New Chronic Kidney Disease Study Findings Recently Were Reported by Researchers at Khalifa University (Investigation on explainable machine learning models to predict chronic kidney diseases).

A recent study conducted by researchers at Khalifa University focused on the use of explainable machine learning models to predict chronic kidney disease (CKD). CKD is a significant global health issue that often goes undetected in its early stages. The study collected clinical data from 491 patients and employed five machine learning methods to develop a predictive model. The XGBoost model achieved the best performance, with an accuracy of 93.29% and an area under the curve (AUC) of 0.9689. The study also utilized the SHAP and LIME algorithms to enhance the interpretability of the model and provide insights into individual predictions. The researchers concluded that their approach could aid in the early prediction of CKD and help clinicians better understand the rationale behind predicted outcomes. [Extracted from the article]