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

2021/03/19 by Yuqing Wang, Yun Zhao, Wang, Yuqing +5
Computer Science · Health Professions · Medicine · #Artificial Intelligence (cs.AI) #Artificial Intelligence in Healthcare #Artificial Intelligence in Healthcare and Education #FOS: Computer and information sciences #Machine Learning (cs.LG) #Machine Learning in Healthcare

paper · pdf · doi:10.48550/arxiv.2103.10929

openalex publication_date 2021/03/19 · openalex created_date 2021/03/29 · openalex updated_date 2026/07/28

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.

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