Predicting Chronic Kidney Disease Stages Through Ensemble Machine Learning Techniques

Chronic Kidney Disease (CKD) stands as a substantive global health concern due to its considerable impact on morbidity and mortality rates worldwide. One of the primary challenges in managing CKD lies in its subtle onset, often remaining asymptomatic in its early stages. Among the array of machine learning algorithms, LightGBM (Light Gradient Boosting Machine) stands out for its efficiency and accuracy in handling large and complex datasets, coupled with its adeptness in managing imbalanced datasets, renders it particularly well-suited for CKD diagnosis and prediction tasks. In clinical practice, the staging of CKD commonly relies on the measurement of the glomerular filtration rate (GFR), a crucial kidney capacity measure. Machine learning models trained on datasets containing GFR measurements and other pertinent clinical variables can effectively categorize patients into distinct CKD stages. The reported 99.12% accuracy of the LightGBM model in diagnosing CKD patients indicates the way of machine learning methodologies in bolstering diagnostic precision and efficiency.