Semi-analytical treatment of fuzzy risk diabetes model in Oman

Abstract

Diabetes mellitus is a growing public health concern in Oman, with rising prevalence linked to lifestyle changes and genetic factors. Effective risk prediction models are essential for early intervention and management. In the interim, the semi-analytical approaches may offer simpler solutions without requiring large-scale numerical calculations or linearization and discretization techniques which could be difficult to use with fuzzy differential equation models. This study proposes a semi-analytical approach to develop a robust diabetes risk model tailored to the Omani population. By integrating clinical, demographic, and biochemical variables, the model employs a combination of analytical techniques to enhance predictive accuracy while maintaining interpretability. Additionally, solving the fuzzy differential equations, a semi-analytical method is employed, combining analytical techniques with numerical approximations to handle the inherent fuzziness in the system. This approach ensures robust and interpretable solutions while accounting for imprecise data common in medical studies. The model is validated using real-world clinical data from Oman, demonstrating its effectiveness in predicting diabetes risk trends under different scenarios. The proposed solution offers a scalable tool for policymakers and healthcare providers to implement targeted prevention strategies, potentially reducing the diabetes burden in Oman. The results highlight the flexibility of the FDE-based model in addressing uncertainty, providing healthcare policymakers with a valuable tool for early intervention strategies.