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. 2024 Jun 14;14(1):13715.
doi: 10.1038/s41598-024-64734-w.

Risk prediction of cholangitis after stent implantation based on machine learning

Rui Zhao  1 , Lin Gu  1 , Xiquan Ke  1 , Xiaojing Deng  1 , Dapeng Li  1 , Zhenzeng Ma  1 , Qizhi Wang  1 , Hailun Zheng  2 , Yong Yang  3
Affiliations

Risk prediction of cholangitis after stent implantation based on machine learning

Rui Zhao et al. Sci Rep. .

Abstract

The risk of cholangitis after ERCP implantation in malignant obstructive jaundice patients remains unknown. To develop models based on artificial intelligence methods to predict cholangitis risk more accurately, according to patients after stent implantation in patients' MOJ clinical data. This retrospective study included 218 patients with MOJ undergoing ERCP surgery. A total of 27 clinical variables were collected as input variables. Seven models (including univariate analysis and six machine learning models) were trained and tested for classified prediction. The model' performance was measured by AUROC. The RFT model demonstrated excellent performances with accuracies up to 0.86 and AUROC up to 0.87. Feature selection in RF and SHAP was similar, and the choice of the best variable subset produced a high performance with an AUROC up to 0.89. We have developed a hybrid machine learning model with better predictive performance than traditional LR prediction models, as well as other machine learning models for cholangitis based on simple clinical data. The model can assist doctors in clinical diagnosis, adopt reasonable treatment plans, and improve the survival rate of patients.

Keywords: Cholangitis; Endoscopic retrograde cholangiopancreatography; Machine learning model; Malignant obstructive jaundice.

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Conflict of interest statement

The authors declare no competing interests.

Figures

Figure 1
Figure 1
Correlation between features.
Figure 2
Figure 2
(a) Comparison of performance of univariate statistical analysis in AUROC, (b) comparison of performance of each machine learning model in AUROC.
Figure 3
Figure 3
Variable importance of features in RFT model.
Figure 4
Figure 4
SHAP value on selected feature.
Figure 5
Figure 5
Comparison of performance of different selection features in AUROC.
See this image and copyright information in PMC

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