Background: Lung cancer surgery often involves complex decision-making, where accurate and interpretable predictive models are crucial for assessing postoperative risks and optimizing outcomes. This study presents XplainLungSHAP, a novel framework combining SHAP (SHapley Additive exPlanations) and attention mechanisms to enhance both predictive accuracy and transparency. The aim is to support clinicians in preoperative evaluations by identifying and prioritizing key clinical features. Methods: The framework was developed using data from 470 patients undergoing lung cancer surgery. Key clinical features were identified through SHAP, ensuring alignment with medical expertise. These features were dynamically weighted using an attention mechanism in a neural network, enhancing their impact on survival predictions. The model’s performance was evaluated through accuracy, confusion matrices, and ROC analysis, demonstrating its reliability and interpretability. Results: The XplainLungSHAP model achieved an accuracy of 91.49%, outperforming traditional machine learning models. SHAP analysis identified critical predictors, including pulmonary function, comorbidities, and age, while the attention mechanism prioritized these features dynamically. The combined approach ensured high accuracy and offered actionable insights into survival predictions. Conclusions: XplainLungSHAP addresses the limitations of black-box models by integrating explainability with state-of-the-art predictive techniques. This framework provides a transparent and clinically relevant tool for guiding surgical decisions, supporting personalized care, and advancing AI applications in thoracic oncology.

The purpose of this paper is to present a detailed investigation of the advantages of employing GraphLIME (Local Interpretable Model Explanations for Graph Neural Networks) for the trustworthy prediction of diabetes mellitus. Our pursuit involves identifying the strengths of GraphLIME combined with the attention-mechanism over the standard coupling of deep learning neural networks with the original LIME method. The system build this way, provided us a proficient method for extracting the most relevant features and applying the attention mechanism exclusively to those features. We have closely monitored the performance metrics of the two approaches and conducted a comparative analysis. Leveraging attention mechanisms, we have achieved an accuracy of 92.6% for the addressed problem. The model’s performance is meticulously demonstrated throughout the study, and the results are furthermore evaluated using the Receiver Operating Characteristic (ROC) curve. By implementing this technique on a dataset of 768 patients diagnosed with or without diabetes mellitus, we have successfully boosted the model’s performance by over 18%.