Please use this identifier to cite or link to this item: http://nopr.niscpr.res.in/handle/123456789/62220

metadata.dc.identifier.doi: https://doi.org/10.56042/ijeb.v61i07.2826Title: Machine learning to predict etiology for infectious diseases of classic fever of unknown origin in adultsAuthors: Zhou, Yani
Chen, Cha
Ruan, Bing
Wang, WeihongKeywords: C-reactive protein (CRP);Extreme gradient boosting (XGBoost);Light gradients boosting (Light GBM);Random forest (RF);SHAPIssue Date: Jun-2023Publisher: NIScPR-CSIR, IndiaAbstract: 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 Page(s): 485-492ISSN: 0975-1009 (Online); 0019-5189 (Print)Appears in Collections:IJEB Vol.61(07) [July 2023]

Items in NOPR are protected by copyright, with all rights reserved, unless otherwise indicated.