فهرست نشریات علمی دسترسی آزاد ایران

The escalating public health costs are a significant concern for governments globally. The efficient management of those costs is critical, with health insurance systems playing a pivotal role. However, the insurance industry faces challenges due to the heterogeneous data, leading to inconsistent outputs for identical inputs. Traditional predictive methods such as Artificial Neural Networks and Adaptive Neuro-Fuzzy Inference Systems (ANFIS) often fail to address these inconsistencies. This study proposes a novel two-stage model to determine insurance premiums, incorporating equity considerations and advanced computational techniques. We advocate for an expenditure-based premium calculation as a superior alternative to the traditional salary-based approach. This method aligns premiums more closely with household expenses, promoting fairness and efficiency. Our results demonstrate that the expenditure-based strategy outperforms the salary-based one in controlling costs for both the insured and the insurer. Specifically, the error metrics, including Mean Absolute Error and Root Mean Square Error, show significant improvement in our model compared to the ANFIS method. To enhance the model's accuracy, we integrate sampling techniques to mitigate the data heterogeneity and employ genetic algorithms to optimize the weights of the neural network. The genetic algorithm iteratively evolves the network parameters, ensuring robust performance even in diverse data. Our findings indicate that this integrated approach significantly reduces prediction errors and enhances the overall reliability of the premium calculation process. In conclusion, the proposed model offers a robust framework for premium determination, addressing the inherent data heterogeneity in the insurance industry. This study provides a valuable contribution to the field by demonstrating a practical and effective solution for improving the accuracy and fairness of insurance premium calculations.