Abstract
The high prevalence of chronic kidney disease (CKD) is a significant public health concern
globally. The condition has a high mortality rate, especially in developing countries. CKD often
go undetected since there are no obvious early-stage symptoms. Meanwhile, early detection and
on-time clinical intervention are necessary to reduce the disease progression. Machine learning
(ML) models can provide an efficient and cost-effective computer-aided diagnosis to assist clinicians
in achieving early CKD detection. This research proposed an approach to effectively detect CKD
by combining the information-gain-based feature selection technique and a cost-sensitive adaptive
boosting (AdaBoost) classifier. An approach like this could save CKD screening time and cost since
only a few clinical test attributes would be needed for the diagnosis. The proposed approach was
benchmarked against recently proposed CKD prediction methods and well-known classifiers. Among
these classifiers, the proposed cost-sensitive AdaBoost trained with the reduced feature set achieved
the best classification performance with an accuracy, sensitivity, and specificity of 99.8%, 100%, and
99.8%, respectively. Additionally, the experimental results show that the feature selection positively
impacted the performance of the various classifiers. The proposed approach has produced an effective
predictive model for CKD diagnosis and could be applied to more imbalanced medical datasets for
effective disease detection