# CopulaSMOTE: A Copula-Based Oversampling Approach for Imbalanced Classification in Diabetes Prediction

arXiv: https://arxiv.org/abs/2506.17326 · HTML: https://mlsanity.com/paper/2506.17326/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 15 of its 87 references that are in the map.
- First posted on arXiv 18 Jun 2025.

## Builds on (most cited first, 15 of 15)

- [XGBoost: A Scalable Tree Boosting System](https://mlsanity.com/paper/1603.02754/) (2016)
- [Modeling Tabular data using Conditional GAN](https://mlsanity.com/paper/1907.00503/) (2019)
- [NIPS 2016 Tutorial: Generative Adversarial Networks](https://mlsanity.com/paper/1701.00160/) (2016)
- [GAN-based Synthetic Medical Image Augmentation for increased CNN Performance in Liver Lesion Classification](https://mlsanity.com/paper/1803.01229/) (2018)
- [Copula-based transferable models for synthetic population generation](https://mlsanity.com/paper/2302.09193/) (2023)
- [Generation and Simulation of Synthetic Datasets with Copulas](https://mlsanity.com/paper/2203.17250/) (2022)
- [Copula-based synthetic data augmentation for machine-learning emulators](https://mlsanity.com/paper/2012.09037/) (2020)
- [A generic approach to nonparametric function estimation with mixed data](https://mlsanity.com/paper/1704.07457/) (2017)
- [D-vine quantile regression with discrete variables](https://mlsanity.com/paper/1705.08310/) (2017)
- [SynC: A Unified Framework for Generating Synthetic Population with Gaussian Copula](https://mlsanity.com/paper/1904.07998/) (2019)
- [A new Gini correlation between quantitative and qualitative variables](https://mlsanity.com/paper/1809.09793/) (2018)
- [Jackknife empirical likelihood confidence intervals for the categorical Gini correlation](https://mlsanity.com/paper/2310.09646/) (2023)
- [Predicting Diabetes Using Machine Learning: A Comparative Study of Classifiers](https://mlsanity.com/paper/2505.07036/) (2025)
- [Deep Copula Classifier: Theory, Consistency, and Empirical Evaluation](https://mlsanity.com/paper/2505.22997/) (2025)
- [A2 Copula-Driven Spatial Bayesian Neural Network For Modeling Non-Gaussian Dependence: A Simulation Study](https://mlsanity.com/paper/2505.24006/) (2025)

## Built on by (most cited first, 2 of 2)

- [Comparing Two Categorical Gini Correlations with Applications to Classification Problems](https://mlsanity.com/paper/2605.17763/) (2026)
- [K-IPO: Kendall-constrained Importance Preserving Oversampling for Imbalanced Tabular Data](https://mlsanity.com/paper/2607.16478/) (2026)

## Abstract (arXiv)

Class imbalance remains a practical obstacle in the development of clinical prediction models for conditions such as diabetes mellitus, where the number of confirmed cases is often much smaller than the number of controls. The Synthetic Minority Over-sampling Technique (SMOTE) and its variants are widely used to address this imbalance, but they generate synthetic observations through local interpolation in feature space and do not explicitly model the joint dependence structure of the minority class. To address this challenge, our study introduces a copula-based data augmentation approach that estimates the minority-class dependence structure when generating synthetic samples and integrates with standard machine learning techniques. Specifically, we employ truncated vine copulas to represent multivariate dependence through a sequence of bivariate building blocks. We evaluate the proposed approach on three public diabetes datasets, namely the Pima Indians Diabetes dataset, the Iraqi Diabetes dataset, and the CDC BRFSS 2015 Diabetes Health Indicators dataset, which together cover a range of sample sizes, dimensionalities, and imbalance regimes. For each dataset, five resampling strategies are compared across five classifiers using a 5 by 2 cross validation protocol with Dietterich's paired t test. Our findings suggest that CopulaSMOTE can improve minority-class recovery in larger tabular diabetes datasets, particularly the CDC BRFSS dataset, but its advantages depend on the classifier and evaluation metric.