Forecasting Clinical Expenditure of Child Patients Using Binary and Multi-Classification Methods

In this paper, random forest algorithm (RF) and error-correction output code model (ECOC) were employed to predict the clinic expenditure of child patients with data consisting of records extracted from a hospital information system. Throughout the modelling, the training set utilized 80% of the records selected from original data set in random and the rest of data were used in the test set. The RF received better predictive accuracy than ECOC, with RMSE being 0.152, R ² being 0.924, |R| being 0.869, and Acc ±1 being 82.6%. Additionally, RF obtained good performances on different types of charges, achieving over 80% accuracy in average. Besides, among different types of information, clinic features gave better results, with RMSE being 0.215, R ² being 0.844, |R| being 0.709 and Acc being 60.5%. In comparison, the random forest generally performed better than ECOC models in most fields. To summarize, the random forest could obtain best accuracy on charge7 (Treatment Fee), with accuracy of 90.5%, and clinic features could provide models with higher accuracy among all fields of information.