page no.379 - 381, May-2024, Available :https://ijsdr.org/papers/IJSDR2405054.pdf

Volume 9 Issue 5, May-2024

Pages : 379 - 381

Paper Reg. ID: IJSDR_211211

Published Paper Id: IJSDR2405054

Downloads: 000347296

Research Area: Engineering

Country: SIVAGANGAI, TamilNadu, India

Published Paper PDF: https://ijsdr.org/papers/IJSDR2405054

Published Paper URL: https://ijsdr.org/viewpaperforall?paper=IJSDR2405054

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

This work is licensed under a Creative Commons Attribution 4.0 International License and The Open Definition