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

Abstract

BACKGROUND

Early diagnosis of upper gastrointestinal bleeding (UGIB) relies on invasive endoscopy and laboratory tests, which carry procedural risks and diagnostic delays. The pathophysiological relationship between bowel sounds (BSs) as a noninvasive monitoring metric and UGIB remains to be elucidated.

AIM

To investigate the feasibility of BS acoustic signatures as UGIB screening biomarkers, analyze their pathological correlations with hematological indices, and construct a machine learning-assisted diagnostic model.

METHODS

A prospective study enrolled 40 UGIB patients (endoscopy-confirmed within 24 hours) and 40 age-/sex-matched healthy controls. BS signals were recorded at the right lower umbilical quadrant using a G-200 device (60 seconds/subject, 4 kHz sampling). After denoising via variational mode decomposition, 78-dimensional features were extracted across four domains: Time-domain, frequency-domain, time-frequency domain, and nonlinear dynamics. Weighted feature importance was calculated using an integrated strategy and gradient-optimized feature subsets were used to train four classifiers: Support vector machine, random forest, logistic regression, and K-nearest neighbor. SHapley Additive exPlanations analysis was conducted on the features of the optimal model. Model performance was evaluated by fivefold cross-validation. Spearman’s correlation analysis was performed to assess key BS features against red blood cell count, hemoglobin, hematocrit, C-reactive protein (CRP), and high-sensitivity CRP.

RESULTS

The support vector machine classifier with 25-feature subsets achieved optimal performance (area under the curve > 0.89), significantly outperforming other models. Acoustic feature importance analysis identified band_Energy and Mel-frequency cepstral coefficient variance as core biomarkers (cumulative contribution > 60%). Key pathological correlations included: (1) Significant negative correlations between spectral centroid and red blood cell count/hemoglobin/hematocrit (P < 0.01); (2) Positive correlation between wavelet entropy and these hematological parameters (P < 0.05), suggesting multiscale microcirculatory flow fluctuations; and (3) Positive wavelet energy correlations with CRP/high-sensitivity-CRP (P < 0.05).

CONCLUSION

Multidimensional BS features enable noninvasive UGIB screening. Their strong correlation with anemia/inflammation indicators reveals an acoustic-hemato-physiological coupling mechanism, providing a novel paradigm for early UGIB monitoring.

Keywords: Bowel sound analysis; Upper gastrointestinal bleeding; Support vector machine; Machine learning; Non-invasive diagnosis

Core Tip: This study develops a machine learning-based screening tool to identify upper gastrointestinal bleeding (UGIB) patients using multidimensional bowel sound (BS) features. It also explores pathological associations between UGIB-related BSs characteristics and blood biochemical indicators, leveraging feature importance evaluation and SHapley Additive exPlanations analysis to identify interpretable predictors. This study pioneers a noninvasive BS analysis tool for UGIB screening, leveraging multidimensional acoustic feature engineering and machine learning optimization.