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Machine Learning for CKD Diagnosis by Preethi Kannan is a document available to read on EtoBox.

What is Machine Learning for CKD Diagnosis about?

This study explores the use of eight ensemble machine learning methods to diagnose and predict chronic kidney disease (CKD) using various medical test features. The methods include techniques like LightGBM, which achieved the highest accuracy of 99.75%, and employed advanced data handling techniques such as MICE imputation and Borderline-SMOTE for improved classifier performance. The findings indicate that the proposed approach significantly enhances CKD detection compared to existing methods.

Author

Preethi Kannan

Language

EN