Empirical Analysis of Machine Learning Configurations for Prediction of Multiple Organ Failure in Trauma Patients
This work addresses early detection of a life-threatening condition for clinicians, but it is incremental as it focuses on empirical analysis of existing methods.
The study analyzed machine learning configurations for predicting multiple organ failure in trauma patients, finding that classifier choice had the greatest impact on performance, with complex classifiers offering better results but also higher variability.
Multiple organ failure (MOF) is a life-threatening condition. Due to its urgency and high mortality rate, early detection is critical for clinicians to provide appropriate treatment. In this paper, we perform quantitative analysis on early MOF prediction with comprehensive machine learning (ML) configurations, including data preprocessing (missing value treatment, label balancing, feature scaling), feature selection, classifier choice, and hyperparameter tuning. Results show that classifier choice impacts both the performance improvement and variation most among all the configurations. In general, complex classifiers including ensemble methods can provide better performance than simple classifiers. However, blindly pursuing complex classifiers is unwise as it also brings the risk of greater performance variation.