Extracellular vesicles as biomarkers for metabolic dysfunction-associated steatotic liver disease staging using explainable artificial intelligence

Figure 6 Each dot represents an individual prediction, with the X-axis denoting the SHapley Additive exPlanations value (impact on model output), while the color gradient indicates the feature value (blue for low values, red for high values). Features such as > 150_pow_5, Height_x_Weight, and Sum_x_BMI also significantly contribute to the predictive capability of the model, showcasing complex interactions between anthropometric, clinical, and extracellular vesicle features. In Supplementary Table 2 we demonstrate the information regarding all the machine learning models of C2 and in Table 3 performances on the CB-C1a and CB-C2h-21, using ten times iterative 5CV and 3CV for C1 and C2, respectively. SHAP: SHapley Additive exPlanations; BMI: Body mass index.