Abstract
The early detection of Chronic Kidney Disease (CKD) using machine learning has attracted significant interest in healthcare-related computer science. Despite rapid advancements, many reported studies remain inconsistent and potentially misleading due to a lack of organized evaluation regarding methodological concerns. Key issues include data leakage, limited access to temporal patient records, and inconsistency in reported clinical indicators.
This research offers a systematic literature review of existing CKD prediction studies using interpretable machine learning techniques, selecting nineteen relevant studies through systematic searches across major academic databases. To assess methodological reliability, this study introduces a structured taxonomy of information leakage and a quantitative leakage scoring framework to systematically evaluate reliability across CKD prediction studies.
The analysis reveals a strong relationship between leakage and inflated performance, with high leakage studies reporting an average accuracy of 95.48% compared to 80.2% for leakage-free studies, reflecting an increase of approximately 15.28%. Furthermore, a cross-study feature stability analysis shows that only a small subset of predictors is consistently reproducible, with over 80% lacking reliability. Overall, the findings suggest that many reported performance improvements stem from methodological limitations rather than true predictive capability.
Blogger's Review: This article highlights the potential pitfalls of machine learning in CKD prediction, emphasizing the impact of data leakage on study outcomes. It underscores the importance of reliability assessments in medical research, urging future studies to prioritize data integrity and methodological rigor.