Using deep learning time series forecasting to predict dropout in childhood obesity treatment

Abstract: The author investigates the performance of a time series based approach in predicting the risk of patients abandoning treatment in a treatment program for childhood obesity. The time series based approach is compared and contrasted to an approach based on static features (which has been applied in similar problems). Four machine learning models are constructed; one ‘Main model’ using both time series forecasting and three ‘reference models’ created by removing or exchanging parts of the main model to test the performance of using only time series forecasting or only static features in the prediction. The main model achieves an ROC-AUC of 0.77 on the data set. ANOVA testing is used to determine whether the four models perform differently. A difference cannot be verified at the significance level of 0.05, and thus, the author concludes that the project cannot show either an advantage or a disadvantage to employing a time series based approach over static features in this problem.