  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.
