Differentiation of intestinal tuberculosis and Crohn’s disease through an explainable machine learning method

Abstract

Differentiation between Crohn’s disease and intestinal tuberculosis is difficult but crucial for medical decisions. This study aims to develop an effective framework to distinguish these two diseases through an explainable machine learning (ML) model. After feature selection, a total of nine variables are extracted, including intestinal surgery, abdominal, bloody stool, PPD, knot, ESAT-6, CFP-10, intestinal dilatation and comb sign. Besides, we compared the predictive performance of the ML methods with traditional statistical methods. This work also provides insights into the ML model’s outcome through the SHAP method for the first time. A cohort consisting of 200 patients’ data (CD = 160, ITB = 40) is used in training and validating models. Results illustrate that the XGBoost algorithm outperforms other classifiers in terms of area under the receiver operating characteristic curve (AUC), sensitivity, specificity, precision and Matthews correlation coefficient (MCC), yielding values of 0.891, 0.813, 0.969, 0.867 and 0.801 respectively. More importantly, the prediction outcomes of XGBoost can be effectively explained through the SHAP method. The proposed framework proves that the effectiveness of distinguishing CD from ITB through interpretable machine learning, which can obtain a global explanation but also an explanation for individual patients.

Artificial intelligence for discrimination of Crohn's disease and gastrointestinal tuberculosis: A systematic review

“…1, PRISMA flow chart). 8,[11][12][13][14][15][16][17][18][19][20][21][22] Table 1 shows the details of the included studies while Table S2 includes details of the excluded studies with the reasons for exclusion.…”

“…They finally extracted nine variables to be used for diagnosis using multiple statistical methods, and machine learning methods were compared with an XGBoost algorithm. 18 A total of 160 patients with CD and 40 patients with TB were divided into training (60%) and validation (40%) cohorts with stratified random sampling. Here, they used statistical methods of linear discriminant analysis and logistic regression, whereas machine learning methods of artificial neural network, support vector machine with different kernel functions, Bayesian regression (Bayes), RF, and gradient boosting decision tree were compared with XGBoost.…”

Numerical solution for high-order ordinary differential equations using H-ELM algorithm

“…With the rapid development of computer hardware and various new theories, neural networks and other machine learning algorithms are gradually applied to various areas (Weng et al ., 2021, 2022). In fact, various machine learning algorithms such as least squares support vector machines (Lu et al.…”

“…With the rapid development of computer hardware and various new theories, neural networks and other machine learning algorithms are gradually applied to various areas (Weng et al, 2021(Weng et al, , 2022. In fact, various machine learning algorithms such as least squares support vector machines (Lu et al, 2019;Mehrkanoon et al, 2012;Mehrkanoon and Suykens, 2015), neural networks (Eskiizmirliler et al, 2021;Famelis and Kaloutsa, 2021;G€ unel and G€ or, 2021;Li and Wang, 2021;Schiassi et al, 2021) and deep learning (Wang et al, 2021;Weinan et al, 2017) have been utilized to solve these differential equations.…”

Predictive models for endoscopic disease activity in patients with ulcerative colitis: Practical machine learning-based modeling and interpretation

“…ML algorithms have the characteristics of continuously updating learning and capturing relationships among variables, which can be a good approach to solving the problems in UC disease activity prediction model building. Previous studies have demonstrated that ML models can provide better accuracy and discrimination for the diagnosis of inflammatory bowel disease (IBD), prediction of biologic treatment response in UC patients, and prognoses of patients with acute severe colitis (21)(22)(23)(24)(25). It creates opportunities for exploring the relationships among features and building highly efficient models.…”