Comparison and development of machine learning for thalidomide-induced peripheral neuropathy prediction of refractory Crohn’s disease in Chinese population

Figure 1 Flow chart display. Flow chart showing the process of model generation validation of the model. RF: Random forest; GBDT: Gradient boosting decision tree; ET: Extremely randomized trees; LR: Logistic regression; XGBoost: Extreme gradient boosting.

Figure 2 Information gain values of the features. The higher the information gain value, the more important the variable. Therefore, these five variables (rs1353248, dose, rs6265, rs2030324, rs11030104) are the optimal feature set.

Figure 3 The correlation between the optimal variables and thalidomide-induced peripheral neuropathy. Illustrate patients with interleukin-12 rs1353248_TT, brain-derived neurotrophic factor (BDNF) rs2030324_AG, BDNF rs6265_CT, and BDNF rs11030104_AG, genotypes are more likely to have thalidomide-induced peripheral neuropathy than non-carriers. A: IL-12; B-D: BDNF. ^aP < 0.01, ^bP < 0.001. BDNF: Brain-derived neurotrophic factor; IL: Interleukin.

Figure 4 Examination of consequences between the top four single nucleotide polymorphisms and gene expression in nerve tibial tissue. Patients with interleukin (IL)-12 rs1353248_TT (chr3_159905770, P = 8.52 × 10^-4), brain-derived neurotrophic factor (BDNF) rs6265_CT (chr11_27658369, P = 1.07 × 10^-4) BDNF rs2030324_AG (chr11_27705368, P = 9.2 × 10^-11), and BDNF rs11030104_AG (chr11_27662970, P = 2.76 × 10^-5). A: IL-12; B-D: BDNF. The expression levels of the BDNF gene were reduced in rs6265CT and rs11030104AG. Additionally, the expression levels of the IL-12 gene were significantly decreased in the rs1353248TT. BDNF: Brain-derived neurotrophic factor; IL: Interleukin.

Figure 5 Evaluation of the predictive models. Average area under the receiver operating characteristic curve and precision recall curve of the five models in the training set. A: Receiver operating characteristic curve (training set); B: Precision-recall curve (training set). Average area and 95% confidence intervals of different predictive models are displayed in the box. XGBoost: Extreme gradient boosting; ET: Extremely random trees; GBDT: Gradient boosting decision tree; LR: Logistic regression; RF, random forest; CI: Confidence interval.

Figure 6 Validation of the training set. The picture shows average area under the receiver operating characteristic curve and precision recall curve of the five models in the test set. A: Receiver operating characteristic curve (testing set); B: Precision-recall curve (testing set). AUC: Area under the curve; CI: Confidence interval; XGBoost: Extreme gradient boosting; ET: Extremely random trees; GBDT: Gradient boosting decision tree; LR: Logistic regression; RF: Random forest; CI: Confidence interval.