A Copula Based Supervised Filter for Feature Selection in Diabetes Risk Prediction Using Machine Learning

TL;DR

This paper introduces a novel copula-based supervised filter for feature selection that emphasizes extreme value associations, improving interpretability and efficiency in diabetes risk prediction models.

Contribution

It proposes a new tail-focused feature ranking method using a Gumbel-copula implied concordance score, outperforming or matching standard methods in large-scale and clinical datasets.

Findings

Reduces features by approximately 52% on CDC dataset

Achieves the highest ROC-AUC on PIMA dataset

Provides a fast, interpretable feature screening method

Abstract

Effective feature selection is critical for robust and interpretable predictive modeling in medicine, especially when risk factors matter most in extreme patient strata. Many standard selectors emphasize average associations and can miss predictors whose relevance is concentrated in the distribution tails. We propose a computationally efficient supervised filter based on a Gumbel-copula implied upper-tail concordance score (lambda U), defined as a monotone transformation of Kendall's tau, to rank features by their tendency to be simultaneously extreme with the positive class. We compare against four common baselines (Mutual Information, mRMR, ReliefF, and L1/Elastic-Net) across four classifiers on two diabetes datasets: a large-scale public health survey (CDC, N=253,680) and a clinical benchmark (PIMA, N=768). Analyses include statistical testing, permutation importance, and robustness checks. On CDC, the proposed selector is the fastest and reduces 21 features to 10 (approx 52%). This yields a small but statistically significant trade-off relative to using all features, while performing better than standard filters (Mutual Information, mRMR) and comparably to the strong ReliefF baseline. On PIMA (8 predictors), the resulting ranking attains the highest ROC-AUC numerically, though paired DeLong tests show no significant differences versus strong baselines; PIMA therefore serves as a ranking-only sanity check in a low-dimensional setting. Across both datasets, the lambda U-based selector highlights clinically coherent predictors and provides an efficient, interpretable screening step that can complement standard feature-selection methods in public health and clinical risk prediction.