A Hybrid Bayesian, Deterministic and Uncertainty Framework for Type 2 Diabetes Risk Assessment and Decision Support

Abstract

The early and accurate diagnosis of Type 2 Diabetes (T2D) is crucial for effective management and prevention of complications. This study explores a novel approach to T2D diagnosis by integrating Bayesian, uncertain, and deterministic strategies. The proposed methodology leverages Bayesian inference to handle uncertainty in patient data, while deterministic models provide clear decision rules based on clinical parameters. By combining these strategies, the model aims to improve diagnostic accuracy, reduce uncertainty in predictions, and offer robust support for clinical decision-making. This hybrid approach addresses the challenges of incomplete or noisy data, providing a more reliable framework for the timely identification of T2D in diverse patient populations. The evaluation of this hybrid model is based on more scenarios and comparisons. This evaluation proves that our hybrid model is more efficient than any uncertain, Bayesian, and deterministic model.