Explainable machine learning model integrating clinical and radiomic features for predicting acute suppurative cholecystitis

Figure 4 The SHapley Additive exPlanations beewram plots of the radiomics model. The X-axis shows SHapley Additive exPlanations values representing the magnitude and direction of feature contributions. Each dot is a sample, colored by the feature value (red high, blue low), helping interpret feature importance and effects. All features used in the radiomics model are derived from non-contrast computed tomography images. The features include: Original first-order and shape features, texture features from gray level size zone matrix, gray level dependence matrix, gray level run length matrix, neighboring gray tone difference matrix, and local binary pattern families, and filtered/transformed features using logarithm, gradient, wavelet, or square root operations. The Y-axis is limited to the range (-2, 2) to focus on the main impact range and improve plot readability by reducing the influence of extreme values. SHAP: SHapley Additive exPlanations.