Robust clinical marker identification for diabetic kidney disease with ensemble feature selection

Abstract

Objective: Diabetic kidney disease (DKD) is one of the most frequent complications in diabetes associated with substantial morbidity and mortality. To accelerate DKD risk factor discovery, we present an ensemble feature selection approach to identify a robust set of discriminant factors using electronic medical records (EMRs).

Material and methods: We identified a retrospective cohort of 15 645 adult patients with type 2 diabetes, excluding those with pre-existing kidney disease, and utilized all available clinical data types in modeling. We compared 3 machine-learning-based embedded feature selection methods in conjunction with 6 feature ensemble techniques for selecting top-ranked features in terms of robustness to data perturbations and predictability for DKD onset.

Results: The gradient boosting machine (GBM) with weighted mean rank feature ensemble technique achieved the best performance with an AUC of 0.82 [95%-CI, 0.81-0.83] on internal validation and 0.71 [95%-CI, 0.68-0.73] on external temporal validation. The ensemble model identified a set of 440 features from 84 872 unique clinical features that are both predicative of DKD onset and robust against data perturbations, including 191 labs, 51 visit details (mainly vital signs), 39 medications, 34 orders, 30 diagnoses, and 95 other clinical features.

Discussion: Many of the top-ranked features have not been included in the state-of-art DKD prediction models, but their relationships with kidney function have been suggested in existing literature.

Conclusion: Our ensemble feature selection framework provides an option for identifying a robust and parsimonious feature set unbiasedly from EMR data, which effectively aids in knowledge discovery for DKD risk factors.

Figure 1.

Study cohort inclusion and exclusion.

Figure 2.

Flowchart for the experimental design.

Figure 3.

AUC vs. number of selected features for different feature selection combos.

Figure 4.

Stability vs. number of selected features for different feature selection combos.

Figure 5.

Stability vs. AUC tradeoff for different feature selection combos.

Figure 6.

Top demographic, lab and vital features and their partial dependence effects on predicting DKD risk.