Prediction of Medical Insurance Costs: A SHAP-Enhanced Predictive Analysis for Transparency and Interpretability

The escalation of healthcare expenses has been markedly pronounced in the aftermath of the COVID-19 pandemic’s onset. Consequently, health insurance has ascended to prominence as a critical domain of investigation, with a series of ongoing endeavors underway to navigate this complex issue. The task of forecasting medical insurance expenditures is laden with challenges, attributed to the dynamic nature of healthcare, the intricacies and occasional disarray of data, unpredictable human behaviors, economic oscillations, unforeseen events, and prevailing privacy apprehensions. In the quest to surmount these obstacles, a predictive model has been unveiled, engineered to impart insights into the nuances of healthcare pricing structures. The examination of SHAP values facilitates the discernment of pivotal elements that steer cost prognostications, a process instrumental in augmenting transparency and enabling judicious choices for both insurance entities and the insured. This inquiry underscores the cardinal role of amalgamating predictive precision with an element of transparency within the realm of healthcare insurance.