Identification of the important variables for prediction of individual medical costs billed by health insurance

Abstract

The cost of health care insurance is one of the most important factors in the health care development. To establish a better health care system, there is a need to estimate the cost of health insurance. The prediction of the cost is one possibility to improve health care development. There is a need for more advanced methods other than traditional regression approaches, because the prediction of the health insurance costs are now a big data problem. To simplify the prediction process in this study, a selection procedure was performed to identify the most important factors for the prediction of the health care insurance costs. Artificial neural network, namely adaptive neuro fuzzy inference system (ANFIS), was used for the identification procedure. ANFIS architecture was employed to model nonlinear relationships between data samples. Five input factors were considered in the analyzing (age of primary beneficiary, insurance contractor gender, Body mass index, Number of children covered by health insurance, and smoking). The obtained results showed that smoking has the highest impact on the cost of health insurance. Moreover, prediction accuracy is acceptable and could be used for future management of health care development.