Machine learning to predict etiology for infectious diseases of classic fever of unknown origin in adults
NOPR - NISCAIR Online Periodicals Repository
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Title |
Machine learning to predict etiology for infectious diseases of classic fever of unknown origin in adults
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Creator |
Zhou, Yani
Chen, Cha Ruan, Bing Wang, Weihong |
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Subject |
C-reactive protein (CRP)
Extreme gradient boosting (XGBoost) Light gradients boosting (Light GBM) Random forest (RF) SHAP |
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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 |
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Date |
2023-06-28T10:31:10Z
2023-06-28T10:31:10Z 2023-07 |
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Type |
Article
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Identifier |
0975-1009 (Online); 0019-5189 (Print)
http://nopr.niscpr.res.in/handle/123456789/62147 https://doi.org/10.56042/ijeb.v61i07.2826 |
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Language |
en
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Publisher |
NIScPR-CSIR, India
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Source |
IJEB Vol.61(07) [July 2023]
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