XGBoost Prediction of Infection of Leukemia Patients with Fever of Unknown Origin

Abstract

Discovering the source of a patient's fever without clinically localised signs can be a daunting task for doctors. In particular for leukaemia patients with fever of unknown origin, fast discovering the source of the fever is a formidable challenge, as this population has the potential to lead to fever in many different situations. In this paper, we applied XGBoost algorithm to predict the pathogenic infections from a big data repository of leukemia patients with fever of unknown origin (FUO) and compared the performance with other machine learning algorithms. Our results illustrates that those machine learning algorithms achieves good performance. In particular, the XGBoost obtains the best performance with an area under receiving-operating-characteristics curve (AUC) of 0.8376 and F1-score of 0.7034. Compared with existing literature, our experiment provides new insights for doctors to determine the cause of fever in leukemia patients.