Machine learning identifies complement factor I as a shared mediator of periodontitis and ossification of posterior longitudinal ligament

Figure 3 Feature variable selection via integrated machine learning algorithms and SHapley Additive exPlanations explainability analysis for disease discrimination. A: Summary of performance metrics (e.g., accuracy, sensitivity, specificity) for the 10 machine learning models (the training set is on the top and the validation set is on the bottom); B: Line charts depicting model performance across different evaluation dimensions; C and D: Forest plot displaying area under the curve values and their 95% confidence intervals for all models; E: Receiver operating characteristic curves illustrating the predictive performance of the models; F: Decision curve analysis evaluating the clinical utility of the optimal model; G: Confusion matrix visualization for the selected model; H: SHapley Additive exPlanations analysis identifying complement factor I as the top contributing feature in the optimal model.