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Machine learning to predict etiology for infectious diseases of classic fever of unknown origin in adults

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Title Machine learning to predict etiology for infectious diseases of classic fever of unknown origin in adults
 
Creator Zhou, Yani
Chen, Cha
Ruan, Bing
Wang, Weihong
 
Subject C-reactive protein (CRP)
Extreme gradient boosting (XGBoost)
Light gradients boosting (Light GBM)
Random forest (RF)
SHAP
 
Description 485-492
The etiologies of infectious diseases (IDs) of classic fever of unknown origin (FUO) are multitudinous. Different
etiologies affect medication decisions. Here, we have made an attempt to predict the types of etiology on the basis of a
machine learning (ML) model for IDs of classic FUO for adults. Ten years clinical data of 408 classic FUO were
retrospectively collected from August 2012 to August 2022 in Huzhou Central Hospital. A total of 256 adult patients with
ID of classic FUO were divided into four subgroups for clinical characteristic analysis. Random forest (RF), light gradients
boosting (Light GBM), and extreme gradient boosting (XGBoost) were used to construct prediction models of 10-fold crossvalidation.
The micro average and weighted average of F1 score were calculated to evaluate the performance of the models.
SHapley Additive exPlanations (SHAP) was used to explain the relationship between features and the predicted results.
Clinical characteristic analysis showed that 25 indices were statistically different (P
 
Date 2023-06-28T10:31:10Z
2023-06-28T10:31:10Z
2023-07
 
Type Article
 
Identifier 0975-1009 (Online); 0019-5189 (Print)
http://nopr.niscpr.res.in/handle/123456789/62147
https://doi.org/10.56042/ijeb.v61i07.2826
 
Language en
 
Publisher NIScPR-CSIR, India
 
Source IJEB Vol.61(07) [July 2023]