  The Center for Disease Control estimates that over 37 million US adults
suffer from chronic kidney disease (CKD), yet 9 out of 10 of these individuals
are unaware of their condition due to the absence of symptoms in the early
stages. It has a significant impact on patients' quality of life, particularly
when it progresses to the need for dialysis. Early prediction of dialysis is
crucial as it can significantly improve patient outcomes and assist healthcare
providers in making timely and informed decisions. However, developing an
effective machine learning (ML)-based Clinical Decision Support System (CDSS)
for early dialysis prediction poses a key challenge due to the imbalanced
nature of data. To address this challenge, this study evaluates various data
augmentation techniques to understand their effectiveness on real-world
datasets. We propose a new approach named Binary Gaussian Copula Synthesis
(BGCS). BGCS is tailored for binary medical datasets and excels in generating
synthetic minority data that mirrors the distribution of the original data.
BGCS enhances early dialysis prediction by outperforming traditional methods in
detecting dialysis patients. For the best ML model, Random Forest, BCGS
achieved a 72% improvement, surpassing the state-of-the-art augmentation
approaches. Also, we present a ML-based CDSS, designed to aid clinicians in
making informed decisions. CDSS, which utilizes decision tree models, is
developed to improve patient outcomes, identify critical variables, and thereby
enable clinicians to make proactive decisions, and strategize treatment plans
effectively for CKD patients who are more likely to require dialysis in the
near future. Through comprehensive feature analysis and meticulous data
preparation, we ensure that the CDSS's dialysis predictions are not only
accurate but also actionable, providing a valuable tool in the management and
treatment of CKD.
