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

摘要

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