To analyze main influencing factors of hospitalization expenses by support vector machine modeling, and explore effective influence factors analysis methods of medical expenses.

Random selection of six hospitals in Zhejiang province. Using hospital electronic medical record system of the hospitals and selecting three kinds of typical diseases of internal medicine and surgery, to build the support vector machine model, BP neural network model, and multiple linear regression model for comparison of analysis results. The SVM model is used to analyze three various diseases.

The support vector machine model based on radial basis kernel function scored the highest prediction accuracy on the hospitalization expenses, up to 96.07%. In a mixed analysis of different diseases, analysis results of all three models pointed the main influence factors of hospitalization expense as days of stay, disease types, and hospital coding for the surgery. In the analysis by diseases individually, the influencing factors, though varying with diseases, key factors remain the same.

The support vector machine in the influence factor analysis is feasible in hospitalization expenses. According to the analysis results, the single disease payment system can be made rationally, which can effectively control excessive growth of medical expenses.

Hospitalization expense; Support vector machine; Influencing factor; Neural network; Multivariate linear regression