Early Prediction of Sepsis Using Random Forest Classification for Imbalanced Clinical Data

The early prediction of sepsis in intensive care units using clinical data is the objective of the PhysioNet/Computing in Cardiology Challenge 2019. In this paper, a machine learning approach is presented which uses an optimized Random Forest for prediction of a septic condition. After an initial data augmentation step, a customized learning process is performed for the trees to consider imbalance in the dataset. Finally, a feature reduction is implemented and the forest is trimmed to 50 trees for an optimal classification in terms of run time and accuracy. Using a 10-fold cross-validation on the complete training dataset, a mean utility score of 0.376 is achieved. In the final submission, a normalized observed utility score of 0.296 on the full test set is achieved. Our team name is The Septic Think Tank (final rank: 21).