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

TL;DR

This paper introduces Binary Gaussian Copula Synthesis (BGCS), a novel data augmentation method for binary medical datasets, significantly improving early dialysis prediction in CKD patients and enhancing ML-based clinical decision support systems.

Contribution

The study presents BGCS, a new synthetic data generation technique tailored for binary datasets, which outperforms existing augmentation methods in predicting dialysis needs among CKD patients.

Findings

BGCS improves prediction accuracy by 72% over traditional methods.

Synthetic data generated by BGCS enhances ML model performance.

The developed CDSS aids clinicians in early intervention decisions.

Abstract

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.