Abstract:

[Objective] Nowadays, how to reduce repeated submissions caused by irregular reimbursements for financial reimbursement approval to improve the efficiency of financial reimbursement becomes a big issue in daily scientific research management of CAS institutes. This paper studies the use of prediction results to improve the efficiency of reimbursement approval. [Methods] This paper conducts a business model for financial reimbursement approval, obtains desensitized data for reimbursement approval that can be machine-understood, constructs variable characteristics and labels according to actual business characteristics, and then uses a random forest approach to analyze the importance of reconstructed variables. Decision tree, random forest, gradient random tree, and XGBoost algorithms are used to predict the reimbursement approval results. [Results] The importance analysis by constructing random forest makes the reconstruction variables more credible, provides reliable support for the approval results predicted by the subsequent four tree-model algorithms, and evaluates the best model from the results. [Conclusions] Based on the tree model, this paper identifies the key factors that affect the results of reimbursement approval and applies the machine learning algorithms to predict financial reimbursement approvals, which provides an application basis for tree models in predicting reimbursement approval.

Key words: variable reconstruction, data mining, tree model, prediction of approval