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

arXiv: https://arxiv.org/abs/2103.10929 · HTML: https://mlsanity.com/paper/2103.10929/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 0 of its 31 references that are in the map.
- First posted on arXiv 19 Mar 2021.

## Built on by (most cited first, 1 of 1)

- [FairEHR-CLP: Towards Fairness-Aware Clinical Predictions with Contrastive Learning in Multimodal Electronic Health Records](https://mlsanity.com/paper/2402.00955/) (2024)

## Abstract (arXiv)

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.