Predicting gastrointestinal bleeding and audio biomarkers based on machine learning analysis of bowel sounds

Figure 1 Research flowchart. BS: Bowel sound; VMD: Variational mode decomposition; SVM: Support vector machine; RF: Random forest; KNN: K-nearest neighbors; LR: Logistic regression.

Figure 2  Schematic diagram of bowel sound collection.

Figure 3 Bowel sound time-domain waveforms and spectrograms. A: Healthy group; B: Upper gastrointestinal bleeding group. UGIB: Upper gastrointestinal bleeding.

Figure 4 Power spectral density of variational-mode-decomposition-derived intrinsic mode functions for bowel sounds. A: Mode 1. Center frequency = 300.8 Hz; B: Mode 2. Center frequency = 275.6 Hz; C: Mode 3. Center frequency = 151.2 Hz; D: Mode 4. Center frequency = 100.1 Hz; E: Mode 5. Center frequency = 50.1 Hz.

Figure 5 Receiver operating characteristic curves and area under the curve values of the four models. ROC: Receiver operating characteristic; AUC: Area under the curve; SVM: Support vector machine; RF: Random forest; KNN: K-nearest neighbors; LR: Logistic regression.

Figure 6 Importance ranking and SHapley Additive exPlanations summary chart of top 20 features in support vector machine model. A: Top 20 features importance ranking; B: SHapley Additive exPlanations summary chart of top 20 features.

Figure 7 Receiver operating characteristic curves and area under the curve values of each model. A-E: Show the top 20, 25, 30, 35, and 40 feature subsets; F: Presents the area under the curve for each model under its respective optimal feature subset size. AUC: Area under the curve; SVM: Support vector machine; RF: Random forest; KNN: K-nearest neighbors; LR: Logistic regression.

Figure 8  Correlation heatmap of the top 30 features and clinical biochemical indicators.