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.

为了解决机器学习在早期透析预测中的挑战，本研究评估了不同的数据增强技术在现实世界数据集上的有效性，并提出了一种名为 Binary Gaussian Copula Synthesis（BGCS）的新方法，该方法通过生成与原始数据分布相似的少数类数据来提高早期透析预测的准确性。此外，我们还设计了一种基于机器学习的临床决策支持系统（CDSS），以帮助临床医生做出明智的决策并有效制定CKD患者的治疗计划。

Binary Gaussian Copula Synthesis: A Novel Data Augmentation Technique to
  Advance ML-based Clinical Decision Support Systems for Early Prediction of
  Dialysis Among CKD Patients

二元高斯库普拉合成：用于提升基于机器学习的临床决策支持系统的新型数据增强技术，早期预测CKD患者透析的方法