Predictive Analytics For Healthcare Insurance Risk Assessment Using Ensemble Learning Models

Abstract

Accurate healthcare insurance risk assessment is essential in designing costeffective and personalized insurance plans. The study proposes the Dynamic Ensemble Risk Stratification Algorithm (DERISA), a new approach using advanced ensemble learning techniques for predictive analytics in healthcare insurance. With Random Forest, Gradient Boosting Machine (GBM), and XGBoost models integrated within a dynamically weighted ensemble framework, DERISA predicts insurance risks with high precision. Feature engineering techniques such as PCA and mutual information are followed to extract and optimize relevant features from the dataset, including historical claims, demographic attributes, and medical histories. Experimental evaluation on real data for healthcare insurance reveals that the efficacy of DERISA is better than traditional machine learning. Accuracy is 95.2%, precision is 94.1%, recall is 93.8%, and AUC-ROC scores 0.97 is better than individual ensemble models. The algorithm stratifies policyholders into well-defined risk tiers, accurately capturing high-risk profiles with higher precision that can help in providing premium premium plans and preventive health incentives.

Keywords: Healthcare Insurance Risk Assessment, Ensemble Learning, Dynamic Risk Stratification, Predictive Analytics, Feature Engineering