Artificial intelligence-based multiclass diabetes risk stratification for big data embedded with explainability: From machine learning to attention models

Abstract

Background

Globally, diabetes mellitus is a major health challenge with high morbidity and significant costs. Traditional methods rely on invasive biomarkers like glycated hemoglobin and lack consistency, necessitating more robust approaches.

Methodology

This study uses attention-based deep learning for enhanced diabetes risk stratification. We focus on exploring recurrent neural networks with attention mechanisms. We used K-fold (K = 5) cross-validation and implemented 14 models for robustness. Further, we integrate an explainability paradigm by validating model outputs through reliability-focused statistical tests. Finally, we present the training time comparison between different hardware.

Results

The attention-based models employed demonstrated superior performance in handling multi-dimensional data, resulting in highly accurate diabetes risk stratification predictions. We went on to evaluate these models and benchmarked them against classical methods, proving significant improvements over traditional ones with metrics such as the area under the curve scores reaching 0.99 for attention models. The percentage improvement over non attention-based models was 3.67%. Also, the models were able to show generalization at 60% of training data.

Conclusion

The attention-based models employed in this study substantially enhance diabetes risk stratification, offering a promising tool for healthcare professionals. They allow for early and precise detection of diabetes risk stratification, thereby potentially improving patient outcomes through timely and tailored interventions. This research underscores the potential of sophisticated deep learning models in transforming the landscape of chronic disease management.