An Interpretable Artificial Intelligence Model for Chronic Kidney Disease Diagnosis Using Decision Trees
Abstract
Chronic Kidney Disease (CKD) is a progressive condition that, without early intervention, can lead to serious health complications and increased mortality. Traditional diagnostic methods rely on extensive laboratory tests, which can be time-consuming and inaccessible in resource-limited settings. In this study, we investigate the application of a machine learning model based on Decision Trees (DT) enhanced with Feature Selection (FS) for diagnosing CKD using publicly available data. The CKD dataset, obtained from the UCI Machine Learning Repository, consists of medical and demographic attributes. Using the SelectKBest method, the top ten most relevant features were identified for training the DT model. Cross-validation was applied to assess the model's robustness, achieving an average accuracy of 97.50% across folds and 98.75% on the independent test set. These results demonstrate that the DT model with FS offers a promising diagnostic tool for CKD, providing high accuracy with interpretable results that are clinically relevant. This approach holds potential to assist healthcare professionals in early CKD diagnosis, enabling timely interventions and resource optimization.
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