Boosting-Based Prediction of Chronic Kidney Disease Using Clinical Parameters
DOI:
https://doi.org/10.48047/z5p51088Keywords:
ML, DL, CKDAbstract
Chronic Kidney Disease (CKD) is a progressive condition that can lead to severe health complications if not diagnosed early. Accurate and timely prediction of CKD is crucial for effective treatment and management. This study explores the application of boosting techniques, such as Adaptive Boosting (AdaBoost), Gradient Boosting, and Extreme Gradient Boosting (XGBoost), to enhance the predictive accuracy of CKD using clinical parameters.
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References
Salman, R., & Gupta, S. (2023). Hybrid machine learning model for chronic disease prediction. International Journal of Intelligent Systems and Applications in Engineering (IJISAE), 11(2), 808–816.
Lee, C., Jo, B., Woo, H., et al. (2022). Chronic disease prediction using the common data model: Development study. JMIR AI, 1(1), e41030.
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