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Journal of Multidisciplinary Healthcare Volume 17, 2024 - Issue
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ORIGINAL RESEARCH

A Machine Learning Method for Differentiation Crohn’s Disease and Intestinal Tuberculosis

Yufeng Shu1 Department of Gastroenterology, Third Xiangya Hospital, Central South University., Changsha, Hunan, People’s Republic of ChinaView further author information
,
Zhe Chen2 Department of Gerontology, The Affiliated Changsha Central Hospital, Hengyang Medical School, University of South China., Changsha, Hunan, People’s Republic of ChinaView further author information
,
Jingshu Chi1 Department of Gastroenterology, Third Xiangya Hospital, Central South University., Changsha, Hunan, People’s Republic of ChinaView further author information
,
Sha Cheng1 Department of Gastroenterology, Third Xiangya Hospital, Central South University., Changsha, Hunan, People’s Republic of ChinaView further author information
,
Huan Li1 Department of Gastroenterology, Third Xiangya Hospital, Central South University., Changsha, Hunan, People’s Republic of ChinaORCID Iconhttps://orcid.org/0000-0001-8568-3151View further author information
ORCID Icon,
Peng Liu3 Department of Gastroenterology, The Affiliated Changsha Central Hospital, Hengyang Medical School, University of South China., Changsha, Hunan, People’s Republic of ChinaCorrespondence[email protected]
View further author information
&
Ju Luo2 Department of Gerontology, The Affiliated Changsha Central Hospital, Hengyang Medical School, University of South China., Changsha, Hunan, People’s Republic of ChinaCorrespondence[email protected]
ORCID Iconhttps://orcid.org/0000-0002-5595-9513View further author information
ORCID Icon show all
Pages 3835-3847 | Received 27 May 2024, Accepted 29 Jul 2024, Published online: 07 Aug 2024

Figures & data

Figure 1 Study flow chart.

Figure 1 Study flow chart.

Table 1 Descriptive Statistics for Machine Learning

Table 2 Performance of All 6 ML Classification Methods

Figure 2 The average AUROC of the XGBoost method after 5-fold cross validation.

Figure 2 The average AUROC of the XGBoost method after 5-fold cross validation.

Figure 3 SHAP method to explain our ML models.

Figure 3 SHAP method to explain our ML models.

Figure 4 LIME method to explain our ML models.

Figure 4 LIME method to explain our ML models.

Table 3 Results of the ML Model and MDT in Clinical Practice

Figure 5 Results of the ML model and MDT in clinical practice.

Figure 5 Results of the ML model and MDT in clinical practice.

Figure 6 Confusion matrix used for calculating the kappa coefficient.

Figure 6 Confusion matrix used for calculating the kappa coefficient.

Data Sharing Statement

All relevant ML methods and codes can be freely accessed at https://github.com/philiplaw1984/IBD/.

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