Health Insurance Cost Prediction Model Based on Big Data Analysis

This research seeks to create a model for predicting health insurance costs using big data analysis techniques, and methodically presents the entire procedure from gathering data, preparing it, engineering features, selecting models, and establishing them. Initially, samples from the US health insurance dataset were utilized for data cleaning and preprocessing to maintain data quality and consistency. Subsequently, through feature selection and dimensionality reduction techniques, key features highly correlated with medical expenses, such as age, gender, type of chronic disease, and historical medical expenses, were extracted. Based on these features, a deep learning model was used to predict health insurance expenses, and the prediction accuracy and stability were improved through model evaluation and optimization strategies. The research results show that the constructed prediction model has high accuracy and good generalization ability in practical applications. Through case analysis of actual data, the application effect of the model in personalized premium pricing, risk management and resource allocation is verified, which can help insurance companies more accurately predict medical expenses and provide decision support. This study provides powerful tools and methods for risk management and cost control in the health insurance industry, which has important practical significance.