Empirical Analysis of Machine Learning Configurations for Prediction of Multiple Organ Failure in Trauma Patients

TL;DR

This study systematically evaluates various machine learning configurations for early prediction of multiple organ failure in trauma patients, highlighting the impact of classifier choice and the benefits of ensemble methods.

Contribution

It provides a comprehensive empirical analysis of ML configurations for MOF prediction, emphasizing the importance of classifier selection and hyperparameter tuning.

Findings

Classifier choice significantly affects performance and variability.

Ensemble methods generally outperform simple classifiers.

Complex classifiers can improve accuracy but increase performance variation.

Abstract

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.