A Novel Approach for Labelling Health Insurance Data for Cardiovascular Disease Risk Classification

Abstract

Health insurance companies own very large databases built from the history of clinical exams and/or hospital procedures undergone by their beneficiaries. An important challenge faced by these companies is then to mine useful information from those database for the purpose of preventive care and financial costs reduction. Bearing this in mind, in this paper we propose a novel approach for building and labelling feature vectors for the beneficiaries of health insurance companies with the aim of building classifiers capable of predicting the risk level (high or low) of a given beneficiary to undergo serious cardiovascular events within a predefined horizon in the near future. The proposed approach was evaluated in the design of neural network classifiers using real-world health data from a Brazilian insurance company. The obtained results show that the proposed method is rather promising and can be used to aid the management of health insurance plans.