Training Prediction Models
An ensemble approach was employed in order to create a more stable and reliable model resistant to outliers. Four different types of algorithms were used: deep neural network, logistic regression, adaboost, and random forest. We employed over-sampling (SVM Smote) and under-sampling (Repeated Edited Nearest Neighbors) of the training data in order to better balance the primary outcome within the dataset. For each type of algorithm, 100 different models were trained using varying degrees of data re-sampling to produce variability in each model’s underlying training data. The resulting 400 algorithms were subsequently combined into the final ensemble prognostic model.