Interpretable Prognostic Modeling for Long-Term Survival of Type A Aortic Dissection Patients Using Support Vector Machine Algorithm

Objective This study aims to develop a reliable and interpretable predictive model for the risk of long-term survival in type A aortic dissection (TAAD) patients, utilizing machine learning (ML) algorithms. Methods We retrospectively reviewed the clinical data diagnosed with Type A Aortic Dissection (TAAD) who underwent open surgical repair at our institution between September 2017 and December 2020. Cases with less than 20% missing data were imputed using random forest algorithms. To identify significant prognostic factors, we performed LASSO (Least Absolute Shrinkage and Selection Operator) Cox regression analysis, including preoperative blood markers, previous medical history and perioperative condition. Based on the advantages of the model and the characteristics of the dataset, we subsequently developed a machine learning-based prognostic model using Support Vector Machine (SVM) and evaluated its performance across key metrics. To further explain the decision-making process of the SVM model, we employed SHapley Additive exPlanation (SHAP) values for model interpretation. Results A total of 175 patients with TAAD were included in the study. Through LASSO Cox regression, univariate analysis, and clinical relevance assessment, eight feature variables were selected for modeling. Performance evaluation revealed that the SVM model showed excellent performance in both the training and test sets, with no significant overfitting, indicating strong clinical applicability. In the training set, the model achieved an AUC of 0.9247 (95% CI: 0.9200–0.9279), and in the testing set, 0.8800 (95% CI: 0.8492–0.9396). The accuracy was 0.8663 and 0.8857, precision was 0.8627 and 1.0000, recall was 0.8713 and 0.7333, F1 score was 0.8670 and 0.8462, Brier score was 0.1068 and 0.1070, average precision (AP) was 0.9266 and 0.9086, and C-index was 0.8901 and 0.8700, respectively. SHAP analysis identified that longer ICU hospital stay, abdominal pain, plasma transfusion volume, creatinine, white blood cell count, operation time, and systemic immune-inflammation index (SII) had significant positive impact on the model's predictions. Conclusion This study developed an interpretable predictive model based on the SVM algorithm to assess long-term survival in TAAD patients. The model demonstrated accuracy, precision, and robustness in identifying high-risk patients, providing clinicians with reliable evidence for prognosis management.