Prediction of days in hospital for children using random forest

In this study, a method was developed to predict the number of hospitalization days of infant patients. The random forest algorithm, along with a data set consisted by records extracted from a hospital information system, was utilized to develop a model to predict the days in hospital. When half of randomly selected records was used as training set to train the random forest algorithm and the other half was used as testing set to test the trained model, the random forest method achieved good predictive accuracy with RMSE being 0.314, R 2 being 0.706, |R| being 0.545, and Acc±1 being 71%, which is better than the results obtained by Adaboost method and Bagging method. Experiment on three subgroups of records: a group with all data, a group with records having less than or equal to 14 days in hospital, and a group with records having greater than 14 days in hospital, shows that the prediction of the developed method on the group having more than 14 days in hospital was better than predictions on other groups. Analysis to the importance of three different types of feature sets to the accuracy of prediction reveals that the feature set relating to personal information contribute more to the prediction than other types of features.